147 Commits

Author SHA1 Message Date
github-actions[bot] fc6f0a1a7b bump: version 0.1.0 → 0.1.1 [skip ci] 2025-11-08 00:22:06 +00:00
Dark-Alex-17 21873da278 docs: Fixed a typo in the CI badge path 2025-11-07 17:17:57 -07:00
Dark-Alex-17 d1cd6be2c9 docs: Fixed some confusing wording in the global configuration example file 2025-11-07 16:57:49 -07:00
github-actions[bot] 0c0ae41bca bump: version 0.0.1 → 0.1.0 [skip ci] 2025-11-07 23:47:37 +00:00
Dark-Alex-17 c9ed7a904a ci: Final release checks before open sourcing the repo 2025-11-07 16:43:50 -07:00
Dark-Alex-17 d200a8f554 Merge remote-tracking branch 'origin/main' 2025-11-07 16:24:47 -07:00
Dark-Alex-17 3d04c8fcf1 docs: Fixed a typo in the Vault documentation 2025-11-07 16:24:42 -07:00
github-actions[bot] f53f165d91 bump: version 0.0.1 → 0.1.0 [skip ci] 2025-11-07 23:19:04 +00:00
Dark-Alex-17 e5645e4064 ci: Prepare for release 2025-11-07 16:18:16 -07:00
Dark-Alex-17 95e15ca8c4 bump: version 0.0.1 → 0.1.0 2025-11-07 16:11:14 -07:00
Dark-Alex-17 dbf7329e87 refactor: Updated to the most recent Rust version with 2024 syntax 2025-11-07 15:50:55 -07:00
github-actions[bot] ed6c3ae431 bump: version 0.1.0 → 0.2.0 [skip ci] 2025-11-07 22:04:11 +00:00
Dark-Alex-17 214d2ecc67 ci: Bumped the patch version 2025-11-07 15:03:31 -07:00
Dark-Alex-17 29c95671de build: bumped the crate version 2025-11-07 14:59:41 -07:00
Dark-Alex-17 238f93a096 docs: Added badges for Loki 2025-11-07 14:24:25 -07:00
Dark-Alex-17 c76877e7b3 ci: Fixed typo in commit message for homebrew tap 2025-11-07 14:24:13 -07:00
Dark-Alex-17 12e5a9c5aa build: Renamed the crate to loki-ai since loki is taken 2025-11-07 14:16:02 -07:00
Dark-Alex-17 7f4be2ca3f ci: Created the homebrew installation steps 2025-11-07 13:53:28 -07:00
Dark-Alex-17 29ffe12d8c ci: Created the release pipeline 2025-11-07 13:51:53 -07:00
Dark-Alex-17 d34bed4f15 docs: Updated the README to credit the AIChat team and to offer quick links to get around the docs 2025-11-07 13:49:26 -07:00
Dark-Alex-17 aec7ea7e80 docs: Wrote migration documentation for users coming from AIChat 2025-11-07 13:49:02 -07:00
Dark-Alex-17 5938e1af29 docs: Added a simple gif to show what the models table looks like for tab completions 2025-11-07 13:48:48 -07:00
Dark-Alex-17 60902297c5 docs: Replaced the copy gif with one that better shows that the content is copied to your clipboard 2025-11-07 13:48:30 -07:00
Dark-Alex-17 12a95aa6fa docs: Updated the continue gif to use a prompt that makes more sense 2025-11-07 13:48:09 -07:00
Dark-Alex-17 78fc459a97 docs: Updated the set gif to show the up-to-date settings names 2025-11-07 13:47:57 -07:00
Dark-Alex-17 281565804c docs: Updated the regenerate gif to use the up-to-date settings names 2025-11-07 13:47:41 -07:00
Dark-Alex-17 33a32fd9c8 docs: Created docs for the REPL 2025-11-07 13:47:20 -07:00
Dark-Alex-17 b64aad55e9 docs: Documented all available environment variables 2025-11-07 13:47:10 -07:00
Dark-Alex-17 2392958114 docs: Added back in the conversation starters gif for the agent docs 2025-11-07 13:46:53 -07:00
Dark-Alex-17 ec04e8e24a docs: Made an example agent gif to show how they work (and variables) 2025-11-07 13:46:35 -07:00
Dark-Alex-17 4e14ee7f50 docs: Created documentation for agents 2025-11-07 13:46:16 -07:00
Dark-Alex-17 7ba4ab0608 docs: Added a screenshot of the tools overrides settings 2025-11-07 13:46:00 -07:00
Dark-Alex-17 fd816112fb docs: Created docs about both built-in and custom tools for function calling capabilities 2025-11-07 13:45:45 -07:00
Dark-Alex-17 d0ee85be40 docs: Documented how to create custom tools in Python, and how custom tools are created and used 2025-11-07 13:45:23 -07:00
Dark-Alex-17 9448704af3 docs: Documented how to create custom Bash-based tools 2025-11-07 13:45:01 -07:00
Dark-Alex-17 9dad9d6ca8 docs: Added back in forgotten gif of a session 2025-11-07 13:44:44 -07:00
Dark-Alex-17 3f41abed7c docs: documentation on how sessions work in Loki 2025-11-07 13:44:32 -07:00
Dark-Alex-17 debcbab445 docs: Created a demo gif of how to use roles in general 2025-11-07 13:44:16 -07:00
Dark-Alex-17 7fcabf1de7 docs: Created a demo gif of a temporary prompt role 2025-11-07 13:44:00 -07:00
Dark-Alex-17 e116a1841d docs: Documented roles 2025-11-07 13:43:37 -07:00
Dark-Alex-17 cd3103ca14 docs: created a gif that demonstrates macro functionality 2025-11-07 13:43:26 -07:00
Dark-Alex-17 50d07a4b13 docs: Removed a forgotten TODO comment 2025-11-07 13:43:09 -07:00
Dark-Alex-17 ed1352936e docs: created a screenshot of the global settings overrides for MCP servers 2025-11-07 13:42:36 -07:00
Dark-Alex-17 f4b4156a0c docs: created screenshots for both ephemeral and persistent RAG 2025-11-07 13:42:15 -07:00
Dark-Alex-17 5cf2cce0e3 docs: documented RAG 2025-11-07 13:41:50 -07:00
Dark-Alex-17 249453d829 docs: Created docs that explain how to use MCP servers with Loki 2025-11-07 13:41:19 -07:00
Dark-Alex-17 c14939cecc docs: created docs for Loki's macro system 2025-11-07 13:40:48 -07:00
Dark-Alex-17 72f516abb1 docs: documented how to use custom themes 2025-11-07 13:40:25 -07:00
Dark-Alex-17 66478ed264 docs: documented how to create custom REPL prompts 2025-11-07 13:40:10 -07:00
Dark-Alex-17 6b10dff41d docs: documented the now built-in bash helper script and the tools it comes with 2025-11-07 13:39:53 -07:00
Dark-Alex-17 f8cc736482 docs: created documentation for how to patch requests via configuration settings 2025-11-07 13:39:04 -07:00
Dark-Alex-17 a0794fecfc docs: created documentation for client configurations 2025-11-07 13:38:34 -07:00
Dark-Alex-17 c68059e5b3 docs: updated the vault demo screenshots and gifs 2025-11-07 13:38:22 -07:00
Dark-Alex-17 832ca6b0de docs: Added screenshots for select custom themes 2025-11-07 13:37:56 -07:00
Dark-Alex-17 89ee43830e docs: Added documentation for secret injection support into environment variables for agents 2025-11-07 12:28:11 -07:00
Dark-Alex-17 f7cf13901e docs: Added an explain-shell screenshot 2025-11-07 12:26:43 -07:00
Dark-Alex-17 ad41fa93fb docs: Fixed a typo in the shell integrations documentation 2025-11-07 12:25:26 -07:00
Dark-Alex-17 617b7dcd49 docs: Created license 2025-11-07 11:48:19 -07:00
Dark-Alex-17 417ea032c4 ci: Created Loki installation scripts 2025-11-07 11:48:08 -07:00
Dark-Alex-17 b77bb6e200 refactor: Changed the name of the summary_prompt setting to summary_context_prompt 2025-11-07 11:13:58 -07:00
Dark-Alex-17 1fa3b4a600 refactor: Renamed summarize_prompt setting to summarization_prompt 2025-11-07 11:09:48 -07:00
Dark-Alex-17 99bd502f62 refactor: Renamed the compress_threshold setting to compression_threshold 2025-11-07 11:06:20 -07:00
Dark-Alex-17 25a271dc95 style: Applied formatting 2025-11-06 18:19:25 -07:00
Dark-Alex-17 5002ac7716 refactor: Migrated around the location of some of the more large documents for documentation 2025-11-06 18:02:17 -07:00
Dark-Alex-17 d92a559460 docs: Updated the global configuration example to have a separate section for the REPL prompts 2025-11-06 16:24:20 -07:00
Dark-Alex-17 3d571e1a31 docs: Fixed a typo in the description of the stream setting 2025-11-06 16:10:44 -07:00
Dark-Alex-17 d338daa4b6 docs: Referenced the vault documentation in the example config 2025-11-06 16:09:21 -07:00
Dark-Alex-17 6f802c2a58 docs: Created a separate, dedicated section of the example configuration file for the vault 2025-11-06 16:08:20 -07:00
Dark-Alex-17 a3f0168817 docs: Improved the documentation for sessions and the examples in the global configuration example 2025-11-06 15:55:38 -07:00
Dark-Alex-17 677702655f docs: Improved the documentation of preludes and their purpose in the example global configuration file 2025-11-06 15:48:44 -07:00
Dark-Alex-17 b0bbd0c083 docs: Improved the documentation of the behavior-related settings of the global configuration file example 2025-11-06 15:47:30 -07:00
Dark-Alex-17 5cbf23a1f4 docs: Improved wording in the example agent configuration 2025-11-06 13:55:44 -07:00
Dark-Alex-17 39eb9b34ec docs: Updated the example agent configuration to show the new global_tools and mcp_servers environment variables 2025-11-06 13:31:25 -07:00
Dark-Alex-17 5da8616518 feat: Added the agents directory to sysinfo output 2025-11-06 13:22:13 -07:00
Dark-Alex-17 b267fe05cd docs: Fixed a typo in the Vertex AI client configuration example in the example global configuration file 2025-11-06 13:07:34 -07:00
Dark-Alex-17 29f7ebe559 Added environment variables for agents for the global_tools and mcp_servers settings 2025-11-06 12:16:36 -07:00
Dark-Alex-17 bbffaca511 docs: Updated the example global configuration file with some better examples for RAG 2025-11-06 10:49:51 -07:00
Dark-Alex-17 80532836c3 docs: Created an example macro configuration file 2025-11-05 16:55:04 -07:00
Dark-Alex-17 9474f4f322 feat: Added built-in macros 2025-11-05 16:28:56 -07:00
Dark-Alex-17 93a09d3a9f bug: Removed deprecated experimentation for MCP sampling 2025-11-05 16:12:04 -07:00
Dark-Alex-17 e3935ce699 style: Added an import for Anyhow's Result in the macros module 2025-11-05 15:52:44 -07:00
Dark-Alex-17 58c15e7833 refactor: Factored out the macros structs from the large config module 2025-11-05 15:50:39 -07:00
Dark-Alex-17 fd2b7f3aa0 bug: Fixed a bug with the spacing of info output now that function_calling_support is a longer name 2025-11-05 15:41:49 -07:00
Dark-Alex-17 5ccbc629d1 feat: Updated the example role configuration file to also have the prompt field 2025-11-05 15:25:01 -07:00
Dark-Alex-17 e98ff5e8e5 feat: Updated the code role 2025-11-05 15:24:45 -07:00
Dark-Alex-17 a6fffa7b57 refactor: Refactored mcp_servers and function_calling to mcp_server_support and function_calling_support to make the purpose of the fields more clear 2025-11-04 13:17:58 -07:00
Dark-Alex-17 3ac153dd06 refactor: Refactored the use_mcp_servers field to enabled_mcp_servers to make the purpose of the field more clear 2025-11-04 12:51:41 -07:00
Dark-Alex-17 8db3108c94 Merge branch 'main' of github.com:Dark-Alex-17/loki 2025-11-04 12:37:32 -07:00
Dark-Alex-17 e25ff4ad19 refactor: Refactored use_tools field to enabled_tools field to make the use of the field more clear 2025-11-04 12:37:14 -07:00
Dark-Alex-17 21e76c6461 Refactored the use_tools field to enabled_tools to make field uses and functions more clear 2025-11-04 12:36:31 -07:00
Dark-Alex-17 103aa1a432 docs: Updated the config.example.yaml to have an example of how to use the visible_tools array 2025-11-04 12:10:17 -07:00
Dark-Alex-17 d2f4fefcf3 refactor: Removed the use of the tools.txt file and added tool visibility declarations to the global configuration file 2025-11-04 12:07:58 -07:00
Dark-Alex-17 629527988d refactor: Agents that depend on global tools now have all binaries compiled and stored in the agent's bin directory so multiple agents can run at once 2025-11-04 11:29:59 -07:00
Dark-Alex-17 7f520f1346 feat: Secret injection as environment variables into agent tools 2025-11-03 15:10:34 -07:00
Dark-Alex-17 e28619b55a feat: Removed the server functionality 2025-11-03 14:25:55 -07:00
Dark-Alex-17 f474e6130e feat: Require Vault set up for first-time setup so all passed in secrets can be encrypted right off the bat 2025-10-27 12:00:27 -06:00
Dark-Alex-17 4b5bcb45ac style: Re-applied formatting to make Clippy happy 2025-10-24 15:05:42 -06:00
Dark-Alex-17 50565a0f17 refactor: Removed the git MCP server and used the newer, better mcp-server-docker for local docker integration 2025-10-24 14:38:13 -06:00
Dark-Alex-17 cf37db4fa2 docs: Added in forgotten MCP server configuration values to the example config 2025-10-24 14:16:13 -06:00
Dark-Alex-17 ad9b4097ef Created an Elvish integration script 2025-10-24 11:28:31 -06:00
Dark-Alex-17 c22c01c6c3 refactor: Renamed the argument for the --completions flag to SHELL 2025-10-24 10:58:28 -06:00
Dark-Alex-17 31f7f50c4a feat: Added static completions via a --completions flag 2025-10-24 10:56:34 -06:00
Dark-Alex-17 a7f6ed4b16 refactor: Updated the instructions for the jira-helper agent 2025-10-23 10:07:50 -06:00
Dark-Alex-17 73ada5a221 bug: Fixed a bug when passing tools to Claude for tools that don't have any inputs 2025-10-21 10:04:38 -06:00
Dark-Alex-17 2f96256893 bug: Fixed a bug that was duplicating entries of all the functions for agents between MCP and tools 2025-10-20 15:30:29 -06:00
Dark-Alex-17 23d9e0775f ci: Updated to only include basic ARM64 and x86_64 architectures 2025-10-17 13:30:42 -06:00
Dark-Alex-17 72ade39144 bug: corrected a typo for sourcing the prompt utility bash script in the built-in tools 2025-10-16 15:48:53 -06:00
Dark-Alex-17 ec64c68777 fix: Corrected a typo for sourcing the bash utility script in some agent definitions 2025-10-16 15:47:07 -06:00
Dark-Alex-17 80932e069f chore: update the models.yaml 2025-10-16 15:20:33 -06:00
Dark-Alex-17 2f9b154b07 refactor: Modified the default PS1 look 2025-10-16 15:08:48 -06:00
Dark-Alex-17 20bf911732 style: Cleaned up some linting issues for Windows 2025-10-16 13:30:30 -06:00
Dark-Alex-17 65a3dbb228 style: Applied formatting 2025-10-16 13:01:37 -06:00
Dark-Alex-17 5844cc93ca refactor: Fixed a linting issue for Windows builds 2025-10-16 12:44:50 -06:00
Dark-Alex-17 4d23ce58c4 docs: Updated outdated API links in the config example 2025-10-16 12:38:07 -06:00
Dark-Alex-17 2bb592d5f6 feat: Support for secret injection into the global config file (API keys, for example) 2025-10-16 12:30:18 -06:00
Dark-Alex-17 3146b20c15 feat: Improved MCP handling toggle handling 2025-10-15 18:36:54 -06:00
Dark-Alex-17 455cf67750 feat: Secret injection into the MCP configuration 2025-10-15 16:06:59 -06:00
Dark-Alex-17 a6d6a877b0 feat: added REPL support for interacting with the Loki vault 2025-10-15 15:15:04 -06:00
Dark-Alex-17 a7bd54471c feat: Integrated gman with Loki to create a vault and added flags to configure the Loki vault 2025-10-14 18:00:11 -06:00
Dark-Alex-17 fe5f803163 Applied formatting 2025-10-10 15:32:51 -06:00
Dark-Alex-17 66a9b5362a bug: Automatically mark all extracted tools as executable 2025-10-10 15:30:58 -06:00
Dark-Alex-17 f3569cf68b docs: Created an example role configuration 2025-10-10 15:15:11 -06:00
Dark-Alex-17 2573f14726 feat: Added a default session to the jira helper to make interaction more natural 2025-10-10 15:03:26 -06:00
Dark-Alex-17 f1fb2d6abf style: applied formatting 2025-10-10 15:01:55 -06:00
Dark-Alex-17 4934e0ff0a refactor: Changed the name of agent_prelude to agent_session to make its purpose more clear 2025-10-10 15:01:44 -06:00
Dark-Alex-17 f772a80501 style: Applied consistent formatting to agent changes 2025-10-10 14:48:10 -06:00
Dark-Alex-17 8950843be2 feat: Created the repo-analyzer role 2025-10-10 14:43:18 -06:00
Dark-Alex-17 9b89e68908 feat: Created the coder and sql agents 2025-10-10 13:38:47 -06:00
Dark-Alex-17 ba134ca53f feat: Cleaned the built-in functions to not have leftover dependencies 2025-10-10 13:38:27 -06:00
Dark-Alex-17 21dbd9c057 feat: Created additional built-in roles for slack, repo analysis, and github 2025-10-10 13:38:03 -06:00
Dark-Alex-17 40a68f8e05 feat: Install built-in agents 2025-10-10 13:37:05 -06:00
Dark-Alex-17 37d861a631 refactor: Removed leftover javascript function support; will not implement 2025-10-10 10:22:05 -06:00
Dark-Alex-17 31f3e885ce docs: Fixed typo in Python execution docs 2025-10-10 10:05:09 -06:00
Dark-Alex-17 7ffaab2012 feat: Embedded baseline MCP config and global tools 2025-07-13 09:58:00 -06:00
Dark-Alex-17 35b7946b0d docs: Created the code of conduct 2025-07-06 10:59:27 -06:00
Dark-Alex-17 3a05a8e712 docs: Added the security policy 2025-07-06 10:58:02 -06:00
Dark-Alex-17 294a1149ef ci: Initialized commitizen configuration 2025-07-06 10:57:37 -06:00
Dark-Alex-17 8d80370014 docs: Added loki contribution guidelines 2025-07-06 10:55:52 -06:00
Dark-Alex-17 1cbdef36cf Created an .actrc file to make local CI/CD testing easier 2025-07-06 10:54:16 -06:00
Dark-Alex-17 4c8accbfc1 Removed the hestia CLI since it is no longer needed 2025-07-06 10:53:44 -06:00
Dark-Alex-17 c4c2d9cb93 Updated gitignore 2025-07-06 10:53:00 -06:00
Dark-Alex-17 7aed112326 Create issue templates and CI/CD workflows 2025-07-06 10:51:04 -06:00
Dark-Alex-17 216a3d53cd Baseline project 2025-07-06 10:45:42 -06:00
Dark-Alex-17 e0823b343b Created initial assets 2025-07-06 10:43:34 -06:00
Dark-Alex-17 cb0bc65ee4 Created initial assets 2025-07-06 10:42:46 -06:00
Dark-Alex-17 5b9ab6636f Initial commit 2025-07-06 10:41:42 -06:00
Alex Clarke 9fd77feebb Initial commit 2025-07-05 10:35:42 -06:00
271 changed files with 13695 additions and 42967 deletions
+13 -13
View File
@@ -21,25 +21,25 @@ body:
value: |
I tried this:
1. `coyote`
1. `loki`
I expected this to happen:
Instead, this happened:
- type: textarea
id: coyote-log
id: loki-log
attributes:
label: Coyote log
description: Include the Coyote log file to help diagnose the issue. (`coyote --info` to see the log_path)
label: Loki log
description: Include the Loki log file to help diagnose the issue. (`loki --info` to see the log_path)
value: |
| OS | Log file location |
| ------- | ----------------------------------------------------- |
| Linux | `~/.cache/coyote/coyote.log` |
| Mac | `~/Library/Logs/coyote/coyote.log` |
| Windows | `C:\Users\<User>\AppData\Local\coyote\coyote.log` |
| Linux | `~/.cache/loki/loki.log` |
| Mac | `~/Library/Logs/loki/loki.log` |
| Windows | `C:\Users\<User>\AppData\Local\loki\loki.log` |
```
please provide a copy of your coyote log file here if possible; you may need to redact some of the lines
please provide a copy of your loki log file here if possible; you may need to redact some of the lines
```
- type: input
@@ -57,13 +57,13 @@ body:
validations:
required: true
- type: input
id: coyote-version
id: loki-version
attributes:
label: Coyote Version
label: Loki Version
description: >
Coyote version (`coyote --version` if using a release, `git describe` if building
Loki version (`loki --version` if using a release, `git describe` if building
from main).
**Make sure that you are using the [latest coyote release](https://github.com/Dark-Alex-17/coyote/releases) or a newer main build**
placeholder: "coyote 0.1.0"
**Make sure that you are using the [latest loki release](https://github.com/Dark-Alex-17/loki/releases) or a newer main build**
placeholder: "loki 0.1.0"
validations:
required: true
@@ -1,11 +0,0 @@
### AI assistance (if any):
- List tools here and files touched by them
### Authorship & Understanding
- [ ] I wrote or heavily modified this code myself
- [ ] I understand how it works end-to-end
- [ ] I can maintain this code in the future
- [ ] No undisclosed AI-generated code was used
- [ ] If AI assistance was used, it is documented below
+14 -14
View File
@@ -98,9 +98,9 @@ jobs:
# Ignore Act's local artifact dir noise
echo artifacts/ >> .git/info/exclude || true
# Edit the version line right after name="coyote"
# Edit the version line right after name="loki"
sed -E -i '
/^[[:space:]]*name[[:space:]]*=[[:space:]]*"coyote"[[:space:]]*$/ {
/^[[:space:]]*name[[:space:]]*=[[:space:]]*"loki"[[:space:]]*$/ {
n
s|^[[:space:]]*version[[:space:]]*=[[:space:]]*"[^"]*"|version = "'"$VERSION"'"|
}
@@ -278,7 +278,7 @@ jobs:
- name: Verify file
shell: bash
run: |
file target/${{ matrix.target }}/release/coyote
file target/${{ matrix.target }}/release/loki
- name: Test
if: matrix.target != 'aarch64-apple-darwin' && matrix.target != 'aarch64-pc-windows-msvc'
@@ -382,11 +382,11 @@ jobs:
shell: bash
run: |
# Set environment variables
macos_sha="$(cat ./artifacts/coyote-x86_64-apple-darwin.sha256 | awk '{print $1}')"
macos_sha="$(cat ./artifacts/loki-x86_64-apple-darwin.sha256 | awk '{print $1}')"
echo "MACOS_SHA=$macos_sha" >> $GITHUB_ENV
macos_sha_arm="$(cat ./artifacts/coyote-aarch64-apple-darwin.sha256 | awk '{print $1}')"
macos_sha_arm="$(cat ./artifacts/loki-aarch64-apple-darwin.sha256 | awk '{print $1}')"
echo "MACOS_SHA_ARM=$macos_sha_arm" >> $GITHUB_ENV
linux_sha="$(cat ./artifacts/coyote-x86_64-unknown-linux-musl.sha256 | awk '{print $1}')"
linux_sha="$(cat ./artifacts/loki-x86_64-unknown-linux-musl.sha256 | awk '{print $1}')"
echo "LINUX_SHA=$linux_sha" >> $GITHUB_ENV
release_version="$(cat ./artifacts/release-version)"
echo "RELEASE_VERSION=$release_version" >> $GITHUB_ENV
@@ -402,23 +402,23 @@ jobs:
if: env.ACT != 'true'
run: |
# run packaging script
python "./deployment/homebrew/packager.py" ${{ env.RELEASE_VERSION }} "./deployment/homebrew/coyote.rb.template" "./coyote.rb" ${{ env.MACOS_SHA }} ${{ env.MACOS_SHA_ARM }} ${{ env.LINUX_SHA }}
python "./deployment/homebrew/packager.py" ${{ env.RELEASE_VERSION }} "./deployment/homebrew/loki.rb.template" "./loki.rb" ${{ env.MACOS_SHA }} ${{ env.MACOS_SHA_ARM }} ${{ env.LINUX_SHA }}
- name: Push changes to Homebrew tap
if: env.ACT != 'true'
env:
TOKEN: ${{ secrets.COYOTE_GITHUB_TOKEN }}
TOKEN: ${{ secrets.LOKI_GITHUB_TOKEN }}
run: |
# push to Git
git config --global user.name "Dark-Alex-17"
git config --global user.email "alex.j.tusa@gmail.com"
git clone https://Dark-Alex-17:${{ secrets.COYOTE_GITHUB_TOKEN }}@github.com/Dark-Alex-17/homebrew-coyote.git
rm homebrew-coyote/Formula/coyote.rb
cp coyote.rb homebrew-coyote/Formula
cd homebrew-coyote
git clone https://Dark-Alex-17:${{ secrets.LOKI_GITHUB_TOKEN }}@github.com/Dark-Alex-17/homebrew-loki.git
rm homebrew-loki/Formula/loki.rb
cp loki.rb homebrew-loki/Formula
cd homebrew-loki
git add .
git diff-index --quiet HEAD || git commit -am "Update formula for Coyote release ${{ env.RELEASE_VERSION }}"
git push https://$TOKEN@github.com/Dark-Alex-17/homebrew-coyote.git
git diff-index --quiet HEAD || git commit -am "Update formula for Loki release ${{ env.RELEASE_VERSION }}"
git push https://$TOKEN@github.com/Dark-Alex-17/homebrew-loki.git
publish-crate:
needs: publish-github-release
+1 -2
View File
@@ -2,6 +2,5 @@
/tmp
/.env
!cli/**
.idea/
/coyote.iml
/.idea/
/loki.iml
+10
View File
@@ -0,0 +1,10 @@
# Default ignored files
/shelf/
/workspace.xml
# Editor-based HTTP Client requests
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml
# Zeppelin ignored files
/ZeppelinRemoteNotebooks/
+2 -308
View File
@@ -1,309 +1,3 @@
## v0.6.0 (2026-06-05)
### Feat
- added skill hint prompt injection and configuration
- Fallthrough on missing secrets during mcp.json merging
- validate visible_skills field at config load time
- implemented reflexion (sorta) in sisyphus for significant code changes to delegate to the code-reviewer agent
- improved explore agent
- removed conditional fallback of LLM_*_RAW_JSON from built-ins
- updated enabled_skills handling to support both list and comma-separated strings
- added new REPL set commands for toggling skills and changing what skills are enabled
- upgraded to the latest version of mcp-remote
- fs_grep now works with both files and directories
- improved code reviewer agents with skills
- added round trip validation for vault providers to ensure permissions and authentication
- created new first-time run wizard for secrets provider
- vault_password_file or nothing at all is shorthand for just using the local gman provider for secret management
- refactored gman usage to be generic and work with various vault providers and use the SupportedProvider enum directly for configurations
- created initial parity gman generalization for vault provider
- Refactored the sisyhpus agent system to utilize the new skills system to improve performance and reliability
- llm graph nodes support skills
- updated sisyphus and coder tools
- removed potentially confusing tab completions for .skill
- .edit skill <name> support from within the REPL
- Added skills_dir to the info output of Coyote
- Created a few auto built-in skills
- Added support for auto_unload skills during chat
- cleaned up skill implementation
- support multiple skill flags to load multiple skills at CLI startup
- Modified --skill CLI to allow users to specify skills to start the REPL or CLI with.
- added CLI --skill flag for modifying skills easily
- REPL integration with skills
- dynamic loading/unloading of skill tools and MCP servers whenever load_skill/unload_skill are invoked
- created built-in functions for listing, loading, and unloading skills
- implemented the skills policy to track available skills per context
- added remote install and install support for skills
- created the skill registry
- decided to make skills persist to disk like agents and not in-memory like built-in roles
- scaffold skill module
### Fix
- disable skills for specific built-in roles
- redirect stderr into user's /dev/tty for guards
- azure doesn't support underscores in key vault
- accidental regression on enabled_skills being empty = all
- greedy secrets regex caused multiple secrets on one line to fail
- add agent context check to skill visibility validation
- enforced global visible_skills in llm node validation and improved skill loading error handling across the project
- restore agent skill policy on error during effective policy calculation
- apply the same validation for skill filenames on list_skills as happens everywhere else
- the vault's init_bare should try to load the provisioned secret_provider from the config file without also interpolating any of the rest of the configuration file. It should only fail if the user has not yet created a configuration file; i.e. done a first-time run.
- the vault roundtrip test used characters that are unsupported by some major secrets providers
- fixed tool filtering logic for skills and user functions in agents
- privilege leak when unloading skills and leaving tool scope untouched
- When bootstrapping an app config to interpolate secrets, clone the secrets provider configuration as well so config secrets stored in remote vaults can be used properly
- forgot to move back up the vault probe value error to be before the delete
- don't silently fail on skill role composition extraction in llm nodes
- set -euo pipefail for the temp script in execute_command.sh tool
- added forgotten skill name validation to has_skill to prevent side-channel attacks
- use unique values for the secrets round trip verification
- stop interpolating a line if any errors occur
- added path validation for skill names
- effective_policy unconditionally overwrote skill values for role-like structs
- updated execute_command to not mangle heredocs and also added explicit instructions to the coder and sisyphus agents to use fs_write and fs_patch over execute_command when writing files
- llm nodes accidentally skipped skill_registry::effective_role because I was passing an inline role instead
- updated temperature values for all agents and roles
- added back in require_max_tokens for new Claude models
- skill support also requires function calling to be enabled
- non_tty tests break on some TTY terminals
- skill loading on agents
- forgot to bootstrap skills on REPL startup
- remove now deprecated .skill edit command
### Refactor
- removed redundant skill name validation from has_skill function
- support both CSV and list formats for enabled_tools
- Support both CSV and list formats for enabled_mcp_servers
## v0.5.0 (2026-05-27)
### Feat
- rename Loki to Coyote
### Fix
- bash-based user interactions in agents accidentally regressed in graph implementation
- Claude function calling in agent contexts
- Claude code rate limit error per new Claude changes
## v0.4.0 (2026-05-23)
### Feat
- LLM node failures propgate up
- Added .install remote tab completions to the REPL
- feature complete install remote with category selection
- Support to interactively add secrets to Coyote that are missing from MCP configs when merging
- Added MCP config merging support for remote asset installations
- install remote now writes files to disk
- Created basic install_remote functions
- Created a more comprehensive and immediately useful default config for first runs
- Created an example graph-based agent called deep-research
- Improved coder agent that is now a graph-based agent
- Removed indicatif spinners. The UX just won't stop clobbering for parallel graph nodes
- Added agent variables support for graph agents and improved script executor to use the same environment variables as normal agent tool calling for further flexibility
- Improved UX with colored spinners for parallel graph agents and no clobbering outputs for sub-agents
- created new graph-based deep-research agent
- improved UX for parallel graph execution
- added branch progress tracker for better visualization of parallel graph super-steps
- Removed the jira-helper agent and replaced it with the atlassian role
- created the RenderMode enum to suppress stdout streaming during parallel graph super-steps
- Full support for map node types
- implemented the frontier-based scheduling for the graph executor with simplified state management (gotta love .clone)
- validation support for parallel graph execution; restricted map nodes to only run for nodes without next targets and not supporting chained map nodes
- created the staging area for state merges per super-step and created the built-in reducers (and their application) for the state merge phase of a super step
- scaffolding work for fan-out nodes for parallel branch execution support and stubbed out Map node types
- Coyote can now update itself via .update and --update commands
- added a .edit command for editing the MCP configuration file
- Created a new .install command to install bundled assets on-demand
- migrated llm node validation to graph loading time instead of graph runtime
- ripped out user input timeout scaffolding for approval and input node types; implementation can't be done cleanly
- added additional support for all RAG-configuration fields in RAG nodes
- initial support for RAG nodes in the graph execution system
- implemented structured logging for graph execution
- merged normal agent config and graph agent configs into one file (either/or)
- added structured-output extraction for llm and agent nodes
- created full llm node runtime implementation
- scaffolded together the initial llm node type and its executor
- wired together graph execution and agent graph dispatch
- implemented support for the graph executor
- created the approval node executor and the input node executor for user interaction
- Added initial support for native Coyote agent nodes in the graph-based agent system
- Added direct script invocation support for graph-based agents
- Added graph validation
- Implemented state management for agent graphs
- initial agent graph scaffolding
- add auto-continue support to all contexts
- dynamic tab completions now show the sessions for a given agent instead of only listing global sessions
- legacy SSE support for MCP server configurations
- support http/sse transport types for MCP server configurations so it fully supports claude desktop-style MCP configs
- 99% complete migration to new state structs to get away from God-Config struct; i.e. AppConfig, AppState, and RequestContext
- Automatic runtime customization using shebangs
- Created a demo TypeScript tool and a get_current_weather function in TypeScript
- Updated the Python demo tool to show all possible parameter types and variations
- Added TypeScript tool support using the refactored common ScriptedLanguage trait
### Fix
- Generified the functions usage of script detection for an executable bit on unix systems
- merge required claude code system prompt into instructions
- updated argc argument passing in run-tool and run-agent scripts
- Added additional graph validation for parallel reads and writes with dependencies between nodes states
- bug in next_single method and improved outcome handling for LLM node execution
- inline RAG bug when globbing files by extension without subdirectory globbing
- update the estimate_token_length function to use the standard word count method
- removed unnecessary regenerate logic for sessions and use the same logic for all contexts; prevents a panic on empty message list
- error when users try to start a session on a graph agent
- added on_other field for approval nodes so users can specify an alternative free-text target when none of the options match what they want
- accidentally added back in full agent tools on LLM nodes
- Improve the coder agent's usage of tools
- make the agent__collect escalation-aware so it doesn't freeze on sub-agent escalations
- check for an existing session before starting up MCP servers when switching to a role
- do not switch to agent if a session is active.
- Do not append todo instructions when function calling is disabled
- a bug in the dynamic completions because the crate name is coyote-ai but the binary is named coyote
- bug found by copilot that would create a lock on the PollSender for sse-based MCP servers
- Accidental shadow of temp_file function for Windows function calling
- upgraded to newer rmcp version to get native-tls support
- RagCache was not being used for agent and sub-agent instantiation
- TypeScript function args were being passed as objects rather than direct parameters
- Added in forgotten wrapper scripts for TypeScript tools
- don't shadow variables in binary path handling for Windows
- Tool call improvements for Windows systems
### Refactor
- migrated llm nodes to use Roles to simplify instructions handling and to function like inline roles
- migrated the next_node and apply_state_updates logic for LLM nodes into the LlmExecutor
- fully complete state re-architecting
- Fully ripped out the god Config struct
- Deprecated old Config struct initialization logic
- migrate functions and MCP servers to AppConfig
- Migrate the vault/bare_init logic
- created a single install_builtins free function to remove from Config::init
- partial migration to init in AppConfig
- Extracted common Python parser logic into a common.rs module
- python tools now use tree-sitter queries instead of AST
## v0.3.0 (2026-04-02)
### Feat
- Added `todo__clear` function to the todo system and updated REPL commands to have a .clear todo as well for significant changes in agent direction
- Added available tools to prompts for sisyphus and code-reviewer agent families
- Added available tools to coder prompt
- Improved token efficiency when delegating from sisyphus -> coder
- modified sisyphus agents to use the new ddg-search MCP server for web searches instead of built-in model searches
- Added support for specifying a custom response to multiple-choice prompts when nothing suits the user's needs
- Supported theming in the inquire prompts in the REPL
- Added the duckduckgo-search MCP server for searching the web (in addition to the built-in tools for web searches)
- Support for Gemini OAuth
- Support authenticating or refreshing OAuth for supported clients from within the REPL
- Allow first-runs to select OAuth for supported providers
- Support OAuth authentication flows for Claude
- Improved MCP server spinup and spindown when switching contexts or settings in the REPL: Modify existing config rather than stopping all servers always and re-initializing if unnecessary
- Allow the explore agent to run search queries for understanding docs or API specs
- Allow the oracle to perform web searches for deeper research
- Added web search support to the main sisyphus agent to answer user queries
- Created a CodeRabbit-style code-reviewer agent
- Added configuration option in agents to indicate the timeout for user input before proceeding (defaults to 5 minutes)
- Added support for sub-agents to escalate user interaction requests from any depth to the parent agents for user interactions
- built-in user interaction tools to remove the need for the list/confirm/etc prompts in prompt tools and to enhance user interactions in Coyote
- Experimental update to sisyphus to use the new parallel agent spawning system
- Added an agent configuration property that allows auto-injecting sub-agent spawning instructions (when using the built-in sub-agent spawning system)
- Auto-dispatch support of sub-agents and support for the teammate pattern between subagents
- Full passive task queue integration for parallelization of subagents
- Implemented initial scaffolding for built-in sub-agent spawning tool call operations
- Initial models for agent parallelization
- Added interactive prompting between the LLM and the user in Sisyphus using the built-in Bash utils scripts
### Fix
- Clarified user text input interaction
- recursion bug with similarly named Bash search functions in the explore agent
- updated the error for unauthenticated oauth to include the REPL .authenticated command
- Corrected a bug in the coder agent that wasn't outputting a summary of the changes made, so the parent Sisyphus agent has no idea if the agent worked or not
- Claude code system prompt injected into claude requests to make them valid once again
- Do not inject tools when models don't support them; detect this conflict before API calls happen
- The REPL .authenticate command works from within sessions, agents, and roles with pre-configured models
- Implemented the path normalization fix for the oracle and explore agents
- Updated the atlassian MCP server endpoint to account for future deprecation
- Fixed a bug in the coder agent that was causing the agent to create absolute paths from the current directory
- the updated regex for secrets injection broke MCP server secrets interpolation because the regex greedily matched on new lines, replacing too much content. This fix just ignores commented out lines in YAML files by skipping commented out lines.
- Don't try to inject secrets into commented-out lines in the config
- Removed top_p parameter from some agents so they can work across model providers
- Improved sub-agent stdout and stderr output for users to follow
- Inject agent variables into environment variables for global tool calls when invoked from agents to modify global tool behavior
- Removed the unnecessary execute_commands tool from the oracle agent
- Added auto_confirm to the coder agent so sub-agent spawning doesn't freeze
- Fixed a bug in the new supervisor and todo built-ins that was causing errors with OpenAI models
- Added condition to sisyphus to always output a summary to clearly indicate completion
- Updated the sisyphus prompt to explicitly tell it to delegate to the coder agent when it wants to write any code at all except for trivial changes
- Added back in the auto_confirm variable into sisyphus
- Removed the now unnecessary is_stale_response that was breaking auto-continuing with parallel agents
- Bypassed enabled_tools for user interaction tools so if function calling is enabled at all, the LLM has access to the user interaction tools when in REPL mode
- When parallel agents run, only write to stdout from the parent and only display the parent's throbber
- Forgot to implement support for failing a task and keep all dependents blocked
- Clean up orphaned sub-agents when the parent agent
- Fixed the bash prompt utils so that they correctly show output when being run by a tool invocation
- Forgot to automatically add the bidirectional communication back up to parent agents from sub-agents (i.e. need to be able to check inbox and send messages)
- Agent delegation tools were not being passed into the {{__tools__}} placeholder so agents weren't delegating to subagents
### Refactor
- Made the oauth module more generic so it can support loopback OAuth (not just manual)
- Changed the default session name for Sisyphus to temp (to require users to explicitly name sessions they wish to save)
- Updated the sisyphus agent to use the built-in user interaction tools instead of custom bash-based tools
- Cleaned up some left-over implementation stubs
## v0.2.0 (2026-02-14)
### Feat
- Simplified sisyphus prompt to improve functionality
- Supported the injection of RAG sources into the prompt, not just via the `.sources rag` command in the REPL so models can directly reference the documents that supported their responses
- Created the Sisyphus agent to make Coyote function like Claude Code, Gemini, Codex, etc.
- Created the Oracle agent to handle high-level architectural decisions and design questions about a given codebase
- Updated the coder agent to be much more task-focused and to be delegated to by Sisyphus
- Created the explore agent for exploring codebases to help answer questions
- Use the official atlassian MCP server for the jira-helper agent
- Created fs_glob to enable more targeted file exploration utilities
- Created a new tool 'fs_grep' to search a given file's contents for relevant lines to reduce token usage for smaller models
- Created the new fs_read tool to enable controlled reading of a file
- Let agent level variables be defined to bypass guard protections for tool invocations
- Implemented a built-in task management system to help smaller LLMs complete larger multistep tasks and minimize context drift
- Improved tool and MCP invocation error handling by returning stderr to the model when it is available
- Added variable interpolation for conversation starters in agents
- Implemented retry logic for failed tool invocations so the LLM can learn from the result and try again; Also implemented chain loop detection to prevent loops
- Added gemini-3-pro to the supported vertexai models
- Added an environment variable that lets users bypass guard operations in bash scripts. This is useful for agent routing
- Added support for thought-signatures for Gemini 3+ models
### Fix
- Improved continuation prompt to not make broad todo-items
- Allow auto-continuation to work in agents after a session is compressed and if there's still unfinish items in the to-do list
- fs_ls and fs_cat outputs should always redirect to "$LLM_OUTPUT" including on errors.
- Claude tool calls work incorrectly when tool doesn't require any arguments or flags; would provide an empty JSON object or error on no args
- Fixed a bug where --agent-variable values were not being passed to the agents
## v0.1.3 (2025-12-13)
### Feat
- Improved MCP implementation to minimize the tokens needed to utilize it so it doesn't quickly overwhelm the token space for a given model
## v0.1.2 (2025-11-08)
### Refactor
- Gave the GitHub MCP server a default placeholder value that doesn't require the vault
## v0.1.1 (2025-11-08)
## v0.1.0 (2025-11-07)
@@ -327,8 +21,8 @@
- Support for secret injection into the global config file (API keys, for example)
- Improved MCP handling toggle handling
- Secret injection into the MCP configuration
- added REPL support for interacting with the Coyote vault
- Integrated gman with Coyote to create a vault and added flags to configure the Coyote vault
- added REPL support for interacting with the Loki vault
- Integrated gman with Loki to create a vault and added flags to configure the Loki vault
- Added a default session to the jira helper to make interaction more natural
- Created the repo-analyzer role
- Created the coder and sql agents
+3 -11
View File
@@ -2,7 +2,7 @@
Contributors are very welcome! **No contribution is too small and all contributions are valued.**
## Rust
You'll need to have the stable Rust toolchain installed in order to develop Coyote.
You'll need to have the stable Rust toolchain installed in order to develop Loki.
The Rust toolchain (stable) can be installed via rustup using the following command:
@@ -48,8 +48,7 @@ cz commit
1. Clone this repo
2. Run `cargo test` to set up hooks
3. Make changes
4. Run the application using `just run` or `just run`
- Install `just` (`cargo install just`) if you haven't already to use the [justfile](./justfile) in this project.
4. Run the application using `make run` or `cargo run`
5. Commit changes. This will trigger pre-commit hooks that will run format, test and lint. If there are errors or
warnings from Clippy, please fix them.
6. Push your code to a new branch named after the feature/bug/etc. you're adding. This will trigger pre-push hooks that
@@ -76,13 +75,6 @@ Then, you can run workflows locally without having to commit and see if the GitH
act -W .github/workflows/release.yml --input_type bump=minor
```
## Authorship Policy
All code in this repository is written and reviewed by humans. AI-generated code (e.g., Copilot, ChatGPT,
Claude, etc.) is not permitted unless explicitly disclosed and approved.
Submissions must certify that the contributor understands and can maintain the code they submit.
## Questions? Reach out to me!
If you encounter any questions while developing Coyote, please don't hesitate to reach out to me at
If you encounter any questions while developing Loki, please don't hesitate to reach out to me at
alex.j.tusa@gmail.com. I'm happy to help contributors in any way I can, regardless of if they're new or experienced!
-31
View File
@@ -1,31 +0,0 @@
# Credits
## AIChat
Coyote originally started as a fork of the fantastic
[AIChat CLI](https://github.com/sigoden/aichat). The initial goal was simply
to fix a bug in how MCP servers worked with AIChat, allowing different MCP
servers to be specified per agent. Since then, Coyote has evolved far beyond
its original scope and grown into a passion project with a life of its own.
Today, Coyote includes first-class MCP server support (for both local and remote
servers), a built-in vault for interpolating secrets in configuration files,
built-in agents and macros, dynamic tab completions, integrated custom
functions (no external `argc` dependency), improved documentation, and much
more with many more ideas planned for the future.
Coyote is now developed and maintained as an independent project. Full credit
for the original foundation goes to the developers of the wonderful
AIChat project.
This project is not affiliated with or endorsed by the AIChat maintainers.
## AIChat
Coyote originally began as a fork of [AIChat CLI](https://github.com/sigoden/aichat),
created and maintained by the AIChat contributors.
While Coyote has since diverged significantly and is now developed as an
independent project, its early foundation and inspiration came from the
AIChat project.
AIChat is licensed under the MIT License.
Generated
+1544 -2026
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+29 -38
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@@ -1,16 +1,16 @@
[package]
name = "coyote-ai"
version = "0.6.0"
name = "loki-ai"
version = "0.1.1"
edition = "2024"
authors = ["Alex Clarke <alex.j.tusa@gmail.com>"]
description = "An all-in-one, batteries included LLM CLI Tool"
keywords = ["chatgpt", "llm", "cli", "ai", "repl"]
homepage = "https://github.com/Dark-Alex-17/coyote"
repository = "https://github.com/Dark-Alex-17/coyote"
homepage = "https://github.com/Dark-Alex-17/loki"
repository = "https://github.com/Dark-Alex-17/loki"
categories = ["command-line-utilities"]
readme = "README.md"
license = "MIT"
rust-version = "1.95.0"
rust-version = "1.89.0"
exclude = [".github", "CONTRIBUTING.md"]
[dependencies]
@@ -18,11 +18,10 @@ anyhow = "1.0.69"
bytes = "1.4.0"
clap = { version = "4.5.40", features = ["cargo", "derive", "wrap_help"] }
dirs = "6.0.0"
dunce = "1.0.5"
futures-util = "0.3.29"
inquire = "0.9.4"
inquire = "0.7.0"
is-terminal = "0.4.9"
reedline = "0.47.0"
reedline = "0.40.0"
serde = { version = "1.0.152", features = ["derive"] }
serde_json = { version = "1.0.93", features = ["preserve_order"] }
serde_yaml = "0.9.17"
@@ -34,7 +33,11 @@ tokio = { version = "1.34.0", features = [
"rt-multi-thread",
"full",
] }
crossterm = "0.29.0"
tokio-graceful = "0.2.2"
tokio-stream = { version = "0.1.15", default-features = false, features = [
"sync",
] }
crossterm = "0.28.1"
chrono = "0.4.23"
bincode = { version = "2.0.0", features = [
"serde",
@@ -47,7 +50,7 @@ nu-ansi-term = "0.50.0"
async-trait = "0.1.74"
textwrap = "0.16.0"
ansi_colours = "1.2.2"
eventsource-stream = "0.2.3"
reqwest-eventsource = "0.6.0"
log = "0.4.28"
log4rs = { version = "1.4.0", features = ["file_appender"] }
shell-words = "1.1.0"
@@ -55,14 +58,20 @@ sha2 = "0.10.8"
unicode-width = "0.2.0"
async-recursion = "1.1.1"
http = "1.1.0"
http-body-util = "0.1"
hyper = { version = "1.0", features = ["full"] }
hyper-util = { version = "0.1", features = ["server-auto", "client-legacy"] }
time = { version = "0.3.36", features = ["macros"] }
indexmap = { version = "2.2.6", features = ["serde"] }
hmac = "0.12.1"
aws-smithy-eventstream = "0.60.4"
urlencoding = "2.1.3"
unicode-segmentation = "1.11.0"
json-patch = { version = "4.0.0", default-features = false }
bitflags = "2.5.0"
path-absolutize = "3.1.1"
hnsw_rs = "0.3.0"
rayon = "1.10.0"
uuid = { version = "1.9.1", features = ["v4"] }
scraper = { version = "0.23.1", default-features = false, features = [
"deterministic",
@@ -79,41 +88,23 @@ duct = "1.0.0"
argc = "1.23.0"
strum_macros = "0.27.2"
indoc = "2.0.6"
rmcp = { version = "1.5.0", features = [
"client",
"transport-child-process",
"transport-streamable-http-client-reqwest",
"reqwest-native-tls",
] }
rmcp = { version = "0.6.1", features = ["client", "transport-child-process"] }
num_cpus = "1.17.0"
tree-sitter = "0.26.8"
tree-sitter-python = "0.25.0"
tree-sitter-typescript = "0.23"
rustpython-parser = "0.4.0"
rustpython-ast = "0.4.0"
colored = "3.0.0"
clap_complete = { version = "4.5.58", features = ["unstable-dynamic"] }
gman = "0.5.0"
gman = "0.2.3"
clap_complete_nushell = "4.5.9"
open = "5"
rand = { version = "0.10.0", features = ["default"] }
url = "2.5.8"
self_update = { version = "0.44", default-features = false, features = [
"reqwest",
"rustls",
"archive-tar",
"compression-flate2",
"archive-zip",
"compression-zip-deflate",
] }
[dependencies.reqwest]
version = "0.13.3"
version = "0.12.0"
features = [
"json",
"multipart",
"stream",
"form",
"socks",
"rustls",
"rustls-tls",
"rustls-tls-native-roots",
]
default-features = false
@@ -123,7 +114,7 @@ default-features = false
features = ["parsing", "regex-onig", "plist-load"]
[target.'cfg(target_os = "macos")'.dependencies]
crossterm = { version = "0.29.0", features = ["use-dev-tty"] }
crossterm = { version = "0.28.1", features = ["use-dev-tty"] }
[target.'cfg(target_os = "linux")'.dependencies]
arboard = { version = "3.3.0", default-features = false, features = [
@@ -135,10 +126,10 @@ arboard = { version = "3.3.0", default-features = false }
[dev-dependencies]
pretty_assertions = "1.4.0"
serial_test = "3"
rand = "0.9.0"
[[bin]]
name = "coyote"
name = "loki"
path = "src/main.rs"
[profile.release]
+114 -133
View File
@@ -1,114 +1,133 @@
# Coyote: All-in-one, batteries-included LLM CLI Tool
# Loki: All-in-one, batteries-included LLM CLI Tool
![Test](https://github.com/Dark-Alex-17/coyote/actions/workflows/ci.yaml/badge.svg)
[![crates.io link](https://img.shields.io/crates/v/coyote-ai.svg)](https://crates.io/crates/coyote-ai)
![Release](https://img.shields.io/github/v/release/Dark-Alex-17/coyote?color=%23c694ff)
![Crate.io downloads](https://img.shields.io/crates/d/coyote-ai?label=Crate%20downloads)
[![GitHub Downloads](https://img.shields.io/github/downloads/Dark-Alex-17/coyote/total.svg?label=GitHub%20downloads)](https://github.com/Dark-Alex-17/coyote/releases)
![Test](https://github.com/Dark-Alex-17/loki/actions/workflows/ci.yaml/badge.svg)
![LOC](https://tokei.rs/b1/github/Dark-Alex-17/loki?category=code)
[![crates.io link](https://img.shields.io/crates/v/loki-ai.svg)](https://crates.io/crates/loki-ai)
![Release](https://img.shields.io/github/v/release/Dark-Alex-17/loki?color=%23c694ff)
![Crate.io downloads](https://img.shields.io/crates/d/loki-ai?label=Crate%20downloads)
[![GitHub Downloads](https://img.shields.io/github/downloads/Dark-Alex-17/loki/total.svg?label=GitHub%20downloads)](https://github.com/Dark-Alex-17/loki/releases)
Coyote is an all-in-one, batteries-included, LLM CLI tool featuring Shell Assistant, CLI & REPL Mode, RAG, AI Tools &
Loki is an all-in-one, batteries-included, LLM CLI tool featuring Shell Assistant, CLI & REPL Mode, RAG, AI Tools &
Agents, and More.
It is designed to include a number of useful agents, roles, macros, and more so users can get up and running with Coyote
in as little time as possible. You can also install entire bundles of agents, roles, macros, tools, and MCP servers from
any git repository. See [Sharing Configurations](https://github.com/Dark-Alex-17/coyote/wiki/Sharing-Configurations) for more information.
It is designed to include a number of useful agents, roles, macros, and more so users can get up and running with Loki
in as little time as possible.
![Agent example](https://raw.githubusercontent.com/wiki/Dark-Alex-17/coyote/images/agents/sql.gif)
![Agent example](./docs/images/agents/sql.gif)
Coming from [AIChat](https://github.com/sigoden/aichat)? Follow the [migration guide](https://github.com/Dark-Alex-17/coyote/wiki/AIChat-Migration) to get started.
Coming from [AIChat](https://github.com/sigoden/aichat)? Follow the [migration guide](./docs/AICHAT-MIGRATION.md) to get started.
## Quick Links
* [AIChat Migration Guide](https://github.com/Dark-Alex-17/coyote/wiki/AIChat-Migration): Coming from AIChat? Follow the migration guide to get started.
* [Installation](#install): Install Coyote
* [Getting Started](#getting-started): Get started with Coyote by doing first-run setup steps.
* [Sharing Configurations](https://github.com/Dark-Alex-17/coyote/wiki/Sharing-Configurations): Install bundles of agents, roles, macros, tools, and MCP servers from any git repo, and share your own.
* [REPL](https://github.com/Dark-Alex-17/coyote/wiki/REPL): Interactive Read-Eval-Print Loop for conversational interactions with LLMs and Coyote.
* [Custom REPL Prompt](https://github.com/Dark-Alex-17/coyote/wiki/REPL-Prompt): Customize the REPL prompt to provide useful contextual information.
* [Vault](https://github.com/Dark-Alex-17/coyote/wiki/Vault): Securely store and manage sensitive information such as API keys and credentials.
* [Shell Integrations](https://github.com/Dark-Alex-17/coyote/wiki/Shell-Integrations): Seamlessly integrate Coyote with your shell environment for enhanced command-line assistance.
* [Function Calling](https://github.com/Dark-Alex-17/coyote/wiki/Tools): Leverage function calling capabilities to extend Coyote's functionality with custom tools
* [Creating Custom Tools](https://github.com/Dark-Alex-17/coyote/wiki/Custom-Tools): You can create your own custom tools to enhance Coyote's capabilities.
* [Create Custom Python Tools](https://github.com/Dark-Alex-17/coyote/wiki/Custom-Tools#custom-python-based-tools)
* [Create Custom TypeScript Tools](https://github.com/Dark-Alex-17/coyote/wiki/Custom-Tools#custom-typescript-based-tools)
* [Create Custom Bash Tools](https://github.com/Dark-Alex-17/coyote/wiki/Custom-Bash-Tools)
* [Bash Prompt Utilities](https://github.com/Dark-Alex-17/coyote/wiki/Bash-Prompt-Helpers)
* [First-Class MCP Server Support](https://github.com/Dark-Alex-17/coyote/wiki/MCP-Servers): Easily connect and interact with MCP servers for advanced functionality.
* [Macros](https://github.com/Dark-Alex-17/coyote/wiki/Macros): Automate repetitive tasks and workflows with Coyote "scripts" (macros).
* [RAG](https://github.com/Dark-Alex-17/coyote/wiki/RAG): Retrieval-Augmented Generation for enhanced information retrieval and generation.
* [Sessions](https://github.com/Dark-Alex-17/coyote/wiki/Sessions): Manage and persist conversational contexts and settings across multiple interactions.
* [Roles](https://github.com/Dark-Alex-17/coyote/wiki/Roles): Customize model behavior for specific tasks or domains.
* [Skills](https://github.com/Dark-Alex-17/coyote/wiki/Skills): Modular knowledge or capability packs the LLM can load and unload mid-conversation. Multiple skills compose; instructions stack, tools and MCPs union.
* [Agents](https://github.com/Dark-Alex-17/coyote/wiki/Agents): Leverage AI agents to perform complex tasks and workflows, including sub-agent spawning, teammate messaging, and user interaction tools.
* [Graph Agents](https://github.com/Dark-Alex-17/coyote/wiki/Graph-Agents): Define an agent as a declarative, YAML-driven workflow. A directed graph of typed nodes (LLM calls, scripts, approvals, user input, RAG retrieval, sub-agent spawns).
* [Todo System](https://github.com/Dark-Alex-17/coyote/wiki/TODO-System): Built-in task tracking for improved LLM reliability with smaller models.
* [Environment Variables](https://github.com/Dark-Alex-17/coyote/wiki/Environment-Variables): Override and customize your Coyote configuration at runtime with environment variables.
* [Client Configurations](https://github.com/Dark-Alex-17/coyote/wiki/Clients): Configuration instructions for various LLM providers.
* [Authentication (API Key & OAuth)](https://github.com/Dark-Alex-17/coyote/wiki/Clients#authentication): Authenticate with API keys or OAuth for subscription-based access.
* [Patching API Requests](https://github.com/Dark-Alex-17/coyote/wiki/Patches): Learn how to patch API requests for advanced customization.
* [Custom Themes](https://github.com/Dark-Alex-17/coyote/wiki/Themes): Change the look and feel of Coyote to your preferences with custom themes.
* [History](#history): A history of how Coyote came to be.
* [AIChat Migration Guide](./docs/AICHAT-MIGRATION.md): Coming from AIChat? Follow the migration guide to get started.
* [History](#history): A history of how Loki came to be.
* [Installation](#install): Install Loki
* [Getting Started](#getting-started): Get started with Loki by doing first-run setup steps.
* [REPL](./docs/REPL.md): Interactive Read-Eval-Print Loop for conversational interactions with LLMs and Loki.
* [Custom REPL Prompt](./docs/REPL-PROMPT.md): Customize the REPL prompt to provide useful contextual information.
* [Vault](./docs/VAULT.md): Securely store and manage sensitive information such as API keys and credentials.
* [Shell Integrations](./docs/SHELL-INTEGRATIONS.md): Seamlessly integrate Loki with your shell environment for enhanced command-line assistance.
* [Function Calling](./docs/function-calling/TOOLS.md#Tools): Leverage function calling capabilities to extend Loki's functionality with custom tools
* [Creating Custom Tools](./docs/function-calling/CUSTOM-TOOLS.md): You can create your own custom tools to enhance Loki's capabilities.
* [Create Custom Python Tools](./docs/function-calling/CUSTOM-TOOLS.md#custom-python-based-tools)
* [Create Custom Bash Tools](./docs/function-calling/CUSTOM-BASH-TOOLS.md)
* [Bash Prompt Utilities](./docs/function-calling/BASH-PROMPT-HELPERS.md)
* [First-Class MCP Server Support](./docs/function-calling/MCP-SERVERS.md): Easily connect and interact with MCP servers for advanced functionality.
* [Macros](./docs/MACROS.md): Automate repetitive tasks and workflows with Loki "scripts" (macros).
* [RAG](./docs/RAG.md): Retrieval-Augmented Generation for enhanced information retrieval and generation.
* [Sessions](/docs/SESSIONS.md): Manage and persist conversational contexts and settings across multiple interactions.
* [Roles](./docs/ROLES.md): Customize model behavior for specific tasks or domains.
* [Agents](/docs/AGENTS.md): Leverage AI agents to perform complex tasks and workflows.
* [Environment Variables](./docs/ENVIRONMENT-VARIABLES.md): Override and customize your Loki configuration at runtime with environment variables.
* [Client Configurations](./docs/clients/CLIENTS.md): Configuration instructions for various LLM providers.
* [Patching API Requests](./docs/clients/PATCHES.md): Learn how to patch API requests for advanced customization.
* [Custom Themes](./docs/THEMES.md): Change the look and feel of Loki to your preferences with custom themes.
---
## History
Loki originally started as a fork of the fantastic [AIChat CLI](https://github.com/sigoden/aichat). The purpose was to
simply fix a bug in how MCP servers worked with AIChat so that I could specify different ones for agents. However, it
has since evolved far beyond that and become a passion project with a life of its own!
Loki now has first class MCP server support (with support for local and remote servers alike), a built-in vault for
interpolating secrets in configuration files, built-in agents, built-in macros, dynamic tab completions, integrated
custom functions (no `argc` dependency), improved documentation, and much more with many more plans for the future!
The original kudos goes out to all the developers of the wonderful AIChat project!
---
## Prerequisites
Coyote requires the following tools to be installed on your system:
Loki requires the following tools to be installed on your system:
* [jq](https://github.com/jqlang/jq)
* `brew install jq`
* [jira (optional)](https://github.com/ankitpokhrel/jira-cli/wiki/Installation) (For the `jira-helper` agent)
* `brew tap ankitpokhrel/jira-cli && brew install jira-cli`
* You'll need to [create a JIRA API token](https://id.atlassian.com/manage-profile/security/api-tokens) for authentication
* Then, save it as an environment variable to your shell profile:
```sh
# ~/.bashrc or ~/.zshrc
export JIRA_API_TOKEN="your_jira_api_token_here"
```
* Then run `jira init`, select installation type as `cloud`, and provide the required details to generate a config
file for the Jira CLI.
* [usql](https://github.com/xo/usql) (For the `sql` agent)
* `brew install xo/xo/usql`
* [docker](https://docs.docker.com/engine/install/)
* [uv](https://docs.astral.sh/uv/getting-started/installation/)
* `curl -LsSf https://astral.sh/uv/install.sh | sh`
These tools are used to provide various functionalities within Coyote, such as document processing, JSON manipulation,
etc., and they are used within agents and tools.
These tools are used to provide various functionalities within Loki, such as document processing, JSON manipulation,
interaction with Jira, and they are used within agents and tools.
## Install
### Cargo
If you have Cargo installed, then you can install `coyote` from Crates.io:
If you have Cargo installed, then you can install `loki` from Crates.io:
```shell
cargo install coyote-ai # Binary name is `coyote`
cargo install loki-ai # Binary name is `loki`
# If you encounter issues installing, try installing with '--locked'
cargo install --locked coyote-ai
cargo install --locked loki-ai
```
### Homebrew (Mac/Linux)
To install Coyote from Homebrew, install the `coyote` tap. Then you'll be able to install `coyote`:
To install Loki from Homebrew, install the `loki` tap. Then you'll be able to install `loki`:
```shell
brew tap Dark-Alex-17/coyote
brew install coyote
brew tap Dark-Alex-17/loki
brew install loki
# If you need to be more specific, use:
brew install Dark-Alex-17/coyote/coyote
brew install Dark-Alex-17/loki/loki
```
To upgrade `coyote` using Homebrew:
To upgrade `loki` using Homebrew:
```shell
brew upgrade coyote
brew upgrade loki
```
### Scripts
#### Linux/MacOS (`bash`)
You can use the following command to run a bash script that downloads and installs the latest version of `coyote` for your
You can use the following command to run a bash script that downloads and installs the latest version of `loki` for your
OS (Linux/MacOS) and architecture (x86_64/arm64):
```shell
curl -fsSL https://raw.githubusercontent.com/Dark-Alex-17/coyote/main/install_coyote.sh | bash
curl -fsSL https://raw.githubusercontent.com/Dark-Alex-17/loki/main/install_loki.sh | bash
```
#### Windows/Linux/MacOS (`PowerShell`)
You can use the following command to run a PowerShell script that downloads and installs the latest version of `coyote`
You can use the following command to run a PowerShell script that downloads and installs the latest version of `loki`
for your OS (Windows/Linux/MacOS) and architecture (x86_64/arm64):
```powershell
powershell -NoProfile -ExecutionPolicy Bypass -Command "iwr -useb https://raw.githubusercontent.com/Dark-Alex-17/coyote/main/scripts/install_coyote.ps1 | iex"
powershell -NoProfile -ExecutionPolicy Bypass -Command "iwr -useb https://raw.githubusercontent.com/Dark-Alex-17/loki/main/scripts/install_loki.ps1 | iex"
```
### Manual
Binaries are available on the [releases](https://github.com/Dark-Alex-17/coyote/releases) page for the following platforms:
Binaries are available on the [releases](https://github.com/Dark-Alex-17/loki/releases) page for the following platforms:
| Platform | Architecture(s) |
|----------------|-----------------|
@@ -119,103 +138,75 @@ Binaries are available on the [releases](https://github.com/Dark-Alex-17/coyote/
#### Windows Instructions
To use a binary from the releases page on Windows, do the following:
1. Download the latest [binary](https://github.com/Dark-Alex-17/coyote/releases) for your OS.
1. Download the latest [binary](https://github.com/Dark-Alex-17/loki/releases) for your OS.
2. Use 7-Zip or TarTool to unpack the Tar file.
3. Run the executable `coyote.exe`!
3. Run the executable `loki.exe`!
#### Linux/MacOS Instructions
To use a binary from the releases page on Linux/MacOS, do the following:
1. Download the latest [binary](https://github.com/Dark-Alex-17/coyote/releases) for your OS.
1. Download the latest [binary](https://github.com/Dark-Alex-17/loki/releases) for your OS.
2. `cd` to the directory where you downloaded the binary.
3. Extract the binary with `tar -C /usr/local/bin -xzf coyote-<arch>.tar.gz` (Note: This may require `sudo`)
4. Now you can run `coyote`!
## Updating
Coyote can update itself in place to the latest GitHub release. Run `coyote --update`
for the newest release, or `coyote --update v0.4.0` for a specific version:
```shell
coyote --update
coyote --update v0.4.0
```
The same is available from within the REPL via `.update` and `.update v0.4.0`.
If Coyote was installed with a package manager, prefer that package manager so its
records stay in sync with the binary on disk; i.e. `brew upgrade coyote` for Homebrew,
or `cargo install --locked coyote-ai` for Cargo.
When Coyote detects a package-manager install it prints a warning and asks for
confirmation. In a non-interactive shell (no TTY), pass `--force` to update
anyway:
```shell
coyote --update --force
```
3. Extract the binary with `tar -C /usr/local/bin -xzf loki-<arch>.tar.gz` (Note: This may require `sudo`)
4. Now you can run `loki`!
## Getting Started
After installation, you can generate the configuration files and directories by simply running:
```sh
coyote --info
loki --info
```
Then, you need to set up the Coyote vault by creating a vault password file. Coyote will do this for you automatically and
Then, you need to set up the Loki vault by creating a vault password file. Loki will do this for you automatically and
guide you through the process when you first attempt to access the vault. So, to get started, you can run:
```sh
coyote --list-secrets
loki --list-secrets
```
### Authentication
Each client in your configuration needs authentication (with a few exceptions; e.g. ollama). Most clients use an API key
(set via `api_key` in the config or through the [vault](https://github.com/Dark-Alex-17/coyote/wiki/Vault)). For providers that support OAuth (e.g. Claude Pro/Max
subscribers, Google Gemini), you can authenticate with your existing subscription instead:
### First Time Setup
In order for Loki to function correctly, you'll need to add a few secrets to the Loki vault so the MCP servers can
function.
```yaml
# In your config.yaml
clients:
- type: claude
name: my-claude-oauth
auth: oauth # Indicate you want to authenticate with OAuth instead of an API key
```
**GitHub MCP Server:**
* `GITHUB_PERSONAL_ACCESS_TOKEN` - A GitHub Personal Access Token with `repo` and `workflow` scopes.
See [Creating a GitHub Personal Access Token](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens)
#### Add the secrets to the Loki vault
You can add the secrets to the Loki vault using the following commands (First time use will prompt you to create a vault
password file):
```sh
coyote --authenticate my-claude-oauth
# Or via the REPL: .authenticate
loki --add-secret GITHUB_PERSONAL_ACCESS_TOKEN
```
For full details, see the [authentication documentation](https://github.com/Dark-Alex-17/coyote/wiki/Clients#authentication).
### Tab-Completions
You can also enable tab completions to make using Coyote easier. To do so, add the following to your shell profile:
You can also enable tab completions to make using Loki easier. To do so, add the following to your shell profile:
```shell
# Bash
# (add to: `~/.bashrc`)
source <(COMPLETE=bash coyote)
source <(COMPLETE=bash loki)
# Zsh
# (add to: `~/.zshrc`)
source <(COMPLETE=zsh coyote)
source <(COMPLETE=zsh loki)
# Fish
# (add to: `~/.config/fish/config.fish`)
source <(COMPLETE=fish coyote | psub)
source <(COMPLETE=fish loki | psub)
# Elvish
# (add to: `~/.elvish/rc.elv`)
eval (E:COMPLETE=elvish coyote | slurp)
eval (E:COMPLETE=elvish loki | slurp)
# PowerShell
# (add to: `$PROFILE`)
$env:COMPLETE = "powershell"
coyote | Out-String | Invoke-Expression
loki | Out-String | Invoke-Expression
```
### Shell Integration
You can integrate Coyote's Shell Assistant into your shell for enhanced command-line assistance. Add the code in the
corresponding [shell integration script](./scripts/shell-integration) to your shell. Then, you can invoke Coyote to convert natural language to
You can integrate Loki's Shell Assistant into your shell for enhanced command-line assistance. Add the code in the
corresponding [shell integration script](./scripts/shell-integration) to your shell. Then, you can invoke Loki to convert natural language to
shell commands by pressing `Alt-e`. For example:
```shell
@@ -225,18 +216,18 @@ find . -name "*.md"
```
## Configuration
The location of the global Coyote configuration varies between systems, so you can use the following command to find your
The location of the global Loki configuration varies between systems, so you can use the following command to find your
`config.yaml` file:
```shell
coyote --info | grep 'config_file' | awk '{print $2}'
loki --info | grep 'config_file' | awk '{print $2}'
```
The configuration file consists of a number of settings. To see a full example configuration file with every setting
defined, refer to the [example configuration file](./config.example.yaml).
### Default LLM
The following settings are available to configure the default LLM that is used when you start Coyote, and its
The following settings are available to configure the default LLM that is used when you start Loki, and its
hyperparameters:
| Setting | Description |
@@ -246,34 +237,34 @@ hyperparameters:
| `top_p` | The default `top_p` hyperparameter value to use for all models, with a range of (0,1) (or (0,2) for some models); <br>Used unless explicitly overridden |
### CLI Behavior
You can use the following settings to modify the behavior of Coyote:
You can use the following settings to modify the behavior of Loki:
| Setting | Default Value | Description |
|---------------|---------------|-------------------------------------------------------------------------------------------------------------------------------------|
| `stream` | `true` | Controls whether to use stream-style APIs when querying for completions from LLM providers |
| `save` | `true` | Controls whether to save each query/response to every model to `messages.md` for posterity; Useful for debugging |
| `keybindings` | `emacs` | Specifies which keybinding schema to use; can either be `emacs` or `vi` |
| `editor` | `null` | What text editor Coyote should use to edit the input buffer or session (e.g. `vim`, `emacs`, `nano`, `hx`); <br>Defaults to `$EDITOR` |
| `editor` | `null` | What text editor Loki should use to edit the input buffer or session (e.g. `vim`, `emacs`, `nano`, `hx`); <br>Defaults to `$EDITOR` |
| `wrap` | `no` | Controls whether text is wrapped (can be `no`, `auto`, or some `<max_width>` |
| `wrap_code` | `false` | Enables or disables the wrapping of code blocks |
### Preludes
Preludes let you define the default behavior for the different operating modes of Coyote. The available settings are
Preludes let you define the default behavior for the different operating modes of Loki. The available settings are
shown below:
| Setting | Description |
|-----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `repl_prelude` | This setting lets you specify a default `session` or `role` to use when starting Coyote in [REPL](https://github.com/Dark-Alex-17/coyote/wiki/REPL) mode. <br>Values can be <ul><li>`role:<name>` to define a role</li><li>`session:<name>` to define a session</li><li>`<session>:<role>` to define both a session and a role to use</li></ul> |
| `cmd_prelude` | This setting lets you specify a default `session` or `role` to use when running one-off queries in Coyote via the CLI. <br>Values can be <ul><li>`role:<name>` to define a role</li><li>`session:<name>` to define a session</li><li>`<session>:<role>` to define both a session and a role to use</li></ul> |
| `repl_prelude` | This setting lets you specify a default `session` or `role` to use when starting Loki in [REPL](./docs/REPL.md) mode. <br>Values can be <ul><li>`role:<name>` to define a role</li><li>`session:<name>` to define a session</li><li>`<session>:<role>` to define both a session and a role to use</li></ul> |
| `cmd_prelude` | This setting lets you specify a default `session` or `role` to use when running one-off queries in Loki via the CLI. <br>Values can be <ul><li>`role:<name>` to define a role</li><li>`session:<name>` to define a session</li><li>`<session>:<role>` to define both a session and a role to use</li></ul> |
| `agent_session` | This setting is used to specify a default session that all agents should start into, unless otherwise specified in the agent configuration. (e.g. `temp`, `default`) |
### Appearance
The appearance of Coyote can be modified using the following settings:
The appearance of Loki can be modified using the following settings:
| Setting | Default Value | Description |
|---------------|---------------|------------------------------------------------------|
| `highlight` | `true` | This setting enables or disables syntax highlighting |
| `light_theme` | `false` | This setting toggles light mode in Coyote |
| `light_theme` | `false` | This setting toggles light mode in Loki |
### Miscellaneous Settings
| Setting | Default Value | Description |
@@ -281,15 +272,5 @@ The appearance of Coyote can be modified using the following settings:
| `user_agent` | `null` | The name of the `User-Agent` that should be passed in the `User-Agent` header on all requests to model providers |
| `save_shell_history` | `true` | Enables or disables REPL command history |
---
## History
Coyote began as a fork of [AIChat CLI](https://github.com/sigoden/aichat) and has since evolved into an independent project.
See [CREDITS.md](./CREDITS.md) for full attribution and background.
---
## Creator
* [Alex Clarke](https://github.com/Dark-Alex-17)
* [Alex Clarke](https://github.com/Dark-Alex-17)
-319
View File
@@ -1,319 +0,0 @@
#!/usr/bin/env bash
# Shared Agent Utilities - Minimal, focused helper functions
set -euo pipefail
#######################
## PROJECT DETECTION ##
#######################
# Cache file name for detected project info
_COYOTE_PROJECT_CACHE=".coyote-project.json"
# Read cached project detection if valid
# Usage: _read_project_cache "/path/to/project"
# Returns: cached JSON on stdout (exit 0) or nothing (exit 1)
_read_project_cache() {
local dir="$1"
local cache_file="${dir}/${_COYOTE_PROJECT_CACHE}"
if [[ -f "${cache_file}" ]]; then
local cached
cached=$(cat "${cache_file}" 2>/dev/null) || return 1
if echo "${cached}" | jq -e '.type and .build != null and .test != null and .check != null' &>/dev/null; then
echo "${cached}"
return 0
fi
fi
return 1
}
# Write project detection result to cache
# Usage: _write_project_cache "/path/to/project" '{"type":"rust",...}'
_write_project_cache() {
local dir="$1"
local json="$2"
local cache_file="${dir}/${_COYOTE_PROJECT_CACHE}"
echo "${json}" > "${cache_file}" 2>/dev/null || true
}
_detect_heuristic() {
local dir="$1"
# Rust
if [[ -f "${dir}/Cargo.toml" ]]; then
echo '{"type":"rust","build":"cargo build","test":"cargo test","check":"cargo check"}'
return 0
fi
# Go
if [[ -f "${dir}/go.mod" ]]; then
echo '{"type":"go","build":"go build ./...","test":"go test ./...","check":"go vet ./..."}'
return 0
fi
# Node.JS/Deno/Bun
if [[ -f "${dir}/deno.json" ]] || [[ -f "${dir}/deno.jsonc" ]]; then
echo '{"type":"deno","build":"deno task build","test":"deno test","check":"deno lint"}'
return 0
fi
if [[ -f "${dir}/package.json" ]]; then
local pm="npm"
[[ -f "${dir}/bun.lockb" ]] || [[ -f "${dir}/bun.lock" ]] && pm="bun"
[[ -f "${dir}/pnpm-lock.yaml" ]] && pm="pnpm"
[[ -f "${dir}/yarn.lock" ]] && pm="yarn"
echo "{\"type\":\"nodejs\",\"build\":\"${pm} run build\",\"test\":\"${pm} test\",\"check\":\"${pm} run lint\"}"
return 0
fi
# Python
if [[ -f "${dir}/pyproject.toml" ]] || [[ -f "${dir}/setup.py" ]] || [[ -f "${dir}/setup.cfg" ]]; then
local test_cmd="pytest"
local check_cmd="ruff check ."
if [[ -f "${dir}/poetry.lock" ]]; then
test_cmd="poetry run pytest"
check_cmd="poetry run ruff check ."
elif [[ -f "${dir}/uv.lock" ]]; then
test_cmd="uv run pytest"
check_cmd="uv run ruff check ."
fi
echo "{\"type\":\"python\",\"build\":\"\",\"test\":\"${test_cmd}\",\"check\":\"${check_cmd}\"}"
return 0
fi
# JVM (Maven)
if [[ -f "${dir}/pom.xml" ]]; then
echo '{"type":"java","build":"mvn compile","test":"mvn test","check":"mvn verify"}'
return 0
fi
# JVM (Gradle)
if [[ -f "${dir}/build.gradle" ]] || [[ -f "${dir}/build.gradle.kts" ]]; then
local gw="gradle"
[[ -f "${dir}/gradlew" ]] && gw="./gradlew"
echo "{\"type\":\"java\",\"build\":\"${gw} build\",\"test\":\"${gw} test\",\"check\":\"${gw} check\"}"
return 0
fi
# .NET / C#
if compgen -G "${dir}/*.sln" &>/dev/null || compgen -G "${dir}/*.csproj" &>/dev/null; then
echo '{"type":"dotnet","build":"dotnet build","test":"dotnet test","check":"dotnet build --warnaserrors"}'
return 0
fi
# C/C++ (CMake)
if [[ -f "${dir}/CMakeLists.txt" ]]; then
echo '{"type":"cmake","build":"cmake --build build","test":"ctest --test-dir build","check":"cmake --build build"}'
return 0
fi
# Ruby
if [[ -f "${dir}/Gemfile" ]]; then
local test_cmd="bundle exec rake test"
[[ -f "${dir}/Rakefile" ]] && grep -q "rspec" "${dir}/Gemfile" 2>/dev/null && test_cmd="bundle exec rspec"
echo "{\"type\":\"ruby\",\"build\":\"\",\"test\":\"${test_cmd}\",\"check\":\"bundle exec rubocop\"}"
return 0
fi
# Elixir
if [[ -f "${dir}/mix.exs" ]]; then
echo '{"type":"elixir","build":"mix compile","test":"mix test","check":"mix credo"}'
return 0
fi
# PHP
if [[ -f "${dir}/composer.json" ]]; then
echo '{"type":"php","build":"","test":"./vendor/bin/phpunit","check":"./vendor/bin/phpstan analyse"}'
return 0
fi
# Swift
if [[ -f "${dir}/Package.swift" ]]; then
echo '{"type":"swift","build":"swift build","test":"swift test","check":"swift build"}'
return 0
fi
# Zig
if [[ -f "${dir}/build.zig" ]]; then
echo '{"type":"zig","build":"zig build","test":"zig build test","check":"zig build"}'
return 0
fi
# Generic build systems (last resort before LLM)
if [[ -f "${dir}/justfile" ]] || [[ -f "${dir}/Justfile" ]]; then
echo '{"type":"just","build":"just build","test":"just test","check":"just lint"}'
return 0
fi
if [[ -f "${dir}/Makefile" ]] || [[ -f "${dir}/makefile" ]] || [[ -f "${dir}/GNUmakefile" ]]; then
echo '{"type":"make","build":"make build","test":"make test","check":"make lint"}'
return 0
fi
return 1
}
# Gather lightweight evidence about a project for LLM analysis
# Usage: _gather_project_evidence "/path/to/project"
# Returns: evidence string on stdout
_gather_project_evidence() {
local dir="$1"
local evidence=""
evidence+="Root files and directories:"$'\n'
evidence+=$(ls -1 "${dir}" 2>/dev/null | head -50)
evidence+=$'\n\n'
evidence+="File extension counts:"$'\n'
evidence+=$(find "${dir}" -type f \
-not -path '*/.git/*' \
-not -path '*/node_modules/*' \
-not -path '*/target/*' \
-not -path '*/dist/*' \
-not -path '*/__pycache__/*' \
-not -path '*/vendor/*' \
-not -path '*/.build/*' \
2>/dev/null \
| sed 's/.*\.//' | sort | uniq -c | sort -rn | head -10)
evidence+=$'\n\n'
local config_patterns=("*.toml" "*.yaml" "*.yml" "*.json" "*.xml" "*.gradle" "*.gradle.kts" "*.cabal" "*.pro" "Makefile" "justfile" "Justfile" "Dockerfile" "Taskfile*" "BUILD" "WORKSPACE" "flake.nix" "shell.nix" "default.nix")
local found_configs=0
for pattern in "${config_patterns[@]}"; do
if [[ ${found_configs} -ge 5 ]]; then
break
fi
local files
files=$(find "${dir}" -maxdepth 1 -name "${pattern}" -type f 2>/dev/null)
while IFS= read -r f; do
if [[ -n "${f}" && ${found_configs} -lt 5 ]]; then
local basename
basename=$(basename "${f}")
evidence+="--- ${basename} (first 30 lines) ---"$'\n'
evidence+=$(head -30 "${f}" 2>/dev/null)
evidence+=$'\n\n'
found_configs=$((found_configs + 1))
fi
done <<< "${files}"
done
echo "${evidence}"
}
# LLM-based project detection fallback
# Usage: _detect_with_llm "/path/to/project"
# Returns: JSON on stdout or empty (exit 1)
_detect_with_llm() {
local dir="$1"
local evidence
evidence=$(_gather_project_evidence "${dir}")
local prompt
prompt=$(cat <<-EOF
Analyze this project directory and determine the project type, primary language, and the correct shell commands to build, test, and check (lint/typecheck) it.
EOF
)
prompt+=$'\n'"${evidence}"$'\n'
prompt+=$(cat <<-EOF
Respond with ONLY a valid JSON object. No markdown fences, no explanation, no extra text.
The JSON must have exactly these 4 keys:
{"type":"<language>","build":"<build command>","test":"<test command>","check":"<lint or typecheck command>"}
Rules:
- "type" must be a single lowercase word (e.g. rust, go, python, nodejs, java, ruby, elixir, cpp, c, zig, haskell, scala, kotlin, dart, swift, php, dotnet, etc.)
- If a command doesn't apply to this project, use an empty string, ""
- Use the most standard/common commands for the detected ecosystem
- If you detect a package manager lockfile, use that package manager (e.g. pnpm over npm)
EOF
)
local llm_response
llm_response=$(coyote --no-stream "${prompt}" 2>/dev/null) || return 1
llm_response=$(echo "${llm_response}" | sed 's/^```json//;s/^```//;s/```$//' | tr -d '\n' | sed 's/^[[:space:]]*//')
llm_response=$(echo "${llm_response}" | grep -o '{[^}]*}' | head -1)
if echo "${llm_response}" | jq -e '.type and .build != null and .test != null and .check != null' &>/dev/null; then
echo "${llm_response}" | jq -c '{type: (.type // "unknown"), build: (.build // ""), test: (.test // ""), check: (.check // "")}'
return 0
fi
return 1
}
# Detect project type and return build/test commands
# Uses: cached result -> fast heuristics -> LLM fallback
detect_project() {
local dir="${1:-.}"
local cached
if cached=$(_read_project_cache "${dir}"); then
echo "${cached}" | jq -c '{type, build, test, check}'
return 0
fi
local result
if result=$(_detect_heuristic "${dir}"); then
local enriched
enriched=$(echo "${result}" | jq -c '. + {"_detected_by":"heuristic","_cached_at":"'"$(date -Iseconds)"'"}')
_write_project_cache "${dir}" "${enriched}"
echo "${result}"
return 0
fi
if result=$(_detect_with_llm "${dir}"); then
local enriched
enriched=$(echo "${result}" | jq -c '. + {"_detected_by":"llm","_cached_at":"'"$(date -Iseconds)"'"}')
_write_project_cache "${dir}" "${enriched}"
echo "${result}"
return 0
fi
echo '{"type":"unknown","build":"","test":"","check":""}'
}
###########################
## FILE SEARCH UTILITIES ##
###########################
_search_files() {
local pattern="$1"
local dir="${2:-.}"
find "${dir}" -type f -name "${pattern}" \
-not -path '*/target/*' \
-not -path '*/node_modules/*' \
-not -path '*/.git/*' \
-not -path '*/dist/*' \
-not -path '*/__pycache__/*' \
2>/dev/null | head -25
}
get_tree() {
local dir="${1:-.}"
local depth="${2:-3}"
if command -v tree &>/dev/null; then
tree -L "${depth}" --noreport -I 'node_modules|target|dist|.git|__pycache__|*.pyc' "${dir}" 2>/dev/null || find "${dir}" -maxdepth "${depth}" -type f | head -50
else
find "${dir}" -maxdepth "${depth}" -type f \
-not -path '*/target/*' \
-not -path '*/node_modules/*' \
-not -path '*/.git/*' \
2>/dev/null | head -50
fi
}
-36
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@@ -1,36 +0,0 @@
# Code Reviewer
A CodeRabbit-style code review orchestrator that coordinates per-file reviews and synthesizes findings into a unified
report.
This agent acts as the manager for the review process, delegating actual file analysis to **[File Reviewer](../file-reviewer/README.md)**
agents while handling coordination and final reporting.
## Features
- 🤖 **Orchestration**: Spawns parallel reviewers for each changed file.
- 🔄 **Cross-File Context**: Broadcasts sibling rosters so reviewers can alert each other about cross-cutting changes.
- 📊 **Unified Reporting**: Synthesizes findings into a structured, easy-to-read summary with severity levels.
-**Parallel Execution**: Runs reviews concurrently for maximum speed.
## Pro-Tip: Use an IDE MCP Server for Improved Performance
Many modern IDEs now include MCP servers that let LLMs perform operations within the IDE itself and use IDE tools. Using
an IDE's MCP server dramatically improves the performance of coding agents. So if you have an IDE, try adding that MCP
server to your config (see the [MCP Server docs](../../../docs/function-calling/MCP-SERVERS.md) to see how to configure
them), and modify the agent definition to look like this:
```yaml
# ...
mcp_servers:
- jetbrains # The name of your configured IDE MCP server
global_tools:
- fs_read.sh
- fs_grep.sh
- fs_glob.sh
# - execute_command.sh
# ...
```
-163
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@@ -1,163 +0,0 @@
name: code-reviewer
description: CodeRabbit-style code reviewer - spawns per-file reviewers, synthesizes findings
version: 2.0.0
auto_continue: true
max_auto_continues: 20
inject_todo_instructions: true
can_spawn_agents: true
max_concurrent_agents: 10
max_agent_depth: 2
skills_enabled: true
enabled_skills:
- delegation-protocol
- parallel-research
variables:
- name: project_dir
description: Project directory to review
default: '.'
global_tools:
- fs_read.sh
- fs_cat.sh
- fs_grep.sh
- fs_glob.sh
- execute_command.sh
instructions: |
You are a code review orchestrator, similar to CodeRabbit. You coordinate per-file reviews and produce a unified report.
## Step 0: Load orchestration skills
Before doing anything else, call `skill__load` for `delegation-protocol` and `parallel-research`. They carry the methodology you need:
- **`delegation-protocol`** — how to write delegation prompts that give the sub-agent its full context (TASK / EXPECTED OUTCOME / MUST DO / MUST NOT DO / CONTEXT). Apply this format when spawning each file-reviewer.
- **`parallel-research`** — the spawn-and-wait protocol, the anti-duplication rule (don't redo work you delegated), and the rule about ending your response and letting the system notify you on agent completion.
Both skills are always-on for this agent's workflow. Skill bodies are your source of truth for HOW to delegate and HOW to coordinate parallel work; this agent's instructions handle the CodeRabbit-specific shape.
## Workflow
1. **Get the diff:** Run `get_diff` to get the git diff (defaults to staged changes, falls back to unstaged)
2. **Parse changed files:** Extract the list of files from the diff
3. **Create todos:** One todo per phase (get diff, spawn reviewers, collect results, synthesize report)
4. **Spawn file-reviewers:** One `file-reviewer` agent per changed file, in parallel. Apply the `delegation-protocol` structured prompt format.
5. **Broadcast sibling roster:** Send each file-reviewer a message with all sibling IDs and their file assignments
6. **Collect all results:** Per `parallel-research`, do not poll. End your response after spawns + roster; the system will notify you when agents complete.
7. **Synthesize:** Combine all findings into a CodeRabbit-style report
## Spawning File Reviewers
Apply the `delegation-protocol` structured prompt format. Each spawn gets the full TASK / EXPECTED OUTCOME / MUST DO / MUST NOT DO / CONTEXT sections — the file-reviewer hasn't seen the codebase or the broader PR; the spawn prompt IS its entire context.
```
agent__spawn --agent file-reviewer --prompt "
## TASK
Review the git diff for <file_path>. Produce structured findings per your output format.
## EXPECTED OUTCOME
A REVIEW_COMPLETE-terminated report following your standard format:
- ## File: <file_path>
- ### Summary (1-2 sentences)
- ### Findings (each with severity, lines, description, suggestion)
- ### Cross-File Concerns (or 'None')
## MUST DO
- Load `code-review` and `ai-slop-remover` skills before reading any code
- Apply both skill checklists to the diff
- Use targeted fs_read with offset/limit; max 5 file reads
- End with REVIEW_COMPLETE
## MUST NOT DO
- Do not modify files (you are read-only)
- Do not review unchanged code unrelated to the diff
- Do not omit findings to keep the report short
## CONTEXT
Project: {{project_dir}}
File under review: <file_path>
Diff:
<diff content for this file>
"
```
Paste the actual diff hunk(s) inline — the reviewer can't see your context. If you have prior knowledge of the change's intent (PR description, ticket), include it in CONTEXT.
## Sibling Roster Broadcast
After spawning ALL file-reviewers (collecting their IDs), send each one a message with the roster:
```
agent__send_message --to <agent_id> --message "SIBLING_ROSTER:
- <agent_id_1>: reviewing <file_1>
- <agent_id_2>: reviewing <file_2>
...
Send cross-cutting alerts to relevant siblings if your changes affect their files."
```
## Diff Parsing
Split the diff by file. Each file's diff starts with `diff --git a/<path> b/<path>`. Extract:
- The file path (from the `+++ b/<path>` line)
- All hunks for that file (from `@@` markers to the next `diff --git` or end)
Skip binary files and files with only whitespace changes.
## Final Report Format
After collecting all file-reviewer results, synthesize into:
```
# Code Review Summary
## Walkthrough
<2-3 sentence overview of what the changes do as a whole>
## Changes
| File | Changes | Findings |
|------|---------|----------|
| `path/to/file1.rs` | <brief description> | 🔴 1 🟡 2 🟢 1 |
| `path/to/file2.rs` | <brief description> | 🟢 2 💡 1 |
## Detailed Findings
### `path/to/file1.rs`
<paste file-reviewer's findings here, cleaned up>
### `path/to/file2.rs`
<paste file-reviewer's findings here, cleaned up>
## Cross-File Concerns
<any cross-cutting issues identified by the teammate pattern>
---
*Reviewed N files, found X critical, Y warnings, Z suggestions, W nitpicks*
```
## Edge Cases
- **Single file changed:** Still spawn one file-reviewer (for consistency), skip roster broadcast
- **Too many files (>10):** Group small files (< 20 lines changed) and review them together
- **No changes found:** Report "No changes to review" and exit
- **Binary files:** Skip with a note in the summary
## Rules
1. **Always use `get_diff` first:** Don't assume what changed
2. **Spawn in parallel:** All file-reviewers should be spawned before collecting any
3. **Don't review code yourself:** Delegate ALL review work to file-reviewers
4. **Preserve severity tags:** Don't downgrade or remove severity from file-reviewer findings
5. **Include ALL findings:** Don't summarize away specific issues
6. **File reads:** If you do read a file directly (e.g. to verify a finding before synthesis), `fs_read` returns a TRUNCATED view with line numbers (default 2000 lines, long lines cut at 2000 chars). Use `fs_cat` only when you need the FULL untruncated contents of a file.
## Context
- Project: {{project_dir}}
- CWD: {{__cwd__}}
- Shell: {{__shell__}}
## Available Tools:
{{__tools__}}
-478
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@@ -1,478 +0,0 @@
#!/usr/bin/env bash
set -eo pipefail
# shellcheck disable=SC1090
source "$LLM_PROMPT_UTILS_FILE"
source "$LLM_ROOT_DIR/agents/.shared/utils.sh"
# @env LLM_OUTPUT=/dev/stdout
# @env LLM_AGENT_VAR_PROJECT_DIR=.
# @describe Code review orchestrator tools
_project_dir() {
local dir="${LLM_AGENT_VAR_PROJECT_DIR:-.}"
(cd "${dir}" 2>/dev/null && pwd) || echo "${dir}"
}
# @cmd Get git diff for code review. Returns staged changes, or unstaged if nothing is staged, or HEAD~1 diff if working tree is clean.
# @option --base Optional base ref to diff against (e.g., "main", "HEAD~3", a commit SHA)
get_diff() {
local project_dir
project_dir=$(_project_dir)
# shellcheck disable=SC2154
local base="${argc_base:-}"
local diff_output=""
if [[ -n "${base}" ]]; then
diff_output=$(cd "${project_dir}" && git diff "${base}" 2>&1) || true
else
diff_output=$(cd "${project_dir}" && git diff --cached 2>&1) || true
if [[ -z "${diff_output}" ]]; then
diff_output=$(cd "${project_dir}" && git diff 2>&1) || true
fi
if [[ -z "${diff_output}" ]]; then
diff_output=$(cd "${project_dir}" && git diff HEAD~1 2>&1) || true
fi
fi
if [[ -z "${diff_output}" ]]; then
warn "No changes found to review" >> "$LLM_OUTPUT"
return 0
fi
local file_count
file_count=$(echo "${diff_output}" | grep -c '^diff --git' || true)
{
info "Diff contains changes to ${file_count} file(s)"
echo ""
echo "${diff_output}"
} >> "$LLM_OUTPUT"
}
# @cmd Get list of changed files with stats
# @option --base Optional base ref to diff against
get_changed_files() {
local project_dir
project_dir=$(_project_dir)
local base="${argc_base:-}"
local stat_output=""
if [[ -n "${base}" ]]; then
stat_output=$(cd "${project_dir}" && git diff --stat "${base}" 2>&1) || true
else
stat_output=$(cd "${project_dir}" && git diff --cached --stat 2>&1) || true
if [[ -z "${stat_output}" ]]; then
stat_output=$(cd "${project_dir}" && git diff --stat 2>&1) || true
fi
if [[ -z "${stat_output}" ]]; then
stat_output=$(cd "${project_dir}" && git diff --stat HEAD~1 2>&1) || true
fi
fi
if [[ -z "${stat_output}" ]]; then
warn "No changes found" >> "$LLM_OUTPUT"
return 0
fi
{
info "Changed files:"
echo ""
echo "${stat_output}"
} >> "$LLM_OUTPUT"
}
# @cmd Get project structure and type information
get_project_info() {
local project_dir
project_dir=$(_project_dir)
local project_info
project_info=$(detect_project "${project_dir}")
{
info "Project: ${project_dir}"
echo "Type: $(echo "${project_info}" | jq -r '.type')"
echo ""
get_tree "${project_dir}" 2
} >> "$LLM_OUTPUT"
}
# ARGC-BUILD {
# This block was generated by argc (https://github.com/sigoden/argc).
# Modifying it manually is not recommended
_argc_run() {
if [[ "${1:-}" == "___internal___" ]]; then
_argc_die "error: unsupported ___internal___ command"
fi
if [[ "${OS:-}" == "Windows_NT" ]] && [[ -n "${MSYSTEM:-}" ]]; then
set -o igncr
fi
argc__args=("$(basename "$0" .sh)" "$@")
argc__positionals=()
_argc_index=1
_argc_len="${#argc__args[@]}"
_argc_tools=()
_argc_parse
if [ -n "${argc__fn:-}" ]; then
$argc__fn "${argc__positionals[@]}"
fi
}
_argc_usage() {
cat <<-'EOF'
Code review orchestrator tools
USAGE: <COMMAND>
COMMANDS:
get_diff Get git diff for code review. Returns staged changes, or unstaged if nothing is staged, or HEAD~1 diff if working tree is clean. [aliases: get-diff]
get_changed_files Get list of changed files with stats [aliases: get-changed-files]
get_project_info Get project structure and type information [aliases: get-project-info]
ENVIRONMENTS:
LLM_OUTPUT [default: /dev/stdout]
LLM_AGENT_VAR_PROJECT_DIR [default: .]
EOF
exit
}
_argc_version() {
echo 0.0.0
exit
}
_argc_parse() {
local _argc_key _argc_action
local _argc_subcmds="get_diff, get-diff, get_changed_files, get-changed-files, get_project_info, get-project-info"
while [[ $_argc_index -lt $_argc_len ]]; do
_argc_item="${argc__args[_argc_index]}"
_argc_key="${_argc_item%%=*}"
case "$_argc_key" in
--help | -help | -h)
_argc_usage
;;
--version | -version | -V)
_argc_version
;;
--)
_argc_dash="${#argc__positionals[@]}"
argc__positionals+=("${argc__args[@]:$((_argc_index + 1))}")
_argc_index=$_argc_len
break
;;
get_diff | get-diff)
_argc_index=$((_argc_index + 1))
_argc_action=_argc_parse_get_diff
break
;;
get_changed_files | get-changed-files)
_argc_index=$((_argc_index + 1))
_argc_action=_argc_parse_get_changed_files
break
;;
get_project_info | get-project-info)
_argc_index=$((_argc_index + 1))
_argc_action=_argc_parse_get_project_info
break
;;
help)
local help_arg="${argc__args[$((_argc_index + 1))]:-}"
case "$help_arg" in
get_diff | get-diff)
_argc_usage_get_diff
;;
get_changed_files | get-changed-files)
_argc_usage_get_changed_files
;;
get_project_info | get-project-info)
_argc_usage_get_project_info
;;
"")
_argc_usage
;;
*)
_argc_die "error: invalid value \`$help_arg\` for \`<command>\`"$'\n'" [possible values: $_argc_subcmds]"
;;
esac
;;
*)
_argc_die "error: \`\` requires a subcommand but one was not provided"$'\n'" [subcommands: $_argc_subcmds]"
;;
esac
done
if [[ -n "${_argc_action:-}" ]]; then
$_argc_action
else
_argc_usage
fi
}
_argc_usage_get_diff() {
cat <<-'EOF'
Get git diff for code review. Returns staged changes, or unstaged if nothing is staged, or HEAD~1 diff if working tree is clean.
USAGE: get_diff [OPTIONS]
OPTIONS:
--base <BASE> Optional base ref to diff against (e.g., "main", "HEAD~3", a commit SHA)
-h, --help Print help
ENVIRONMENTS:
LLM_OUTPUT [default: /dev/stdout]
LLM_AGENT_VAR_PROJECT_DIR [default: .]
EOF
exit
}
_argc_parse_get_diff() {
local _argc_key _argc_action
local _argc_subcmds=""
while [[ $_argc_index -lt $_argc_len ]]; do
_argc_item="${argc__args[_argc_index]}"
_argc_key="${_argc_item%%=*}"
case "$_argc_key" in
--help | -help | -h)
_argc_usage_get_diff
;;
--)
_argc_dash="${#argc__positionals[@]}"
argc__positionals+=("${argc__args[@]:$((_argc_index + 1))}")
_argc_index=$_argc_len
break
;;
--base)
_argc_take_args "--base <BASE>" 1 1 "-" ""
_argc_index=$((_argc_index + _argc_take_args_len + 1))
if [[ -z "${argc_base:-}" ]]; then
argc_base="${_argc_take_args_values[0]:-}"
else
_argc_die "error: the argument \`--base\` cannot be used multiple times"
fi
;;
*)
if _argc_maybe_flag_option "-" "$_argc_item"; then
_argc_die "error: unexpected argument \`$_argc_key\` found"
fi
argc__positionals+=("$_argc_item")
_argc_index=$((_argc_index + 1))
;;
esac
done
if [[ -n "${_argc_action:-}" ]]; then
$_argc_action
else
argc__fn=get_diff
if [[ "${argc__positionals[0]:-}" == "help" ]] && [[ "${#argc__positionals[@]}" -eq 1 ]]; then
_argc_usage_get_diff
fi
if [[ -z "${LLM_OUTPUT:-}" ]]; then
export LLM_OUTPUT=/dev/stdout
fi
if [[ -z "${LLM_AGENT_VAR_PROJECT_DIR:-}" ]]; then
export LLM_AGENT_VAR_PROJECT_DIR=.
fi
fi
}
_argc_usage_get_changed_files() {
cat <<-'EOF'
Get list of changed files with stats
USAGE: get_changed_files [OPTIONS]
OPTIONS:
--base <BASE> Optional base ref to diff against
-h, --help Print help
ENVIRONMENTS:
LLM_OUTPUT [default: /dev/stdout]
LLM_AGENT_VAR_PROJECT_DIR [default: .]
EOF
exit
}
_argc_parse_get_changed_files() {
local _argc_key _argc_action
local _argc_subcmds=""
while [[ $_argc_index -lt $_argc_len ]]; do
_argc_item="${argc__args[_argc_index]}"
_argc_key="${_argc_item%%=*}"
case "$_argc_key" in
--help | -help | -h)
_argc_usage_get_changed_files
;;
--)
_argc_dash="${#argc__positionals[@]}"
argc__positionals+=("${argc__args[@]:$((_argc_index + 1))}")
_argc_index=$_argc_len
break
;;
--base)
_argc_take_args "--base <BASE>" 1 1 "-" ""
_argc_index=$((_argc_index + _argc_take_args_len + 1))
if [[ -z "${argc_base:-}" ]]; then
argc_base="${_argc_take_args_values[0]:-}"
else
_argc_die "error: the argument \`--base\` cannot be used multiple times"
fi
;;
*)
if _argc_maybe_flag_option "-" "$_argc_item"; then
_argc_die "error: unexpected argument \`$_argc_key\` found"
fi
argc__positionals+=("$_argc_item")
_argc_index=$((_argc_index + 1))
;;
esac
done
if [[ -n "${_argc_action:-}" ]]; then
$_argc_action
else
argc__fn=get_changed_files
if [[ "${argc__positionals[0]:-}" == "help" ]] && [[ "${#argc__positionals[@]}" -eq 1 ]]; then
_argc_usage_get_changed_files
fi
if [[ -z "${LLM_OUTPUT:-}" ]]; then
export LLM_OUTPUT=/dev/stdout
fi
if [[ -z "${LLM_AGENT_VAR_PROJECT_DIR:-}" ]]; then
export LLM_AGENT_VAR_PROJECT_DIR=.
fi
fi
}
_argc_usage_get_project_info() {
cat <<-'EOF'
Get project structure and type information
USAGE: get_project_info
ENVIRONMENTS:
LLM_OUTPUT [default: /dev/stdout]
LLM_AGENT_VAR_PROJECT_DIR [default: .]
EOF
exit
}
_argc_parse_get_project_info() {
local _argc_key _argc_action
local _argc_subcmds=""
while [[ $_argc_index -lt $_argc_len ]]; do
_argc_item="${argc__args[_argc_index]}"
_argc_key="${_argc_item%%=*}"
case "$_argc_key" in
--help | -help | -h)
_argc_usage_get_project_info
;;
--)
_argc_dash="${#argc__positionals[@]}"
argc__positionals+=("${argc__args[@]:$((_argc_index + 1))}")
_argc_index=$_argc_len
break
;;
*)
argc__positionals+=("$_argc_item")
_argc_index=$((_argc_index + 1))
;;
esac
done
if [[ -n "${_argc_action:-}" ]]; then
$_argc_action
else
argc__fn=get_project_info
if [[ "${argc__positionals[0]:-}" == "help" ]] && [[ "${#argc__positionals[@]}" -eq 1 ]]; then
_argc_usage_get_project_info
fi
if [[ -z "${LLM_OUTPUT:-}" ]]; then
export LLM_OUTPUT=/dev/stdout
fi
if [[ -z "${LLM_AGENT_VAR_PROJECT_DIR:-}" ]]; then
export LLM_AGENT_VAR_PROJECT_DIR=.
fi
fi
}
_argc_take_args() {
_argc_take_args_values=()
_argc_take_args_len=0
local param="$1" min="$2" max="$3" signs="$4" delimiter="$5"
if [[ "$min" -eq 0 ]] && [[ "$max" -eq 0 ]]; then
return
fi
local _argc_take_index=$((_argc_index + 1)) _argc_take_value
if [[ "$_argc_item" == *=* ]]; then
_argc_take_args_values=("${_argc_item##*=}")
else
while [[ $_argc_take_index -lt $_argc_len ]]; do
_argc_take_value="${argc__args[_argc_take_index]}"
if _argc_maybe_flag_option "$signs" "$_argc_take_value"; then
if [[ "${#_argc_take_value}" -gt 1 ]]; then
break
fi
fi
_argc_take_args_values+=("$_argc_take_value")
_argc_take_args_len=$((_argc_take_args_len + 1))
if [[ "$_argc_take_args_len" -ge "$max" ]]; then
break
fi
_argc_take_index=$((_argc_take_index + 1))
done
fi
if [[ "${#_argc_take_args_values[@]}" -lt "$min" ]]; then
_argc_die "error: incorrect number of values for \`$param\`"
fi
if [[ -n "$delimiter" ]] && [[ "${#_argc_take_args_values[@]}" -gt 0 ]]; then
local item values arr=()
for item in "${_argc_take_args_values[@]}"; do
IFS="$delimiter" read -r -a values <<<"$item"
arr+=("${values[@]}")
done
_argc_take_args_values=("${arr[@]}")
fi
}
_argc_maybe_flag_option() {
local signs="$1" arg="$2"
if [[ -z "$signs" ]]; then
return 1
fi
local cond=false
if [[ "$signs" == *"+"* ]]; then
if [[ "$arg" =~ ^\+[^+].* ]]; then
cond=true
fi
elif [[ "$arg" == -* ]]; then
if (( ${#arg} < 3 )) || [[ ! "$arg" =~ ^---.* ]]; then
cond=true
fi
fi
if [[ "$cond" == "false" ]]; then
return 1
fi
local value="${arg%%=*}"
if [[ "$value" =~ [[:space:]] ]]; then
return 1
fi
return 0
}
_argc_die() {
if [[ $# -eq 0 ]]; then
cat
else
echo "$*" >&2
fi
exit 1
}
_argc_run "$@"
# ARGC-BUILD }
+10 -76
View File
@@ -1,82 +1,16 @@
# Coder
A graph-based implementation agent. Plans, implements, and runs build +
tests in a bounded fix-loop until verified. Designed to be delegated to by
the **[Sisyphus](../sisyphus/README.md)** agent.
An AI agent that assists you with your coding tasks.
Coder is a [graph agent](https://github.com/Dark-Alex-17/coyote/wiki/Graph-Agents): its workflow is
defined declaratively in `graph.yaml`, with verification and the
implement-fix loop enforced as graph edges rather than prose.
## Features
## Workflow
- 🏗️ Intelligent project structure creation and management
- 🖼️ Convert screenshots into clean, functional code
- 📁 Comprehensive file system operations (create folders, files, read/write files)
- 🧐 Advanced code analysis and improvement suggestions
- 📊 Precise diff-based file editing for controlled code modifications
```
analyze_request (llm + output_schema) plan + complexity extraction
route_complexity (script) opt-out approval gate (complexity ≥ 7)
gate_approval (approval, optional)
implement (llm + fs tools) actual file edits
verify_build (script)
verify_tests (script)
fix_loop_gate (script) back-edge to implement (bounded)
end_success / end_rejected / end_failure
```
## Similar Projects
End nodes emit one of three sentinel outcomes for the caller:
- `CODER_COMPLETE` — build and tests passed.
- `CODER_REJECTED` — user rejected the plan at the approval gate.
- `CODER_FAILED` — fix-loop exhausted; build/tests still failing.
## Tuning
The agent's `project_dir` is exposed via the standard `variables:` block,
so it accepts the runtime override flag:
```sh
# Invoke from inside the project (project_dir defaults to ".")
cd /path/to/your/project
coyote -a coder "Add a foo() function..."
# Or invoke from anywhere with an explicit override
coyote -a coder --agent-variable project_dir /path/to/your/project "Add..."
```
`graph.yaml` `initial_state` exposes:
- `max_fix_attempts` (default `3`) — fix-loop budget before `end_failure`.
Environment overrides honored by the script nodes:
- `BUILD_CMD` — skip project-type detection for the build/check command.
- `TEST_CMD` — skip detection for tests.
- `CODER_AUTOAPPROVE=1` — bypass the approval gate (for non-interactive runs
where complexity might trip the gate).
## Pro-Tip: IDE MCP Server
Modern IDEs (JetBrains, VS Code, Cursor, Zed, etc.) expose MCP servers
that let LLMs use IDE tools directly. To wire one in, edit `graph.yaml`:
```yaml
mcp_servers:
- your-ide-mcp-server
global_tools:
# Keep read-only fs tools for files outside the IDE project
- fs_read.sh
- fs_grep.sh
- fs_glob.sh
# - fs_write.sh
# - fs_patch.sh
- execute_command.sh
```
Then add the MCP server's write/patch tools to the `implement` node's
`tools:` whitelist.
- https://github.com/Doriandarko/claude-engineer
- https://github.com/paul-gauthier/aider
+53
View File
@@ -0,0 +1,53 @@
name: Coder
description: An AI agent that assists you with your coding tasks
version: 0.1.0
global_tools:
- fs_mkdir.sh
- fs_ls.sh
- fs_patch.sh
- fs_cat.sh
instructions: |
You are an exceptional software developer with vast knowledge across multiple programming languages, frameworks, and best practices.
Your capabilities include:
1. Creating and managing project structures
2. Writing, debugging, and improving code across multiple languages
3. Providing architectural insights and applying design patterns
4. Staying current with the latest technologies and best practices
5. Analyzing and manipulating files within the project directory
Available tools and their optimal use cases:
1. fs_mkdir: Create new directories in the project structure.
2. fs_create: Generate new files with specified contents.
3. fs_patch: Examine and modify existing files.
4. fs_cat: View the contents of existing files without making changes.
5. fs_ls: Understand the current project structure or locate specific files.
Tool Usage Guidelines:
- Always use the most appropriate tool for the task at hand.
- For file modifications, use fs_patch. Read the file first, then apply changes if needed.
- After making changes, always review the diff output to ensure accuracy.
Project Creation and Management:
1. Start by creating a root folder for new projects.
2. Create necessary subdirectories and files within the root folder.
3. Organize the project structure logically, following best practices for the specific project type.
Code Editing Best Practices:
1. Always read the file content before making changes.
2. Analyze the code and determine necessary modifications.
3. Pay close attention to existing code structure to avoid unintended alterations.
4. Review changes thoroughly after each modification.
Always strive for accuracy, clarity, and efficiency in your responses and actions.
Answer the user's request using relevant tools (if they are available). Before calling a tool, do some analysis within <thinking></thinking> tags. First, think about which of the provided tools is the relevant tool to answer the user's request. Second, go through each of the required parameters of the relevant tool and determine if the user has directly provided or given enough information to infer a value. When deciding if the parameter can be inferred, carefully consider all the context to see if it supports a specific value. If all of the required parameters are present or can be reasonably inferred, close the thinking tag and proceed with the tool call. BUT, if one of the values for a required parameter is missing, DO NOT invoke the function (not even with fillers for the missing params) and instead, ask the user to provide the missing parameters. DO NOT ask for more information on optional parameters if it is not provided.
Do not reflect on the quality of the returned search results in your response.
conversation_starters:
- 'Create a new Python project structure for a web application'
- 'Explain the code in file.py and suggest improvements'
- 'Search for the latest best practices in React development'
- 'Help me debug this error: [paste your error message]'
-375
View File
@@ -1,375 +0,0 @@
name: coder
description: |
Implementation agent. Plans, implements, and runs build + tests in a
bounded fix-loop until verified. Designed to be delegated to by sisyphus.
version: "1.0"
global_tools:
- fs_cat.sh
- fs_ls.sh
- fs_write.sh
- fs_patch.sh
- execute_command.sh
skills_enabled: true
enabled_skills:
- ai-slop-remover
- code-review
- git-master
- frontend-ui-ux
- verification-gates
variables:
- name: project_dir
description: |
Absolute path to the project directory. Defaults to "." which is the
directory you invoked `coyote` from. Override at runtime with
`coyote -a coder --agent-variable project_dir /abs/path "..."`.
default: "."
settings:
max_loop_iterations: 20
log_state_snapshots: true
validate_before_run: true
timeout: 1800
initial_state:
project_dir: ""
fix_attempts: 0
max_fix_attempts: 3
fix_instructions: ""
build_output: ""
tests_output: ""
last_node_output: ""
plan_summary: ""
files_to_modify: []
files_to_create: []
risks: []
complexity_score: 0
review_attempts: 0
max_review_attempts: 1
review_clean: true
review_notes: ""
start: resolve_paths
nodes:
resolve_paths:
id: resolve_paths
type: script
description: Resolve project_dir to an absolute path from the agent variable
script: scripts/resolve_paths.sh
timeout: 5
fallback: end_failure
analyze_request:
id: analyze_request
type: llm
description: Extract a structured plan and complexity score from the orchestrator's prompt
instructions: |
You are a senior engineer's planning assistant. Read the orchestrator's
request and emit a structured plan. You only plan. You never edit files.
Score complexity from 1 to 10:
1-3: trivial - single file, <=20 lines changed, obvious approach
4-6: moderate - 2-5 files, clear approach, some pattern matching
7-10: complex - multi-component, ambiguous tradeoffs, refactoring,
or wide blast radius
Be specific in `files_to_modify` and `files_to_create`. All paths
MUST be absolute. The project root is {{project_dir}}. Prefer paths
like "{{project_dir}}/src/foo.rs" over "src/foo.rs". The implementer
uses these paths directly with fs_write and fs_patch tools, which
resolve relative paths against the coyote invocation directory (NOT
the project dir). Empty arrays are fine if no files in that category.
`risks` is a list of short strings. Anything that could derail the
implementation: unknown dependencies, brittle tests, blast radius,
etc. Empty list is fine.
Project directory: {{project_dir}}
prompt: "{{initial_prompt}}"
tools: []
output_schema:
type: object
properties:
plan_summary:
type: string
description: 1-3 sentences summarizing what will be done
files_to_modify:
type: array
items: {type: string}
files_to_create:
type: array
items: {type: string}
complexity_score:
type: integer
minimum: 1
maximum: 10
risks:
type: array
items: {type: string}
required: [plan_summary, files_to_modify, files_to_create, complexity_score, risks]
state_updates:
last_node_output: "{{output}}"
fallback: end_failure
next: route_complexity
route_complexity:
id: route_complexity
type: script
description: Route to approval gate for complex plans; skip otherwise
script: scripts/route_complexity.sh
timeout: 5
fallback: implement
gate_approval:
id: gate_approval
type: approval
description: Optional human checkpoint for high-complexity plans
question: |
## Plan
{{plan_summary}}
## Files to modify
{{files_to_modify}}
## Files to create
{{files_to_create}}
## Risks
{{risks}}
Complexity: {{complexity_score}}/10
Approve this plan?
options:
- "yes"
- "no"
routes:
"yes": implement
"no": end_rejected
on_other: end_rejected
implement:
id: implement
type: llm
description: Write code via fs tools. Bounded tool-call loop.
skills_enabled: true
enabled_skills:
- ai-slop-remover
- code-review
- git-master
- frontend-ui-ux
- verification-gates
instructions: |
You are a senior engineer. Implement the plan by writing code via
tools. Follow existing patterns in the codebase.
## Skills
Use `skill__list` to see what's available, then `skill__load` the ones
that fit the work: `ai-slop-remover` always, `frontend-ui-ux` when
touching UI, `git-master` when touching history, `verification-gates`
to remember what evidence is required. Unload when a phase ends.
## Writing code
1. Use `fs_patch` for surgical edits to existing files.
2. Use `fs_write` for new files or full rewrites.
3. NEVER write files via `execute_command`. Do not use `cat >`,
`cat >>`, `echo >`, `printf >`, `tee`, heredocs (`<<EOF`), or
`python3 -c "open(...).write(...)"`. Shell-based file writes
break on multi-line content, special characters, quoted strings,
and nested language blocks. `fs_write` and `fs_patch` handle
these correctly because they don't go through shell parsing.
4. NEVER output code to chat. Always use tools.
5. ALWAYS pass ABSOLUTE paths to fs_write and fs_patch. Relative
paths resolve against the coyote invocation directory (not the
project dir), which is rarely what you want. The project root
is {{project_dir}}.
## File reading
1. Use `execute_command` to grep/find:
`execute_command --command "grep -rn 'fn handle_request' --include='*.rs' ."`
`execute_command --command "find . -name '*.rs' -not -path '*/target/*'"`
2. Read only what you need:
`fs_cat --path "src/main.rs" --offset 50 --limit 30`
3. Never read entire large files. Use offset/limit.
4. Use `fs_ls` to list directory contents.
## Pattern matching
Before writing ANY file:
1. Find a similar existing file (grep, then read).
2. Match its style: imports, naming, structure, error handling.
3. Follow the same patterns exactly. Do not invent new ones.
## Fix loop
If the "Fix loop status" section in your user prompt is non-empty,
the previous attempt failed verification. Read the error, identify
the minimal fix, apply it. Do not refactor while fixing.
## Rules
1. Match existing patterns - read examples first.
2. Minimal changes - implement only what's asked.
3. Never suppress errors (`as any`, `@ts-ignore`, `#[allow(...)]`
on unfamiliar lints, etc.).
4. No dead code, no commented-out blocks, no premature abstractions.
5. End your turn when editing is done. The graph runs verification next.
Project directory: {{project_dir}}
prompt: |
## Plan summary
{{plan_summary}}
## Files involved
- Modify: {{files_to_modify}}
- Create: {{files_to_create}}
## Original request from the orchestrator
{{initial_prompt}}
## Fix loop status
{{fix_instructions}}
tools:
- fs_cat
- fs_ls
- fs_write
- fs_patch
- execute_command
max_iterations: 30
state_updates:
last_node_output: "{{output}}"
fallback: end_failure
next: verify_build
verify_build:
id: verify_build
type: script
description: Run the project's check/build command. Routes to verify_tests on success, fix_loop_gate on failure.
script: scripts/verify_build.sh
timeout: 300
fallback: fix_loop_gate
verify_tests:
id: verify_tests
type: script
description: Run the project's test command. Routes to end_success on pass, fix_loop_gate on failure.
script: scripts/verify_tests.sh
timeout: 600
fallback: fix_loop_gate
fix_loop_gate:
id: fix_loop_gate
type: script
description: Budget gate. Loops back to implement with fix_instructions populated, or terminates as end_failure.
script: scripts/fix_loop_gate.sh
timeout: 5
fallback: end_failure
self_review:
id: self_review
type: llm
description: Skill-driven self-review of the diff. Catches AI slop, dishonest naming, suppressed errors. Bounded to max_review_attempts.
skills_enabled: true
enabled_skills:
- code-review
- ai-slop-remover
instructions: |
You are reviewing the diff you just produced. Load `code-review` and
`ai-slop-remover` via `skill__load` and apply their checklists STRICTLY.
Flag ONLY concrete issues:
- Correctness bugs or uncovered edge cases
- Suppressed errors (as any, @ts-ignore, #[allow(...)] on unfamiliar
lints, empty catch blocks)
- Dishonest naming (get_X that mutates, returns wrong type, etc.)
- Useless comments that restate the code
- AI slop (filler prose, multi-paragraph docstrings, defensive
handling of impossible cases)
Do NOT flag:
- Style preferences if the pattern matches existing code in the repo
- Things the build/tests already verified
- "Could be more elegant" without a concrete bug
Be terse. The orchestrator wants signal, not noise. If you find nothing
blocking, set review_clean=true and leave review_notes empty.
Project directory: {{project_dir}}
prompt: |
## Files to review
Modified: {{files_to_modify}}
Created: {{files_to_create}}
## What the implementation was supposed to do
{{plan_summary}}
Read each file's changed region. Apply the review skills. Output your verdict.
tools:
- fs_cat
- fs_ls
- execute_command
max_iterations: 15
output_schema:
type: object
properties:
review_clean:
type: boolean
description: True if no blocker issues were found.
review_notes:
type: string
description: Concrete issues found, one per line as file:line - description. Empty when review_clean is true.
required: [review_clean, review_notes]
state_updates:
last_node_output: "{{output}}"
fallback: end_success
next: route_review_result
route_review_result:
id: route_review_result
type: script
description: Routes based on review_clean and review_attempts budget. End on clean or budget exhausted; loop to implement otherwise.
script: scripts/route_review_result.sh
timeout: 5
fallback: end_success
end_success:
id: end_success
type: end
output: |
CODER_COMPLETE
Plan: {{plan_summary}}
Files modified: {{files_to_modify}}
Files created: {{files_to_create}}
Build: passed
Tests: passed
end_rejected:
id: end_rejected
type: end
output: |
CODER_REJECTED
Plan was rejected at the approval gate.
Plan: {{plan_summary}}
end_failure:
id: end_failure
type: end
output: |
CODER_FAILED
Plan: {{plan_summary}}
Attempts: {{fix_attempts}}/{{max_fix_attempts}}
Last node output:
{{last_node_output}}
Last build output:
{{build_output}}
Last tests output:
{{tests_output}}
@@ -1,49 +0,0 @@
#!/usr/bin/env bash
set -euo pipefail
if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
state=$(cat "$GRAPH_STATE_FILE")
elif [[ -n "${GRAPH_STATE:-}" ]]; then
state="$GRAPH_STATE"
else
state='{}'
fi
fix_attempts=$(echo "$state" | jq -r '.fix_attempts // 0')
max_fix_attempts=$(echo "$state" | jq -r '.max_fix_attempts // 3')
build_ok=$(echo "$state" | jq -r '.build_ok | if . == null then "true" else (. | tostring) end')
tests_ok=$(echo "$state" | jq -r '.tests_ok | if . == null then "true" else (. | tostring) end')
build_output=$(echo "$state" | jq -r '.build_output // ""')
tests_output=$(echo "$state" | jq -r '.tests_output // ""')
if (( fix_attempts >= max_fix_attempts )); then
jq -nc \
--argjson n "$fix_attempts" \
'{
"fix_attempts": $n,
"_next": "end_failure"
}'
exit 0
fi
next_attempts=$((fix_attempts + 1))
if [[ "$build_ok" != "true" ]]; then
fix_instructions=$(printf '## Fix loop status (attempt %d of %d)\n\nThe previous attempt failed the build.\n\nBuild output:\n```\n%s\n```\n\nIdentify the minimal fix and apply it. Do not refactor.' \
"$next_attempts" "$max_fix_attempts" "$build_output")
elif [[ "$tests_ok" != "true" ]]; then
fix_instructions=$(printf '## Fix loop status (attempt %d of %d)\n\nBuild passed but tests failed.\n\nTest output:\n```\n%s\n```\n\nIdentify the minimal fix and apply it. Do not refactor.' \
"$next_attempts" "$max_fix_attempts" "$tests_output")
else
fix_instructions=$(printf '## Fix loop status (attempt %d of %d)\n\nfix_loop_gate was reached but no failure was detected in state. Re-run the verification step.' \
"$next_attempts" "$max_fix_attempts")
fi
jq -nc \
--argjson n "$next_attempts" \
--arg fi "$fix_instructions" \
'{
"fix_attempts": $n,
"fix_instructions": $fi,
"_next": "implement"
}'
@@ -1,12 +0,0 @@
#!/usr/bin/env bash
set -euo pipefail
project_dir="${LLM_AGENT_VAR_PROJECT_DIR:-.}"
resolved=$(cd "$project_dir" 2>/dev/null && pwd) || resolved="$project_dir"
jq -nc \
--arg pd "$resolved" \
'{
"project_dir": $pd,
"_next": "analyze_request"
}'
@@ -1,23 +0,0 @@
#!/usr/bin/env bash
set -euo pipefail
if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
state=$(cat "$GRAPH_STATE_FILE")
elif [[ -n "${GRAPH_STATE:-}" ]]; then
state="$GRAPH_STATE"
else
state='{}'
fi
complexity=$(echo "$state" | jq -r '.complexity_score // 0')
if [[ "${CODER_AUTOAPPROVE:-0}" == "1" ]]; then
jq -nc '{"_next": "implement"}'
exit 0
fi
if (( complexity >= 7 )); then
jq -nc '{"_next": "gate_approval"}'
else
jq -nc '{"_next": "implement"}'
fi
@@ -1,58 +0,0 @@
#!/usr/bin/env bash
set -euo pipefail
if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
state=$(cat "$GRAPH_STATE_FILE")
elif [[ -n "${GRAPH_STATE:-}" ]]; then
state="$GRAPH_STATE"
else
state='{}'
fi
review_clean=$(echo "$state" | jq -r '.review_clean // true')
review_attempts=$(echo "$state" | jq -r '.review_attempts // 0')
max_review_attempts=$(echo "$state" | jq -r '.max_review_attempts // 1')
review_notes=$(echo "$state" | jq -r '.review_notes // ""')
if [[ "$review_clean" != "true" && "$review_clean" != "false" ]]; then
echo "ERROR: review_clean must be boolean ('true'/'false'); got: $review_clean" >&2
exit 1
fi
if ! [[ "$review_attempts" =~ ^[0-9]+$ ]]; then
echo "ERROR: review_attempts must be a non-negative integer; got: $review_attempts" >&2
exit 1
fi
if ! [[ "$max_review_attempts" =~ ^[0-9]+$ ]]; then
echo "ERROR: max_review_attempts must be a non-negative integer; got: $max_review_attempts" >&2
exit 1
fi
if [[ "$review_clean" == "true" ]]; then
jq -nc '{"_next": "end_success"}'
exit 0
fi
if (( review_attempts >= max_review_attempts )); then
jq -nc \
--arg n "$review_notes" \
'{
"_next": "end_success",
"review_notes_unresolved": ("Shipped with unresolved review notes (budget exhausted):\n" + $n)
}'
exit 0
fi
next_review=$((review_attempts + 1))
fix_instr=$(printf '## Self-review feedback (attempt %d of %d)\n\nThe code review found concrete issues. Address them with minimal edits. Do not refactor unrelated code.\n\n%s' \
"$next_review" "$max_review_attempts" "$review_notes")
jq -nc \
--argjson n "$next_review" \
--arg fi "$fix_instr" \
'{
"review_attempts": $n,
"fix_instructions": $fi,
"_next": "implement"
}'
@@ -1,55 +0,0 @@
#!/usr/bin/env bash
set -uo pipefail
# shellcheck disable=SC1091
source "$(dirname "$0")/../../.shared/utils.sh"
if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
state=$(cat "$GRAPH_STATE_FILE")
elif [[ -n "${GRAPH_STATE:-}" ]]; then
state="$GRAPH_STATE"
else
state='{}'
fi
project_dir=$(echo "$state" | jq -r '.project_dir // "."')
if [[ -n "${BUILD_CMD:-}" ]]; then
cmd="$BUILD_CMD"
else
project_info=$(detect_project "$project_dir")
cmd=$(echo "$project_info" | jq -r '.check // .build // ""')
fi
if [[ -z "$cmd" || "$cmd" == "null" ]]; then
jq -nc '{
"build_ok": true,
"build_output": "(no build/check command available for this project type)",
"_next": "verify_tests"
}'
exit 0
fi
exit_code=0
output=$(cd "$project_dir" && eval "$cmd" 2>&1) || exit_code=$?
if (( exit_code == 0 )); then
jq -nc \
--arg out "$output" \
--arg cmd "$cmd" \
'{
"build_ok": true,
"build_output": ("Ran: " + $cmd + "\n\n" + $out),
"_next": "verify_tests"
}'
else
jq -nc \
--arg out "$output" \
--arg cmd "$cmd" \
--argjson rc "$exit_code" \
'{
"build_ok": false,
"build_output": ("Ran: " + $cmd + "\nExit code: " + ($rc | tostring) + "\n\n" + $out),
"_next": "fix_loop_gate"
}'
fi
@@ -1,55 +0,0 @@
#!/usr/bin/env bash
set -uo pipefail
# shellcheck disable=SC1091
source "$(dirname "$0")/../../.shared/utils.sh"
if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
state=$(cat "$GRAPH_STATE_FILE")
elif [[ -n "${GRAPH_STATE:-}" ]]; then
state="$GRAPH_STATE"
else
state='{}'
fi
project_dir=$(echo "$state" | jq -r '.project_dir // "."')
if [[ -n "${TEST_CMD:-}" ]]; then
cmd="$TEST_CMD"
else
project_info=$(detect_project "$project_dir")
cmd=$(echo "$project_info" | jq -r '.test // ""')
fi
if [[ -z "$cmd" || "$cmd" == "null" ]]; then
jq -nc '{
"tests_ok": true,
"tests_output": "(no test command available for this project type)",
"_next": "self_review"
}'
exit 0
fi
exit_code=0
output=$(cd "$project_dir" && eval "$cmd" 2>&1) || exit_code=$?
if (( exit_code == 0 )); then
jq -nc \
--arg out "$output" \
--arg cmd "$cmd" \
'{
"tests_ok": true,
"tests_output": ("Ran: " + $cmd + "\n\n" + $out),
"_next": "self_review"
}'
else
jq -nc \
--arg out "$output" \
--arg cmd "$cmd" \
--argjson rc "$exit_code" \
'{
"tests_ok": false,
"tests_output": ("Ran: " + $cmd + "\nExit code: " + ($rc | tostring) + "\n\n" + $out),
"_next": "fix_loop_gate"
}'
fi
+12 -92
View File
@@ -1,98 +1,18 @@
#!/usr/bin/env bash
set -eo pipefail
set -e
# @env LLM_OUTPUT=/dev/stdout The output path
# shellcheck disable=SC1090
source "$LLM_PROMPT_UTILS_FILE"
source "$LLM_ROOT_DIR/agents/.shared/utils.sh"
# @env LLM_OUTPUT=/dev/stdout
# @env LLM_AGENT_VAR_PROJECT_DIR=.
# @describe Coder agent tools for implementing code changes
_project_dir() {
local dir="${LLM_AGENT_VAR_PROJECT_DIR:-.}"
(cd "${dir}" 2>/dev/null && pwd) || echo "${dir}"
# @cmd Create a new file at the specified path with the given contents.
# @option --path! The path where the file should be created
# @option --contents! The contents of the file
# shellcheck disable=SC2154
fs_create() {
guard_path "$argc_path" "Create '$argc_path'?"
mkdir -p "$(dirname "$argc_path")"
printf "%s" "$argc_contents" > "$argc_path"
echo "File created: $argc_path" >> "$LLM_OUTPUT"
}
# @cmd Verify the project builds successfully
verify_build() {
local project_dir
project_dir=$(_project_dir)
local project_info
project_info=$(detect_project "${project_dir}")
local build_cmd
build_cmd=$(echo "${project_info}" | jq -r '.check // .build')
if [[ -z "${build_cmd}" ]] || [[ "${build_cmd}" == "null" ]]; then
warn "No build command detected" >> "$LLM_OUTPUT"
return 0
fi
info "Running: ${build_cmd}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local output exit_code=0
output=$(cd "${project_dir}" && eval "${build_cmd}" 2>&1) || exit_code=$?
echo "${output}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
if [[ ${exit_code} -eq 0 ]]; then
green "BUILD SUCCESS" >> "$LLM_OUTPUT"
return 0
else
error "BUILD FAILED (exit code: ${exit_code})" >> "$LLM_OUTPUT"
return 1
fi
}
# @cmd Run project tests
run_tests() {
local project_dir
project_dir=$(_project_dir)
local project_info
project_info=$(detect_project "${project_dir}")
local test_cmd
test_cmd=$(echo "${project_info}" | jq -r '.test')
if [[ -z "${test_cmd}" ]] || [[ "${test_cmd}" == "null" ]]; then
warn "No test command detected" >> "$LLM_OUTPUT"
return 0
fi
info "Running: ${test_cmd}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local output exit_code=0
output=$(cd "${project_dir}" && eval "${test_cmd}" 2>&1) || exit_code=$?
echo "${output}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
if [[ ${exit_code} -eq 0 ]]; then
green "TESTS PASSED" >> "$LLM_OUTPUT"
return 0
else
error "TESTS FAILED (exit code: ${exit_code})" >> "$LLM_OUTPUT"
return 1
fi
}
# @cmd Get project structure for context
get_project_structure() {
local project_dir
project_dir=$(_project_dir)
local project_info
project_info=$(detect_project "${project_dir}")
{
info "Project: $(echo "${project_info}" | jq -r '.type')"
echo ""
get_tree "${project_dir}" 2
} >> "$LLM_OUTPUT"
}
-274
View File
@@ -1,274 +0,0 @@
# deep-research
A deep web research agent, built as a Coyote graph agent. It plans an
investigation, decomposes it into sub-questions researched in
parallel, grounds the work in a local knowledge corpus, vets the
credibility of cited sources, runs a reflexion self-critique loop to
revise weak findings, delegates the final write-up to a focused
sub-agent, checks that the cited sources are reachable, and gates the
result behind human approval.
Unlike a regular agent (which takes a goal and improvises the steps),
this agent runs a fixed graph: every request goes through the same
`plan -> parallel research -> vet -> critique -> synthesize -> verify -> approve`
pipeline.
This agent is also the **canonical reference for the Coyote graph
system**: it exercises every node type (`script`, `llm`, `rag`, `map`,
`agent`, `input`, `approval`, `end`) and both static fan-out and
dynamic `map` fan-out. If you are learning how to build a graph
agent, this is the file to read alongside the
[Graph-Agents wiki](https://github.com/Dark-Alex-17/coyote/wiki/Graph-Agents).
## Workflow
17 nodes. `->` is the static route; a script node can also route
dynamically via `_next`. The `▶▶` line is a parallel super-step —
those branches run concurrently:
```
parse_request (script) -> bootstrap_research (or -> ask_topic if no topic)
ask_topic (input) -> bootstrap_research
bootstrap_research (script) -> [plan, knowledge_lookup] ▶▶ parallel
plan (llm + output_schema) -> research_each_question
knowledge_lookup (rag) -> research_each_question
research_each_question (map) -> combine_findings (spawns one branch per question)
└─ research_one_question (llm) (atomic; runs N×, joins at map)
combine_findings (script) -> vet_sources
vet_sources (llm + custom tool) -> critique
critique (llm) -> reflexion_gate
reflexion_gate (script) -> synthesize (or -> research_each_question: reflexion loop)
synthesize (agent: report-writer) -> verify_sources
verify_sources (script) -> approve
approve (approval) -> end_accepted ("accept")
-> end_rejected ("reject")
-> incorporate_feedback (any free-form answer)
incorporate_feedback (script) -> research_each_question (the human-feedback loop)
```
### Node-type breakdown
| Type | Nodes |
|-----------------------------|-----------------------------------------------------------------------------------------------------------------------|
| `script` (Python) | `parse_request`, `bootstrap_research`, `combine_findings`, `reflexion_gate`, `verify_sources`, `incorporate_feedback` |
| `llm` (tools: `[]`) | `plan`, `critique` |
| `llm` (with tool whitelist) | `research_one_question`, `vet_sources` |
| `rag` | `knowledge_lookup` — local corpus retrieval |
| `map` | `research_each_question` — dynamic fan-out per sub-question |
| `agent` | `synthesize` — spawns the `report-writer` sub-agent |
| `input` | `ask_topic` |
| `approval` | `approve` |
| `end` | `end_accepted`, `end_rejected` |
## Parallel execution
The graph has two parallel super-steps where Coyote's BSP scheduler runs
branches concurrently.
**1. Context loading (`plan` ‖ `knowledge_lookup`)** — after
`bootstrap_research`, the LLM planner (which decomposes the topic into
sub-questions) and the RAG retrieval over the local `knowledge/`
corpus run side by side. They write disjoint state keys (`plan` writes
`research_plan` and `questions`; `knowledge_lookup` writes
`local_context` and `local_sources`) so no reducer is needed.
**2. Per-question research (`research_each_question` map)** — the
plan emits a `questions` array (3-5 entries, enforced by its
`output_schema`). The `map` node spawns one parallel branch per
question (`max_concurrency: 3`). Each branch is an isolated
`research_one_question` LLM invocation with web tools, instructed to
investigate exactly its assigned question. Outputs collect into
`question_findings` in input order, then `combine_findings` joins
them into a single `findings` Markdown document for downstream nodes.
`settings.max_concurrency: 4` is the graph-wide cap; the per-`map`
override (`max_concurrency: 3` on `research_each_question`) is
deliberately lower to leave headroom for the planner's tool calls
running alongside RAG.
## Local knowledge corpus
`knowledge_lookup` is a `rag` node — it runs hybrid (vector + keyword)
retrieval over every file in `knowledge/`. The directory ships with a
small `research-style-notes.md` so the RAG node has something to
retrieve against on a clean install; drop your own Markdown notes,
PDFs, or text files into `knowledge/` to bias the research toward
your local context.
The knowledge base is built once, at agent-load time, into
`~/.config/coyote/agents/deep-research/knowledge_lookup.yaml`. Because
the node fully specifies its build config (`embedding_model`,
`chunk_size`, `chunk_overlap`), the build is non-interactive. Delete
that cached file after adding or changing knowledge to force a
rebuild.
## Sub-agent: report-writer
The `synthesize` node is an `agent` node that spawns the
`report-writer` sub-agent (`assets/agents/report-writer/`). This is
the agent-as-tool pattern: the orchestrating graph delegates the
writing phase to a focused sub-agent dedicated to coherent prose,
while the research phase uses different (typically cheaper) LLM nodes
for fast-and-many-question investigation.
The `report-writer` sub-agent has no tools — it cannot access the
web, cannot search, and cannot invent facts. It reads only the
findings it is given and produces a final Markdown report preserving
every inline citation. See `assets/agents/report-writer/README.md`
for details.
## Tools and tool scoping
This agent demonstrates Coyote's three tool sources and how an `llm`
node's `tools:` whitelist scopes them per node.
The agent's full tool universe, declared in `graph.yaml`:
- **Global tools** (`global_tools`): `web_search_coyote`,
`fetch_url_via_curl`, `search_arxiv` - Coyote's built-in tool scripts.
- **MCP server** (`mcp_servers`): `ddg-search` - a DuckDuckGo web
search MCP server. Referenced in a whitelist as `mcp:ddg-search`.
- **Custom agent tool** (`tools.sh`): `classify_source` - a
deterministic source-credibility classifier shipped with this agent.
No node receives all of these. Each `llm` node's `tools:` whitelist
narrows the universe to exactly what that step needs:
| Node | `tools:` whitelist | Draws from |
|-------------------------|-----------------------------------------------------------------------------|--------------------------|
| `plan`, `critique` | `[]` | nothing - pure reasoning |
| `research_one_question` | `web_search_coyote`, `fetch_url_via_curl`, `search_arxiv`, `mcp:ddg-search` | global tools + MCP |
| `vet_sources` | `classify_source` | the custom tool only |
`research_one_question` (each parallel branch of the map) can search
and fetch but cannot classify sources; `vet_sources` can classify
sources but cannot touch the web. That separation is the point of the
`tools:` whitelist: a node gets only the tools its job calls for,
never the agent's full set.
The `classify_source` custom tool (`tools.sh`) takes a URL and returns
a credibility tier (government, academic, preprint, organization,
unverified) derived from the host and top-level domain. It is
deterministic - exactly the kind of logic a tool should own rather than
the LLM guessing.
Web search may require API-key configuration; see the
[Tools](https://github.com/Dark-Alex-17/coyote/wiki/Tools) docs.
`fetch_url_via_curl`, `search_arxiv`, and `classify_source` work
without a key.
## Setup
`research_one_question` (each parallel branch of the `map`) uses the
`ddg-search` MCP server via `mcp:ddg-search`. It is one of Coyote's
default MCP servers; make sure it is registered in
`~/.config/coyote/mcp.json` (run `coyote --install mcp_config` to restore
the default template if it is missing). If `ddg-search` is unavailable,
the branches still have their global web-search tools to fall back on.
The `synthesize` node spawns the `report-writer` sub-agent. Both
agents ship with `coyote agents install`; if you install one manually,
install both so the agent reference resolves.
## Reflexion
The agent has two loops, both built with script nodes that route via
`_next`. The engine allows back-edges at runtime; the validator only
rejects cycles built from static `next` / `routes` edges, so script
`_next` loops are always allowed.
**Automated reflexion loop.** After the parallel research map and
`vet_sources`, the `critique` node reviews the merged findings
against the research plan and the source credibility assessment, and
emits `VERDICT: PASS` or `VERDICT: REVISE` with specific feedback.
`reflexion_gate.py` then:
- `PASS` -> continue to `synthesize`.
- `REVISE`, budget remaining -> loop back to `research_each_question`,
with the critique injected as `research_feedback` so every parallel
branch sees it on the retry.
- `REVISE`, budget spent -> continue to `synthesize` anyway (the human
approval step is the final backstop).
The budget is `MAX_REFLEXION_REVISIONS` in `reflexion_gate.py`
(default 2, so the research map runs at most 3 times per pass).
**Human-feedback loop.** At `approve` the user answers `accept`,
`reject`, or types their own feedback. A free-form answer routes via
the approval node's `on_other` to `incorporate_feedback.py`, which
folds that text into `research_feedback` and loops back to
`research_each_question` for another parallel pass.
`settings.max_loop_iterations` (40) is the engine's infinite-loop
backstop: it caps the total visits to any single node.
## Running
```sh
coyote agents install # ships deep-research
coyote -a deep-research "How does HTTP/3 differ from HTTP/2?"
coyote -a deep-research "Recent advances in solid-state batteries"
coyote -a deep-research # no prompt -> triggers ask_topic
```
## Anti-hallucination
- `research_one_question` (each map branch) is instructed to back
every claim with a real retrieved source and never to fabricate
URLs, titles, or DOIs.
- `vet_sources` classifies every cited source so weak sources are
visible to the critique step.
- `critique` independently reviews the merged findings and sends weak
or uncited work back for another parallel research pass.
- `synthesize` (the `report-writer` sub-agent) is grounded: it may use
only the gathered findings and must keep each claim's inline source.
It has no tools and cannot browse the web.
- `verify_sources` probes every cited URL / DOI with an HTTP HEAD
request and reports which are unreachable, so the human reviewer
sees broken citations before approving.
## Customizing
- **Loop budget.** `MAX_REFLEXION_REVISIONS` in `reflexion_gate.py`.
- **Map concurrency.** The `research_each_question` node's
`max_concurrency: 3` caps simultaneous web-research branches.
Raise to investigate more questions in parallel; lower to be gentle
on rate-limited providers.
- **Per-node model.** Add `model: anthropic:...` to any `llm` node.
Cheap models work well for `plan` / `critique` / `vet_sources`; the
heavy intelligence is needed in `research_one_question` and the
`report-writer` sub-agent.
- **Tool scope.** Narrow the `research_one_question` node's `tools:`
list to constrain where each branch looks (for example, drop
`web_search_coyote` and `mcp:ddg-search` to force arXiv-only
research).
- **Local knowledge.** Drop files into `knowledge/` to bias every
research branch toward your local context (see the *Local
knowledge corpus* section above).
- **Different writer.** Replace `agent: report-writer` on the
`synthesize` node with the name of any other agent. The
orchestrator does not care what kind of agent the writer is.
- **Skip approval.** Point both `approve` routes at `end_accepted`,
or wire `verify_sources` straight to an `end` node.
## Files
```
assets/agents/deep-research/
graph.yaml - agent config + 17-node workflow
tools.sh - classify_source custom tool
README.md - this file
knowledge/
README.md - corpus-format notes
research-style-notes.md - starter knowledge file (replace with your notes)
scripts/
parse_request.py - _next: bootstrap_research, or ask_topic if no topic
bootstrap_research.py - fan-out source: next [plan, knowledge_lookup]
combine_findings.py - joins map output (question_findings) into findings
reflexion_gate.py - _next: research_each_question (revise) or synthesize
verify_sources.py - HTTP HEAD on cited URLs / DOIs
incorporate_feedback.py - _next: research_each_question, with user feedback
```
See also `assets/agents/report-writer/` — the sub-agent the
`synthesize` node spawns.
-291
View File
@@ -1,291 +0,0 @@
name: deep-research
description: |
Deep web research workflow. Plans an investigation, decomposes it
into sub-questions researched in parallel, grounds the work in a
local knowledge corpus, vets the credibility of cited sources, runs
a reflexion self-critique loop to revise weak or incomplete findings,
delegates the final write-up to a focused sub-agent, checks that the
cited sources are reachable, and gates the result behind human
approval. A reviewer's free-form feedback at the approval step feeds
back into another research pass.
This is the canonical Coyote graph-agent reference: it exercises every
node type (script, llm, rag, map, agent, input, approval, end) and
both static fan-out and dynamic map fan-out.
version: "1.0"
global_tools:
- web_search_coyote.sh
- fetch_url_via_curl.sh
- search_arxiv.sh
mcp_servers:
- ddg-search
conversation_starters:
- "How does HTTP/3 differ from HTTP/2?"
- "Summarize recent advances in solid-state battery chemistry"
settings:
max_loop_iterations: 40
log_state_snapshots: false
validate_before_run: true
max_concurrency: 4
initial_state:
research_feedback: ""
research_attempts: 0
local_context: ""
local_sources: ""
start: parse_request
nodes:
parse_request:
id: parse_request
type: script
script: scripts/parse_request.py
next: bootstrap_research
ask_topic:
id: ask_topic
type: input
question: "What would you like me to research?"
validation: "len(input) > 0"
state_updates:
topic: "{{input}}"
next: bootstrap_research
bootstrap_research:
id: bootstrap_research
type: script
script: scripts/bootstrap_research.py
next: [plan, knowledge_lookup]
plan:
id: plan
type: llm
instructions: |
You are a research planner. Given a topic, produce a focused
research plan and decompose it into 3-5 specific sub-questions
that can each be researched independently in parallel.
The plan is a short narrative naming the key questions and the
kinds of sources that would be authoritative. The sub-questions
are precise, self-contained queries (each one is sent on its own
to a separate research worker, so they must be answerable
without each other's context).
prompt: "Research topic: {{topic}}"
tools: []
output_schema:
type: object
properties:
research_plan:
type: string
description: A short plan narrative.
questions:
type: array
items: { type: string }
minItems: 1
maxItems: 6
description: 3-5 specific, self-contained sub-questions.
required: [research_plan, questions]
next: research_each_question
knowledge_lookup:
id: knowledge_lookup
type: rag
documents:
- ./knowledge/
query: "{{topic}}"
top_k: 6
chunk_size: 1000
chunk_overlap: 100
state_updates:
local_context: "{{output.context}}"
local_sources: "{{output.sources}}"
next: research_each_question
research_each_question:
id: research_each_question
type: map
over: "{{questions}}"
as: question
branch: research_one_question
collect_into: question_findings
max_concurrency: 3
next: combine_findings
research_one_question:
id: research_one_question
type: llm
instructions: |
You are a web research assistant. Investigate the SINGLE question
given to you using your tools: search the web, fetch and read
pages, and search arXiv for academic sources.
Rules:
- Every factual claim must be backed by a real source you
actually retrieved. Never fabricate URLs, page titles,
authors, or DOIs.
- Prefer primary and authoritative sources over aggregators.
- Where sources disagree, report the disagreement rather than
papering over it.
- Put the URL (or DOI) inline next to each claim it supports.
Return organized findings in plain text. Do not include
meta-commentary about the process.
prompt: |
Research question: {{question}}
Local context that may help:
{{local_context}}
{{research_feedback}}
tools:
- web_search_coyote
- fetch_url_via_curl
- search_arxiv
- mcp:ddg-search
max_iterations: 10
max_attempts: 2
temperature: 0.1
combine_findings:
id: combine_findings
type: script
script: scripts/combine_findings.py
next: vet_sources
vet_sources:
id: vet_sources
type: llm
instructions: |
You assess the credibility of the sources cited in a set of
research findings. For every distinct source URL in the findings,
call the `classify_source` tool to get its credibility tier. Then
summarize: which claims rest on HIGH-credibility sources, and
which rest on PREPRINT or UNVERIFIED sources and so need
corroboration. Do NOT do any new research -- assess only what is
already cited.
prompt: |
Findings to assess:
{{findings}}
tools:
- classify_source
max_iterations: 15
state_updates:
source_assessment: "{{output}}"
next: critique
critique:
id: critique
type: llm
instructions: |
You are a meticulous research reviewer. Judge whether the
findings below are good enough to synthesize a complete,
well-supported report that answers the research plan.
Mark the findings REVISE if ANY of these hold:
- A research-plan question is unanswered or only weakly
addressed.
- A factual claim has no source, or cites a source that looks
fabricated.
- The findings lean on a single source where corroboration is
needed.
- A key claim rests only on a PREPRINT or UNVERIFIED source,
per the source credibility assessment below.
- An obvious counter-perspective or recent development is
missing.
Otherwise mark them PASS.
Respond in EXACTLY this format, nothing else:
VERDICT: <PASS or REVISE>
FEEDBACK: <if REVISE, be specific and actionable -- name the gaps
and what kind of source would close them; if PASS, write "none">
prompt: |
Research plan:
{{research_plan}}
Findings under review:
{{findings}}
Source credibility assessment:
{{source_assessment}}
tools: []
state_updates:
critique: "{{output}}"
next: reflexion_gate
reflexion_gate:
id: reflexion_gate
type: script
script: scripts/reflexion_gate.py
next: synthesize
synthesize:
id: synthesize
type: agent
agent: report-writer
prompt: |
Research topic: {{topic}}
Findings (organized by sub-question, with inline citations):
{{findings}}
Source credibility assessment:
{{source_assessment}}
Produce the final report following your instructions.
timeout: 300
state_updates:
report: "{{output}}"
next: verify_sources
verify_sources:
id: verify_sources
type: script
script: scripts/verify_sources.py
next: approve
approve:
id: approve
type: approval
question: |
Research report on: {{topic}}
{{report}}
----
{{source_check}}
----
Accept this report? Pick "accept" or "reject", or type specific
feedback to send the research back for another pass.
options:
- "accept"
- "reject"
routes:
"accept": end_accepted
"reject": end_rejected
on_other: incorporate_feedback
state_updates:
decision: "{{choice}}"
incorporate_feedback:
id: incorporate_feedback
type: script
script: scripts/incorporate_feedback.py
end_accepted:
id: end_accepted
type: end
output: "{{report}}"
end_rejected:
id: end_rejected
type: end
output: "Research on '{{topic}}' was rejected and discarded."
@@ -1,23 +0,0 @@
# Local knowledge corpus for deep-research
The `knowledge_lookup` node in `graph.yaml` is a `rag` node that runs
hybrid (vector + keyword) retrieval over every file in this directory.
Drop your own notes, papers (PDFs), Markdown docs, or text files here
and they will be indexed into a per-agent knowledge base on first run.
Coyote supports common file types out of the box: `.md`, `.txt`, `.pdf`,
`.html`, and others. Subdirectories are walked recursively.
A small starter file (`research-style-notes.md`) ships so the RAG
node has something non-empty to retrieve against on a clean install.
Replace or extend it with your own materials to bias the research
phase toward your local context.
To force the knowledge base to rebuild after you add or change files,
delete the cached index:
```sh
rm ~/.config/coyote/agents/deep-research/knowledge_lookup.yaml
```
The next run will rebuild from the current contents of this directory.
@@ -1,49 +0,0 @@
# Research style notes
These are general principles the `deep-research` agent should keep in
mind regardless of topic. Replace this file with your own notes if you
want to bias retrieval toward your local context.
## What "good research" means here
- **Every factual claim cites a source you actually retrieved.** Never
fabricate URLs, page titles, authors, or DOIs.
- **Primary sources beat aggregators.** Prefer the original paper, the
RFC, the standards body, or the manufacturer over a blog summarizing
them.
- **Corroboration matters where stakes are high.** If a single source
makes a strong claim, look for a second independent source before
taking it as established.
- **Disagreement is information, not noise.** If two credible sources
disagree, report the disagreement and the reasoning on each side.
- **Old does not mean wrong.** A 2014 RFC is still authoritative if no
newer one has obsoleted it; check before assuming a source is stale.
## Source-tier heuristics
The `vet_sources` node uses these rough tiers to weigh credibility.
The custom tool `classify_source` (see `tools.sh`) implements this
deterministically by hostname / TLD.
- **HIGH:** government domains (`.gov`, `.mil`), academic institutions
(`.edu`, university subdomains), peer-reviewed journals, standards
bodies (IETF/RFCs, W3C, ISO, IEEE, NIST), and primary documents from
the entities being researched (e.g. a vendor's official spec page).
- **PREPRINT:** arXiv, bioRxiv, medRxiv, SSRN. Useful but not yet
peer-reviewed; treat numeric claims with extra caution.
- **ORGANIZATION:** established nonprofits, standards-adjacent groups,
industry consortia. Reliable for their stated mission but may have a
perspective.
- **UNVERIFIED:** general web pages, blogs, news aggregators, social
media. Useful for leads but should not be the only source for a
factual claim.
## Common pitfalls to flag in critique
- A claim cited only to a PREPRINT or UNVERIFIED source on a numeric
or contested point.
- A research-plan question that the findings address only obliquely.
- "Findings" that paraphrase a single source three times rather than
triangulating.
- Citation collisions where two sources are listed but turn out to
be the same study reported via different aggregators.
@@ -1,18 +0,0 @@
#!/usr/bin/env python3
"""Fan-out source for context loading.
Has no logic of its own. Exists so the static `next: [plan, knowledge_lookup]`
list on this node fans out into two parallel branches (the LLM planner and
the RAG knowledge lookup) as a single super-step. The validator requires
declared parallel-branch script outputs, so we emit an empty JSON object
explicitly here.
"""
import json
def main():
print(json.dumps({}))
if __name__ == "__main__":
main()
@@ -1,39 +0,0 @@
#!/usr/bin/env python3
"""Join the per-question map outputs into a single `findings` string.
The `research_each_question` map writes `question_findings` (an array,
one entry per sub-question, in input order). Downstream nodes
(`vet_sources`, `critique`, `synthesize`) read `{{findings}}` as a
single block, so this script renders the array as a Markdown document
with one section per question.
"""
import json
import os
def load_state():
path = os.environ.get("GRAPH_STATE_FILE")
if path:
with open(path) as f:
return json.load(f)
return json.loads(os.environ.get("GRAPH_STATE", "{}"))
def main():
state = load_state()
questions = state.get("questions") or []
per_question = state.get("question_findings") or []
sections = []
for idx, q in enumerate(questions):
body = per_question[idx] if idx < len(per_question) else ""
if isinstance(body, dict) or isinstance(body, list):
body = json.dumps(body, indent=2)
sections.append(f"## {q}\n\n{body}")
findings = "\n\n".join(sections) if sections else "No findings gathered."
print(json.dumps({"findings": findings}))
if __name__ == "__main__":
main()
@@ -1,41 +0,0 @@
#!/usr/bin/env python3
"""Fold a reviewer's free-form feedback back into the research loop.
Runs when the user answers the approval step with their own text
instead of "accept" or "reject". That text (saved by the approval node
as `decision`) becomes `research_feedback`, and the graph loops back to
`research_each_question` for another informed pass (each sub-question is
re-researched in parallel with the new feedback in context). The
reflexion counter is reset so the user-driven pass gets a fresh revision
budget.
Routing (`_next`): always research_each_question.
"""
import json
import os
def load_state():
path = os.environ.get("GRAPH_STATE_FILE")
if path:
with open(path) as f:
return json.load(f)
return json.loads(os.environ.get("GRAPH_STATE", "{}"))
def main():
state = load_state()
feedback = (state.get("decision") or "").strip()
output = {
"_next": "research_each_question",
"research_attempts": 0,
"research_feedback": (
"The user reviewed the report and asked for changes. Treat "
"this as the top priority for the next pass:\n\n" + feedback
),
}
print(json.dumps(output))
if __name__ == "__main__":
main()
@@ -1,35 +0,0 @@
#!/usr/bin/env python3
"""Entry router for deep-research.
Reads the caller's prompt from state. If it contains a usable research
topic, stores it as `topic` and falls through to the static `next`
(plan). If the prompt is empty, routes to `ask_topic` so the user can
supply one interactively.
Routing (`_next`):
- prompt present -> (no _next; static next: plan)
- prompt empty -> ask_topic
"""
import json
import os
def load_state():
path = os.environ.get("GRAPH_STATE_FILE")
if path:
with open(path) as f:
return json.load(f)
return json.loads(os.environ.get("GRAPH_STATE", "{}"))
def main():
state = load_state()
prompt = (state.get("initial_prompt") or "").strip()
if prompt:
print(json.dumps({"topic": prompt}))
else:
print(json.dumps({"_next": "ask_topic"}))
if __name__ == "__main__":
main()
@@ -1,76 +0,0 @@
#!/usr/bin/env python3
"""Reflexion gate for deep-research.
Runs after `critique` has reviewed the current research findings. If the
critique's verdict is REVISE and the reflexion budget is not spent,
loops back to `research` with the critique attached as
`research_feedback`, so the retry is informed rather than a blind
re-run. Otherwise it proceeds to `synthesize`.
Routing (`_next`):
- verdict PASS -> synthesize
- verdict REVISE, budget remaining -> research_each_question (+ research_feedback)
- verdict REVISE, budget spent -> synthesize
Reflexion is a best-effort quality booster, not a hard gate: once the
budget is spent the workflow proceeds anyway, and the human approval
step is the final backstop.
"""
import json
import os
import re
# Automated revision passes allowed. `research` runs at most
# MAX_REFLEXION_REVISIONS + 1 times per user pass. Bump to allow more.
MAX_REFLEXION_REVISIONS = 2
def load_state():
path = os.environ.get("GRAPH_STATE_FILE")
if path:
with open(path) as f:
return json.load(f)
return json.loads(os.environ.get("GRAPH_STATE", "{}"))
def as_int(value, default=0):
try:
return int(value)
except (TypeError, ValueError):
return default
def parse_verdict(critique):
"""Pull PASS/REVISE from the critique's `VERDICT:` line. Defaults to
PASS when no verdict line is found, so a malformed critique lets the
workflow proceed instead of burning the whole revision budget."""
match = re.search(r"VERDICT:\s*([A-Za-z]+)", critique, re.IGNORECASE)
if not match:
return "PASS"
return match.group(1).upper()
def main():
state = load_state()
critique = state.get("critique") or ""
verdict = parse_verdict(critique)
attempts = as_int(state.get("research_attempts"))
if verdict == "REVISE" and attempts < MAX_REFLEXION_REVISIONS:
feedback = (
"A reviewer judged the previous research pass incomplete. "
"Address every point in the critique below:\n\n" + critique
)
output = {
"_next": "research_each_question",
"research_attempts": attempts + 1,
"research_feedback": feedback,
}
else:
output = {"_next": "synthesize"}
print(json.dumps(output))
if __name__ == "__main__":
main()
@@ -1,69 +0,0 @@
#!/usr/bin/env python3
"""Check that the sources cited in the research report are reachable.
Scans the final report for URLs and DOIs, probes each with a HEAD
request, and writes a `source_check` summary into state so the human
reviewer sees broken citations at the approval step.
Times out per request so a slow source cannot stall the graph.
"""
import json
import os
import re
import urllib.error
import urllib.request
DOI_RE = re.compile(r"\b(10\.\d{4,9}/[-._;()/:A-Z0-9]+)", re.IGNORECASE)
URL_RE = re.compile(r"https?://[^\s)\]\}\"'>]+")
def load_state():
path = os.environ.get("GRAPH_STATE_FILE")
if path:
with open(path) as f:
return json.load(f)
return json.loads(os.environ.get("GRAPH_STATE", "{}"))
def reachable(url, timeout=5.0):
req = urllib.request.Request(url, method="HEAD")
try:
with urllib.request.urlopen(req, timeout=timeout) as resp:
return 200 <= resp.status < 400
except urllib.error.HTTPError as e:
return 200 <= e.code < 400
except Exception:
return False
def main():
state = load_state()
report = state.get("report") or ""
urls = sorted({u.rstrip(".,;)") for u in URL_RE.findall(report)})
dois = sorted(set(DOI_RE.findall(report)))
results = []
for url in urls:
ok = reachable(url)
results.append(f" {'OK' if ok else 'UNREACHABLE'} {url}")
for doi in dois:
url = f"https://doi.org/{doi}"
if url in urls:
continue
ok = reachable(url)
results.append(f" {'OK' if ok else 'UNREACHABLE'} DOI {doi} ({url})")
if not results:
summary = "No web sources were cited in the report."
else:
summary = (
f"Source reachability ({len(results)} checked):\n"
+ "\n".join(results)
)
print(json.dumps({"source_check": summary}))
if __name__ == "__main__":
main()
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@@ -1,39 +0,0 @@
#!/usr/bin/env bash
set -e
# @env LLM_OUTPUT=/dev/stdout The output path
# @cmd Classify the credibility tier of a web source from its URL.
# A deterministic check based on the host and top-level domain. Use it
# to weigh how much trust to place in a source before relying on it.
# @option --url! The full source URL to classify
classify_source() {
# shellcheck disable=SC2154
local url="$argc_url"
local host="${url#*://}"
host="${host%%/*}"
host="${host##*@}"
host="${host%%:*}"
host="$(printf '%s' "$host" | tr '[:upper:]' '[:lower:]')"
local tier
case "$host" in
'')
tier="UNKNOWN - no host could be parsed from the URL" ;;
*.gov | *.gov.* | *.mil)
tier="HIGH - government source" ;;
*.edu | *.edu.* | *.ac.*)
tier="HIGH - academic institution" ;;
arxiv.org | *.arxiv.org | biorxiv.org | *.biorxiv.org | medrxiv.org | *.medrxiv.org | ssrn.com | *.ssrn.com)
tier="PREPRINT - not yet peer reviewed, corroborate before citing" ;;
wikipedia.org | *.wikipedia.org)
tier="TERTIARY - encyclopedia, good for orientation not citation" ;;
*.org | *.org.*)
tier="MEDIUM - organization site, check for institutional bias" ;;
*)
tier="UNVERIFIED - general web source, corroborate before citing" ;;
esac
printf '%s: %s\n' "${host:-<none>}" "$tier" >> "$LLM_OUTPUT"
}
+1 -1
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@@ -2,6 +2,6 @@
This agent serves as a demo to guide agent development and showcase various agent capabilities.
To enable tools, Coyote will look for the first `tools.py` or `tools.sh` file it finds in this directory.
To enable tools, Loki will look for the first `tools.py` or `tools.sh` file it finds in this directory.
The base configuration using `tools.py`. To switch to using `tools.sh`, rename or remove `tools.py`.
-37
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@@ -1,37 +0,0 @@
# Explore
An AI agent specialized in exploring codebases, finding patterns, and understanding project structures.
This agent is designed to be delegated to by the **[Sisyphus](../sisyphus/README.md)** agent to gather information and context. Sisyphus
acts as the coordinator/architect, while Explore handles the research and discovery phase.
It can also be used as a standalone tool for understanding codebases and finding specific information.
## Features
- 🔍 Deep codebase exploration and pattern matching
- 📂 File system navigation and content analysis
- 🧠 Context gathering for complex tasks
- 🛡️ Read-only operations for safe investigation
## Pro-Tip: Use an IDE MCP Server for Improved Performance
Many modern IDEs now include MCP servers that let LLMs perform operations within the IDE itself and use IDE tools. Using
an IDE's MCP server dramatically improves the performance of coding agents. So if you have an IDE, try adding that MCP
server to your config (see the [MCP Server docs](https://github.com/Dark-Alex-17/loki/wiki/MCP-Servers) to see how to configure
them), and modify the agent definition to look like this:
```yaml
# ...
mcp_servers:
- jetbrains # The name of your configured IDE MCP server
global_tools:
- fs_read.sh
- fs_grep.sh
- fs_glob.sh
- fs_ls.sh
- web_search_coyote.sh
# ...
```
-116
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@@ -1,116 +0,0 @@
name: explore
description: Fast codebase exploration agent - finds patterns, structures, and relevant files. Designed to be fanned out 2-5 in parallel by orchestrators.
version: 3.0.0
skills_enabled: true
enabled_skills:
- ai-slop-remover
variables:
- name: project_dir
description: Project directory to explore
default: '.'
mcp_servers:
- ddg-search
global_tools:
- fs_read.sh
- fs_cat.sh
- fs_grep.sh
- fs_glob.sh
- fs_ls.sh
instructions: |
You are a codebase explorer. Your job: Search, find, report. Nothing else.
## Step 0: Load your skills
At the start of every exploration, call `skill__load` for `ai-slop-remover`. Your findings go directly into the orchestrator's synthesis, so concise, slop-free output is the contract. Apply the skill's standards to your final findings block:
- No filler ("It's important to note that…", "Let me explain…"). Just the finding.
- No flattery, no padding, no status updates about your process.
- No multi-paragraph commentary — bullet points with code snippets are enough.
## You may be one of many parallel explorers
Orchestrators (like Sisyphus) often fan out 2-5 explore agents at once, each covering a different angle of the same question. Assume you are ONE narrow slice of a larger investigation. Stay strictly within YOUR slice as defined by the prompt — don't broaden scope to cover what other parallel explorers might be handling.
If the prompt says "find auth middleware", you find auth middleware. You do NOT also tour the routing layer, the error system, and the database connection pool. Narrow scope is the contract.
## Investigation methodology
Before searching, build a quick mental model. Then narrow in. Then read.
1. **Frame the question.** What kind of artifact am I looking for? Symbols (struct/class/function)? File patterns? Configuration? Implementation details? Tests? Different artifact kinds use different tools.
2. **Find first, read second.** Never `fs_read` a file without knowing why you're reading it.
3. **Build a directory mental model with `fs_ls` and `fs_glob`** — `fs_ls src/` to see what's there; `fs_glob '**/*.rs' src/` to see which files exist by name.
4. **Locate symbols with `fs_grep`** — for finding where things live across the codebase. `fs_grep --pattern "fn handle_request" --include "*.rs"` is faster than reading files.
5. **Read targeted sections with `fs_read --offset/--limit`** — `fs_read --path "src/main.rs" --offset 50 --limit 30` reads lines 50-79 only. `fs_read` adds line numbers but TRUNCATES long lines (over 2000 chars) and caps output at 2000 lines by default.
6. **Use `fs_cat` only when you need the full untruncated file** — rare in exploration. If you reach for `fs_cat`, ask whether `fs_grep` + targeted `fs_read` would answer your question with less context spend.
7. **Never read entire large files** — for files 500+ lines, read the relevant section only.
## Available actions
- `fs_grep --pattern "struct User" --include "*.rs"` — find content across files in a directory tree
- `fs_grep --pattern "TODO" --path "src/main.rs"` — find content within a single file (--include is ignored in this mode)
- `fs_glob --pattern "*.rs" --path src/` — find files by name pattern
- `fs_read --path "src/main.rs"` — read a TRUNCATED view with line numbers (default 2000 lines, lines over 2000 chars cut off)
- `fs_read --path "src/main.rs" --offset 100 --limit 50` — read lines 100-149 only (line numbers; truncation rules still apply)
- `fs_cat --path "src/main.rs"` — read the FULL untruncated file (no line numbers); use only when you actually need every line
- `fs_ls --path "src/"` — list directory contents
## When to use the web (ddg-search MCP)
Rarely. You are a CODEBASE explorer, not a web researcher. Use the web only when the codebase references an external library/framework whose documented behavior is the answer to the question (e.g., "how does Tokio's #[tokio::main] expand"), and the answer isn't in the local code. For internal questions ("how does OUR auth work"), grep the codebase — never the web.
## Output format
Always end your response with a structured findings block. Sisyphus reads this verbatim and may paste sections directly into delegation prompts for a coder agent, so the structure matters:
```
FINDINGS:
- [One-line concrete fact about what you found]
- [Another one-line fact]
- Relevant files: [list of paths, no commentary]
Code patterns (paste actual lines):
- From `path/to/file.ext` lines N-M:
<5-20 lines of actual code that show the pattern>
- From `path/to/other.ext` lines N-M:
<another snippet>
Open questions (only if any):
- [Anything you couldn't determine and the orchestrator should clarify or delegate elsewhere]
EXPLORE_COMPLETE
```
Pasting actual code lines (5-20 per pattern) lets the orchestrator hand snippets directly to a coder agent without re-exploration. That is the entire point of your existence in a parallel research phase. File paths alone make downstream delegation impossible — the coder would have to re-do your work.
## Rules
1. **Be fast.** Don't read every file, read representative ones.
2. **Stay in your slice.** Narrow scope is the contract.
3. **Be concise.** Report findings, not your process. Apply the `ai-slop-remover` skill to your output.
4. **Never modify files.** You are read-only.
5. **Limit reads.** Target around 5 file reads per exploration; go higher only when the question genuinely requires it.
6. **Paste code snippets.** File paths alone make downstream delegation impossible.
7. **Report what you didn't find.** If the prompt asked for X and X doesn't exist in your slice, say so explicitly — don't pad your findings with adjacent material to hide the gap.
## Context
- Project: {{project_dir}}
- CWD: {{__cwd__}}
## Available tools:
{{__tools__}}
conversation_starters:
- 'Find how authentication is implemented'
- 'What patterns are used for API endpoints'
- 'Show me the project structure'
-175
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@@ -1,175 +0,0 @@
#!/usr/bin/env bash
set -eo pipefail
# shellcheck disable=SC1090
source "$LLM_PROMPT_UTILS_FILE"
source "$LLM_ROOT_DIR/agents/.shared/utils.sh"
# @env LLM_OUTPUT=/dev/stdout
# @env LLM_AGENT_VAR_PROJECT_DIR=.
# @describe Explore agent tools for codebase search and analysis
_project_dir() {
local dir="${LLM_AGENT_VAR_PROJECT_DIR:-.}"
(cd "${dir}" 2>/dev/null && pwd) || echo "${dir}"
}
# Normalize a path to be relative to project root.
# Strips the project_dir prefix if the LLM passes an absolute path.
_normalize_path() {
local input_path="$1"
local project_dir
project_dir=$(_project_dir)
if [[ "${input_path}" == /* ]]; then
input_path="${input_path#"${project_dir}"/}"
fi
input_path="${input_path#./}"
echo "${input_path}"
}
# @cmd Get project structure and layout
get_structure() {
local project_dir
project_dir=$(_project_dir)
info "Project structure:" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local project_info
project_info=$(detect_project "${project_dir}")
{
echo "Type: $(echo "${project_info}" | jq -r '.type')"
echo ""
get_tree "${project_dir}" 3
} >> "$LLM_OUTPUT"
}
# @cmd Search for files by name pattern
# @option --pattern! File name pattern (e.g., "*.rs", "config*", "*test*")
search_files() {
# shellcheck disable=SC2154
local pattern="${argc_pattern}"
local project_dir
project_dir=$(_project_dir)
info "Files matching: ${pattern}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local results
results=$(_search_files "${pattern}" "${project_dir}")
if [[ -n "${results}" ]]; then
echo "${results}" >> "$LLM_OUTPUT"
else
warn "No files found" >> "$LLM_OUTPUT"
fi
}
# @cmd Search for content in files
# @option --pattern! Text or regex pattern to search for
# @option --file-type Filter by file extension (e.g., "rs", "py", "ts")
search_content() {
local pattern="${argc_pattern}"
local file_type="${argc_file_type:-}"
local project_dir
project_dir=$(_project_dir)
info "Searching: ${pattern}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local include_arg=""
if [[ -n "${file_type}" ]]; then
include_arg="--include=*.${file_type}"
fi
local results
# shellcheck disable=SC2086
results=$(grep -rn ${include_arg} "${pattern}" "${project_dir}" 2>/dev/null | \
grep -v '/target/' | \
grep -v '/node_modules/' | \
grep -v '/.git/' | \
grep -v '/dist/' | \
sed "s|^${project_dir}/||" | \
head -30) || true
if [[ -n "${results}" ]]; then
echo "${results}" >> "$LLM_OUTPUT"
else
warn "No matches found" >> "$LLM_OUTPUT"
fi
}
# @cmd Read a file's contents
# @option --path! Path to the file (relative to project root)
# @option --lines Maximum lines to read (default: 200)
read_file() {
local file_path
# shellcheck disable=SC2154
file_path=$(_normalize_path "${argc_path}")
local max_lines="${argc_lines:-200}"
local project_dir
project_dir=$(_project_dir)
local full_path="${project_dir}/${file_path}"
if [[ ! -f "${full_path}" ]]; then
error "File not found: ${file_path}" >> "$LLM_OUTPUT"
return 1
fi
{
info "File: ${file_path}"
echo ""
} >> "$LLM_OUTPUT"
head -n "${max_lines}" "${full_path}" >> "$LLM_OUTPUT"
local total_lines
total_lines=$(wc -l < "${full_path}")
if [[ "${total_lines}" -gt "${max_lines}" ]]; then
echo "" >> "$LLM_OUTPUT"
warn "... truncated (${total_lines} total lines)" >> "$LLM_OUTPUT"
fi
}
# @cmd Find similar files to a given file (for pattern matching)
# @option --path! Path to the reference file
find_similar() {
local file_path
file_path=$(_normalize_path "${argc_path}")
local project_dir
project_dir=$(_project_dir)
local ext="${file_path##*.}"
local dir
dir=$(dirname "${file_path}")
info "Files similar to: ${file_path}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local results
results=$(find "${project_dir}/${dir}" -maxdepth 1 -type f -name "*.${ext}" \
! -name "$(basename "${file_path}")" \
! -name "*test*" \
! -name "*spec*" \
2>/dev/null | sed "s|^${project_dir}/||" | head -5)
if [[ -n "${results}" ]]; then
echo "${results}" >> "$LLM_OUTPUT"
else
results=$(find "${project_dir}" -type f -name "*.${ext}" \
! -name "$(basename "${file_path}")" \
! -name "*test*" \
-not -path '*/target/*' \
2>/dev/null | sed "s|^${project_dir}/||" | head -5)
if [[ -n "${results}" ]]; then
echo "${results}" >> "$LLM_OUTPUT"
else
warn "No similar files found" >> "$LLM_OUTPUT"
fi
fi
}
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@@ -1,35 +0,0 @@
# File Reviewer
A specialized worker agent that reviews a single file's diff for bugs, style issues, and cross-cutting concerns.
This agent is designed to be spawned by the **[Code Reviewer](../code-reviewer/README.md)** agent. It focuses deeply on
one file while communicating with sibling agents to catch issues that span multiple files.
## Features
- 🔍 **Deep Analysis**: Focuses on bugs, logic errors, security issues, and style problems in a single file.
- 🗣️ **Teammate Communication**: Sends and receives alerts to/from sibling reviewers about interface or dependency
changes.
- 🎯 **Targeted Reading**: Reads only relevant context around changed lines to stay efficient.
- 🏷️ **Structured Findings**: Categorizes issues by severity (🔴 Critical, 🟡 Warning, 🟢 Suggestion, 💡 Nitpick).
## Pro-Tip: Use an IDE MCP Server for Improved Performance
Many modern IDEs now include MCP servers that let LLMs perform operations within the IDE itself and use IDE tools. Using
an IDE's MCP server dramatically improves the performance of coding agents. So if you have an IDE, try adding that MCP
server to your config (see the [MCP Server docs](../../../docs/function-calling/MCP-SERVERS.md) to see how to configure
them), and modify the agent definition to look like this:
```yaml
# ...
mcp_servers:
- jetbrains # The name of your configured IDE MCP server
global_tools:
- fs_read.sh
- fs_grep.sh
- fs_glob.sh
# ...
```
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@@ -1,124 +0,0 @@
name: file-reviewer
description: Reviews a single file's diff for bugs, style issues, and cross-cutting concerns
version: 2.0.0
skills_enabled: true
enabled_skills:
- code-review
- ai-slop-remover
variables:
- name: project_dir
description: Project directory for context
default: '.'
global_tools:
- fs_read.sh
- fs_grep.sh
- fs_glob.sh
- fs_cat.sh
- fs_ls.sh
instructions: |
You are a precise code reviewer. You review ONE file's diff and produce structured findings.
## Step 0: Load review skills
Before reading any code, call `skill__load` for `code-review` and `ai-slop-remover`. They carry your detailed review methodology — the categories to check (correctness, tests, clarity, coupling, footguns), the investigation workflow (how to use the fs tools to build context before reviewing), the slop checklist (useless comments, dishonest naming, defensive handling of impossible cases), and the standard for when to flag vs. skip.
Apply BOTH checklists in every review. Skill bodies are your source of truth for what to flag; this agent's instructions handle workflow and output shape.
## Your Mission
You receive a git diff for a single file. Your job:
1. Load the review skills (above).
2. Analyze the diff applying both skill checklists.
3. Read surrounding code for context using the skill's investigation workflow.
4. Check your inbox for cross-cutting alerts from sibling reviewers.
5. Send alerts to siblings if you spot cross-file issues.
6. Return structured findings in the format below.
## Input
You receive:
- The file path being reviewed
- The git diff for that file
- A sibling roster (other file-reviewers and which files they're reviewing)
## Cross-Cutting Alerts (Teammate Pattern)
After analyzing your file, check if changes might affect sibling files:
- **Interface changes**: If a function signature changed, alert siblings reviewing callers
- **Type changes**: If a type/struct changed, alert siblings reviewing files that use it
- **Import changes**: If exports changed, alert siblings reviewing importers
- **Config changes**: Alert all siblings if config format changed
To alert a sibling:
```
agent__send_message --to <sibling_agent_id> --message "ALERT: <description of cross-file concern>"
```
Check your inbox periodically for alerts from siblings:
```
agent__check_inbox
```
If you receive an alert, incorporate it into your findings under a "Cross-File Concerns" section.
## File Reading Limits
The `code-review` skill teaches the investigation workflow. Apply these per-review caps on top:
- **Max 5 fs_read calls per review.** Be deliberate about which files you read.
- **`fs_read` returns a TRUNCATED view** with line numbers (long lines cut at 2000 chars, output capped at 2000 lines by default). Use `--offset` and `--limit` (default 50 lines of context) to target specific sections. Never read entire large files.
- **Use `fs_cat` only when you genuinely need the full untruncated file** — for a diff review this should be rare; `fs_grep` + targeted `fs_read` usually answers the question with less context.
- **Focus on the diff.** Read surrounding code only when needed to evaluate the change; do not audit unrelated code in the same file.
## Output Format
Structure your response EXACTLY as:
```
## File: <file_path>
### Summary
<1-2 sentence summary of the changes>
### Findings
#### <finding_title>
- **Severity**: 🔴 CRITICAL | 🟡 WARNING | 🟢 SUGGESTION | 💡 NITPICK
- **Lines**: <start_line>-<end_line>
- **Description**: <clear explanation of the issue>
- **Suggestion**: <how to fix it>
#### <next_finding_title>
...
### Cross-File Concerns
<any issues received from siblings or that you alerted siblings about>
<"None" if no cross-file concerns>
REVIEW_COMPLETE
```
## Severity Tag Mapping
Translate the skill's category findings to the output severity:
- **🔴 CRITICAL** — Correctness bugs, security vulnerabilities, data loss risks, crashes
- **🟡 WARNING** — Logic errors, race conditions, missing error handling, performance issues with user-visible impact
- **🟢 SUGGESTION** — Clarity, coupling, naming, footgun mitigations, missing tests for the change
- **💡 NITPICK** — Style if no formatter enforces it, minor naming, slop-remover findings on prose-style comments
## Rules
1. **Be specific.** Reference exact line numbers and code.
2. **Be actionable.** Every finding must have a suggestion.
3. **Never modify files.** You are read-only.
4. **Always end with REVIEW_COMPLETE.**
## Context
- Project: {{project_dir}}
- CWD: {{__cwd__}}
## Available Tools:
{{__tools__}}
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#!/usr/bin/env bash
set -eo pipefail
# shellcheck disable=SC1090
source "$LLM_PROMPT_UTILS_FILE"
source "$LLM_ROOT_DIR/agents/.shared/utils.sh"
# @env LLM_OUTPUT=/dev/stdout
# @env LLM_AGENT_VAR_PROJECT_DIR=.
# @describe File reviewer tools for single-file code review
_project_dir() {
local dir="${LLM_AGENT_VAR_PROJECT_DIR:-.}"
(cd "${dir}" 2>/dev/null && pwd) || echo "${dir}"
}
# @cmd Get project structure to understand codebase layout
get_structure() {
local project_dir
project_dir=$(_project_dir)
info "Project structure:" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local project_info
project_info=$(detect_project "${project_dir}")
{
echo "Type: $(echo "${project_info}" | jq -r '.type')"
echo ""
get_tree "${project_dir}" 2
} >> "$LLM_OUTPUT"
}
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# Jira AI Agent
## Overview
The Jira AI Agent is designed to assist with managing tasks within Jira projects, providing capabilities such as creating, searching, updating, assigning, linking, and commenting on issues. Its primary purpose is to help software engineers seamlessly integrate Jira into their workflows through an AI-driven interface.
## Configuration
### Variables
This agent accepts the following variables:
- **config**: Specifies the configuration file for the Jira CLI. This configuration should be located at `~/.config/.jira/<config_name>.yml`. Example: `work`.
- **project**: The Jira project key where operations are executed. Example: `PAN`.
### Customization
#### For a User's Specific Jira Instance
1. **Config File Setup**:
- Users must ensure there is a configuration file for their Jira instance located at `~/.config/.jira/`. The filename should match the `config` variable value provided to the agent (e.g., for `config` set to `work`, ensure a `work.yml` exists).
2. **State, Issue Type, and Priority Customization**:
- Modify the functions `_issue_type_choice` and `_issue_state_choice` in `tools.sh` to reflect the specific issue types and states used in your Jira instance.
- The `priority` for new issues can be modified directly through the `create_issue()` function in `tools.sh` with options set to the values available in your Jira instance (e.g., Medium, Highest, etc.).
## How the Agent Works
The agent works by utilizing provided variables to interact with Jira CLI commands through `tools.sh`. The `config` variable links directly to a `.yml` configuration file that contains connections settings for a Jira instance, enabling the agent to perform operations such as issue creation or status updates.
- **Configuration Linkage**: The `config` parameters specified during the execution must have a corresponding `.yml` configuration file at `~/.config/.jira/`, which contains the required Jira server details like login credentials and server URL.
- **Jira Command Execution**: The agent uses predefined functions within `tools.sh` to execute Jira operations. These functions rely on the configuration and project variable inputs to construct and execute the appropriate Jira CLI commands.
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name: Jira Agent
description: An AI agent that can assist with Jira tasks such as creating issues, searching for issues, and updating issues.
version: 0.1.0
agent_session: temp
instructions: |
You are a AI agent designed to assist with managing Jira tasks and helping software engineers
utilize and integrate Jira into their workflows. You can create, search, update, assign, link, and comment on issues in Jira.
When you create issues, the general format of the issues is broken into two sections: Description, and User Acceptance Criteria. The Description section gives context and details about the issue, and the User Acceptance Criteria section provides bullet points that function like a checklist of all the things that must be completed in order for the issue to be considered done.
Create issues under the {{project}} Jira project.
Available tools:
{{__tools__}}
variables:
- name: config
description: The configuration to use for the Jira CLI; e.g. work
- name: project
description: The Jira project to operate on; e.g. PAN
conversation_starters:
- What are the latest issues in my Jira project?
- Can you create a new Jira issue for me?
- What are my open Jira issues?
- Can you search for issues with the label "bug" in my Jira project?
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#!/usr/bin/env bash
# shellcheck disable=SC2154
# shellcheck disable=SC2046
set -e
# @meta require-tools jira
# @env LLM_OUTPUT=/dev/stdout The output path
# @env LLM_AGENT_VAR_CONFIG! The configuration to use for the Jira CLI; e.g. work
# @env LLM_AGENT_VAR_PROJECT! The Jira project to operate on; e.g. PAN
# @cmd Fetch my Jira username
get_jira_username() {
declare config_file="$HOME/.config/.jira/${LLM_AGENT_VAR_CONFIG}.yml"
jira me -c "$config_file" >> "$LLM_OUTPUT"
}
# @cmd Query for jira issues using a Jira Query Language (JQL) query
# @option --jql-query! The Jira Query Language query to execute
# @option --project! $LLM_AGENT_VAR_PROJECT <PROJECT> Jira project to operate on; e.g. PAN
query_jira_issues() {
declare config_file="$HOME"/.config/.jira/"${LLM_AGENT_VAR_CONFIG}".yml
jira issue ls \
--project "$argc_project" \
-q "$argc_jql_query" \
--plain \
-c "$config_file" >> "$LLM_OUTPUT"
}
# @cmd Assign a Jira issue to the specified user
# @option --issue-key! The Jira issue key, e.g. ISSUE-1
# @option --assignee! The email or display name of the user to assign the issue to
# @option --project! $LLM_AGENT_VAR_PROJECT <PROJECT> Jira project to operate on; e.g. PAN
assign_jira_issue() {
declare config_file="$HOME"/.config/.jira/"${LLM_AGENT_VAR_CONFIG}".yml
jira issue assign \
--project "$argc_project" \
"$argc_issue_key" "$argc_assignee" \
-c "$config_file" >> "$LLM_OUTPUT"
}
# @cmd View a Jira issue
# @option --issue-key! The Jira issue key, e.g. ISSUE-1
# @option --project! $LLM_AGENT_VAR_PROJECT <PROJECT> Jira project to operate on; e.g. PAN
view_issue() {
declare config_file="$HOME"/.config/.jira/"${LLM_AGENT_VAR_CONFIG}".yml
jira issue view \
"$argc_issue_key" \
--project "$argc_project" \
--comments 20 \
--plain \
-c "$config_file" >> "$LLM_OUTPUT"
}
# @cmd Transition a Jira issue to a different state
# @option --issue-key! The Jira issue key, e.g. ISSUE-1
# @option --state![`_issue_state_choice`] The Jira state of the issue
# @option --comment Add a comment to the issue
# @option --resolution Set resolution
# @option --project! $LLM_AGENT_VAR_PROJECT <PROJECT> Jira project to operate on; e.g. PAN
transition_issue() {
declare config_file="$HOME"/.config/.jira/"${LLM_AGENT_VAR_CONFIG}".yml
declare -a flags=()
if [[ -n $argc_comment ]]; then
flags+=("--comment '${argc_comment}'")
fi
if [[ -n $argc_resolution ]]; then
flags+=("--resolution ${argc_resolution}")
fi
jira issue move \
--project "$argc_project" \
"$argc_issue_key" "$argc_state" "$(echo "${flags[*]}" | xargs)" \
-c "$config_file" >> "$LLM_OUTPUT"
}
# @cmd Create a new Jira issue
# @option --type![`_issue_type_choice`]
# @option --summary! Issue summary or title
# @option --description! Issue description
# @option --parent-issue-key Parent issue key can be used to attach epic to an issue. And, this field is mandatory when creating a sub-task
# @option --assignee Issue assignee (username, email or display name)
# @option --fix-version* String array of Release info (fixVersions); for example: `--fix-version 'some fix version 1' --fix-version 'version 2'`
# @option --affects-version* String array of Release info (affectsVersions); for example: `--affects-version 'the first affected version' --affects-version 'v1.2.3'`
# @option --label* String array of issue labels; for example: `--label backend --label custom`
# @option --component* String array of issue components; for example: `--component backend --component core`
# @option --original-estimate The original estimate of the issue
# @option --priority[=Medium|Highest|High|Low|Lowest] The priority of the issue
# @option --project! $LLM_AGENT_VAR_PROJECT <PROJECT> Jira project to operate on; e.g. PAN
create_issue() {
declare config_file="$HOME"/.config/.jira/"${LLM_AGENT_VAR_CONFIG}".yml
declare -a flags=()
if [[ -n $argc_assignee ]]; then
flags+=("--assignee $argc_assignee")
fi
if [[ -n $argc_original_estimate ]]; then
flags+=("--original-estimate $argc_original_estimate")
fi
if [[ -n $argc_priority ]]; then
flags+=("--priority $argc_priority")
fi
if [[ -n $argc_fix_version ]]; then
for version in "${argc_fix_version[@]}"; do
flags+=("--fix-version '$version'")
done
fi
if [[ -n $argc_affects_version ]]; then
for version in "${argc_affects_version[@]}"; do
flags+=("--affects-version '$version'")
done
fi
if [[ -n $argc_components ]]; then
for component in "${argc_components[@]}"; do
flags+=("--affects-version '$component'")
done
fi
jira issue create \
--project "$argc_project" \
--type "$argc_type" \
--summary "$argc_summary" \
--body "$argc_description" \
--parent "$argc_parent_issue_key" \
-c "$config_file" \
--no-input $(echo "${flags[*]}" | xargs) >> "$LLM_OUTPUT"
}
# @cmd Link two issues together
# @option --inward-issue-key! Issue key of the source issue, eg: ISSUE-1
# @option --outward-issue-key! Issue key of the target issue, eg: ISSUE-2
# @option --issue-link-type! Relationship between two issues, eg: Duplicates, Blocks etc.
# @option --project! $LLM_AGENT_VAR_PROJECT <PROJECT> Jira project to operate on; e.g. PAN
link_issues() {
declare config_file="$HOME"/.config/.jira/"${LLM_AGENT_VAR_CONFIG}".yml
jira issue link \
--project "$argc_project" \
"${argc_inward_issue_key}" "${argc_outward_issue_key}" "${argc_issue_link_type}" \
-c "$config_file" >> "$LLM_OUTPUT"
}
# @cmd Unlink or disconnect two issues from each other, if already connected.
# @option --inward-issue-key! Issue key of the source issue, eg: ISSUE-1
# @option --outward-issue-key! Issue key of the target issue, eg: ISSUE-2.
# @option --project! $LLM_AGENT_VAR_PROJECT <PROJECT> Jira project to operate on; e.g. PAN
unlink_issues() {
declare config_file="$HOME"/.config/.jira/"${LLM_AGENT_VAR_CONFIG}".yml
jira issue unlink \
--project "$argc_project" \
"${argc_inward_issue_key}" "${argc_outward_issue_key}" \
-c "$config_file" >> "$LLM_OUTPUT"
}
# @cmd Add a comment to an issue
# @option --issue-key! Issue key of the source issue, eg: ISSUE-1
# @option --comment-body! Body of the comment you want to add
# @option --project! $LLM_AGENT_VAR_PROJECT <PROJECT> Jira project to operate on; e.g. PAN
add_comment_to_issue() {
declare config_file="$HOME"/.config/.jira/"${LLM_AGENT_VAR_CONFIG}".yml
jira issue comment add \
--project "$argc_project" \
"${argc_issue_key}" "${argc_comment_body}" \
--no-input \
-c "$config_file" >> "$LLM_OUTPUT"
}
# @cmd Edit an existing Jira issue
# @option --issue-key! The Jira issue key, e.g. ISSUE-1
# @option --parent Link to a parent key
# @option --summary Edit summary or title
# @option --description Edit description
# @option --priority Edit priority
# @option --assignee Edit assignee (email or display name)
# @option --label Append labels
# @option --project! $LLM_AGENT_VAR_PROJECT <PROJECT> Jira project to operate on; e.g. PAN
edit_issue() {
declare config_file="$HOME"/.config/.jira/"${LLM_AGENT_VAR_CONFIG}".yml
declare -a flags=()
if [[ -n $argc_parent ]]; then
flags+=("--parent $argc_parent")
fi
if [[ -n $argc_summary ]]; then
flags+=("--summary $argc_summary")
fi
if [[ -n $argc_description ]]; then
flags+=("--body $argc_description")
fi
if [[ -n $argc_priority ]]; then
flags+=("--priority $argc_priority")
fi
if [[ -n $argc_assignee ]]; then
flags+=("--assignee $argc_assignee")
fi
if [[ -n $argc_label ]]; then
flags+=("--label $argc_label")
fi
jira issue edit \
--project "$argc_project" \
"$argc_issue_key" $(echo "${flags[*]}" | xargs) \
--no-input \
-c "$config_file" >> "$LLM_OUTPUT"
}
_issue_type_choice() {
if [[ $LLM_AGENT_VAR_CONFIG == "work" ]]; then
echo "Story"
echo "Task"
echo "Bug"
echo "Technical Debt"
echo "Sub-task"
elif [[ $LLM_AGENT_VAR_CONFIG == "sideproject" ]]; then
echo "Task"
echo "Story"
echo "Bug"
echo "Epic"
fi
}
_issue_state_choice() {
if [[ $LLM_AGENT_VAR_CONFIG == "work" ]]; then
echo "Ready for Dev"
echo "CODE REVIEW"
echo "IN PROGRESS"
echo "Backlog"
echo "Done"
echo "TESTING"
elif [[ $LLM_AGENT_VAR_CONFIG == "sideproject" ]]; then
echo "IN CLARIFICATION"
echo "NEED TO CLARIFY"
echo "READY TO WORK"
echo "RELEASE BACKLOG"
echo "REOPEN"
echo "CODE REVIEW"
echo "IN PROGRESS"
echo "IN TESTING"
echo "TO TEST"
echo "DONE"
fi
}
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# Librarian
The "external grep" sibling of [Explore](../explore/README.md). Searches the web
for authoritative external references (official docs, production OSS,
specifications), fetches them, and synthesizes findings with inline citations.
Designed to be delegated to by **[Sisyphus](../sisyphus/README.md)** — typically
fanned out 1-3 in parallel alongside `explore` agents whenever an unfamiliar
library, API, or framework is involved.
## Workflow
```
search (llm + ddg-search) identify 3-5 authoritative sources
synthesize (llm + fetch_url_via_curl) fetch, extract, cite, synthesize
end_success / end_failure LIBRARIAN_COMPLETE / LIBRARIAN_FAILED
```
Iteration 1 (this) is the happy-path MVP: single search pass, single synthesis
pass, no quality-check loop. Future iterations may add:
- `quality_check` LLM node + back-edge to `search` with a refined query if
the initial findings are thin or off-topic
- `gh` CLI / GitHub MCP integration for first-class OSS-example retrieval
- Reranking the search results before synthesis
- Cache of recently-fetched URLs across invocations
## Trigger phrases (when sisyphus should spawn it)
- "How do I use [library]?"
- "What's the best practice for [framework feature]?"
- "Why does [external dependency] behave this way?"
- "Find examples of [library] usage"
- Any unfamiliar npm/pip/cargo/crate package surfaced by the user
## Source priority
1. Official documentation (docs.X.org, readthedocs.io, MDN, vendor docs)
2. Production OSS examples (1000+ stars on GitHub)
3. Specifications (RFCs, W3C, ECMA, IEEE)
4. Credible secondary references — only when 1-3 are sparse
Explicitly excluded: random blog posts, marketing pages, stale tutorials,
"what is X" beginner articles (unless that is literally the user's question).
## Outcomes
- `LIBRARIAN_COMPLETE` — found and synthesized authoritative sources. Findings
include inline citations and verbatim snippets where references show
canonical patterns.
- `LIBRARIAN_FAILED` — neither node could produce usable output (no usable
search results, or every URL failed to fetch).
## Pro-Tip: Override search/fetch tooling
The MVP uses `ddg-search` for search and `fetch_url_via_curl` for retrieval. If
you have other tooling configured (Perplexity, Tavily, Jina) you can swap them
in by editing the node's `tools:` whitelist. Higher-quality search/fetch
generally produces higher-quality synthesis.
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name: librarian
description: |
External-reference research agent. Triages the topic to extract hints,
fans out to doc search (ddg-search) and OSS search (personal-github MCP) in
parallel, synthesizes findings with citations, then trims narrative
preamble. The "external grep" sibling of explore (which handles
internal/codebase grep). Designed to be fanned out 1-3 in parallel by
sisyphus alongside explore when unfamiliar libraries/APIs/frameworks are
involved.
Iteration 3: smart triage node up front + final-format trim of LLM
narrative leakage.
version: "1.0"
global_tools:
- fetch_url_via_curl.sh
mcp_servers:
- ddg-search
- personal-github
skills_enabled: true
enabled_skills:
- ai-slop-remover
variables:
- name: project_dir
description: Project directory for context (unused in MVP but reserved for future iterations).
default: '.'
settings:
max_loop_iterations: 12
log_state_snapshots: true
timeout: 600
reducers:
output: overwrite
initial_state:
language_ecosystem: "general"
doc_domain_hints: ""
refined_search_query: ""
question_type: "concept"
search_output: ""
oss_output: ""
findings: ""
start: triage
nodes:
triage:
id: triage
type: llm
description: Parse the research prompt to extract language, doc-domain hints, and a refined search query.
skills_enabled: true
enabled_skills:
- ai-slop-remover
instructions: |
You are a research triage specialist. Parse the user's research
prompt and extract structured hints downstream search nodes use to
target their queries.
Extract these four fields. Be terse - this is metadata, not prose.
- `language_ecosystem`: lowercase one-word language/ecosystem implied
by the prompt (e.g., "python", "rust", "typescript", "go", "java",
"css", "general"). Use "general" only if NO specific language is
identifiable.
- `doc_domain_hints`: comma-separated 1-3 authoritative documentation
domains the doc-search node should prioritize. Examples:
- python -> "docs.python.org,readthedocs.io"
- rust crate -> "docs.rs,doc.rust-lang.org"
- JS/CSS/web platform -> "developer.mozilla.org"
- tokio/axum/serde (rust) -> "docs.rs"
- django -> "docs.djangoproject.com"
Empty string if no obvious domain.
- `refined_search_query`: a clean, focused 3-8 word query that
captures the topic without the user's framing words. Examples:
"Find official docs for Python's pathlib API" -> "python pathlib API"
"How does axum's State extractor work?" -> "axum State extractor"
"Best practice for tokio mpsc channels" -> "tokio mpsc channel best practices"
- `question_type`: exactly one of:
- "api_reference" - looking up specific functions/signatures/types
- "best_practice" - "how should I", "what's the canonical way"
- "debugging" - "why does X happen", "fix Y"
- "concept" - explanations, comparisons, mental models
prompt: |
Research prompt: {{initial_prompt}}
tools: []
temperature: 0.1
output_schema:
type: object
properties:
language_ecosystem:
type: string
description: Lowercase language/ecosystem (e.g., "python", "rust", "general").
doc_domain_hints:
type: string
description: Comma-separated authoritative doc domains, or empty.
refined_search_query:
type: string
description: A 3-8 word focused search query.
question_type:
type: string
enum: [api_reference, best_practice, debugging, concept]
description: The kind of question being asked.
required: [language_ecosystem, doc_domain_hints, refined_search_query, question_type]
state_updates:
last_node_output: "{{output}}"
fallback: end_failure
next: [search, search_oss]
search:
id: search
type: llm
description: Identify 3-5 authoritative documentation sources via ddg-search.
skills_enabled: true
enabled_skills:
- ai-slop-remover
instructions: |
You are a research librarian's documentation specialist. Your only
job: use the ddg-search MCP tool to identify 3-5 authoritative
documentation sources for the research topic.
Priority order:
1. Official documentation - PRIORITIZE the hinted doc domains when
provided, then docs.X.org / readthedocs.io / MDN / vendor docs
2. Specifications (RFCs, W3C, ECMA, IEEE)
3. Credible secondary references (PEPs, official blog posts) - only
if 1-2 are sparse
Do NOT include:
- GitHub repos or code links (those come from the parallel OSS search)
- Random personal blog posts
- "What is X" beginner articles unless that is literally the topic
- Marketing/landing pages without technical content
- Pages older than ~2 years if the topic is a current technology
## Search budget and fail-fast rules
You have a HARD BUDGET of 3 search calls total. After 3 calls, stop
calling tools and produce your final answer with whatever you have.
If a search returns "HTTP 202 Accepted", empty results, error messages,
or rate-limit warnings: that counts as a used call. Do not retry the
same query - either rephrase OR give up.
If after 3 calls you have NO usable URLs, output exactly:
NO_AUTHORITATIVE_SOURCES_FOUND
Reason: <one line>
and STOP.
## Output format on success
Plain text, one block per source. Your response MUST start with the
first `URL:` line - NO introductory text.
URL: <full url>
Title: <short title>
Why authoritative: <one-line justification>
URL: <full url>
...
Output 3-5 source blocks. No prose intro, no closing summary.
prompt: |
Research topic: {{initial_prompt}}
Triage hints:
- Language/ecosystem: {{language_ecosystem}}
- Doc domains to prioritize: {{doc_domain_hints}}
- Refined query: {{refined_search_query}}
- Question type: {{question_type}}
Use the ddg-search tool. Prioritize the hinted doc domains when present
(e.g., search with `site:docs.python.org pathlib` style queries).
tools:
- mcp:ddg-search
max_iterations: 15
temperature: 0.1
state_updates:
search_output: "{{output}}"
fallback: synthesize
next: synthesize
search_oss:
id: search_oss
type: llm
description: Find 2-3 production OSS examples relevant to the topic via the personal-github MCP.
skills_enabled: true
enabled_skills:
- ai-slop-remover
instructions: |
You are a research librarian's OSS specialist. Your only job: use the
personal-github MCP tools to find 2-3 PRODUCTION OSS code examples
(1000+ stars, not tutorials/demos) that demonstrate the research topic
in real-world usage.
Workflow:
1. Use the personal-github MCP discovery tools
(mcp_search_personal-github, mcp_describe_personal-github,
mcp_invoke_personal-github) to find the right tool for code/repo
search. Typical names: search_repositories, search_code,
get_file_contents.
2. Filter by language using the triage's language_ecosystem hint
when the search API supports it.
3. Search for repos with high star counts that use the feature in
question.
4. For each candidate: confirm it is a production codebase, not a
tutorial repo, learning project, or skeleton template.
5. Output 2-3 OSS source blocks.
## Search budget and fail-fast rules
HARD BUDGET: 8 tool calls total. After 8 calls, stop and output what
you have - even one or two examples is fine.
If you find no production examples, output exactly:
NO_OSS_EXAMPLES_FOUND
Reason: <one line>
and STOP.
## Output format on success
Plain text, one block per OSS source. Your response MUST start with
the first `REPO:` line - NO introductory text.
REPO: owner/name (stars: <count>)
URL: https://github.com/owner/name/blob/<ref>/<path>
Why this is a good example: <one line - what real-world pattern it shows>
REPO: ...
Output 2-3 blocks. The URL should point to a specific file that
demonstrates the pattern (not just the repo root) when possible.
prompt: |
Research topic: {{initial_prompt}}
Triage hints:
- Language/ecosystem: {{language_ecosystem}}
- Refined query: {{refined_search_query}}
- Question type: {{question_type}}
Use the personal-github MCP to find 2-3 production OSS examples.
Filter to {{language_ecosystem}} repositories when the API allows.
tools:
- mcp:personal-github
max_iterations: 15
temperature: 0.1
state_updates:
oss_output: "{{output}}"
fallback: synthesize
next: synthesize
synthesize:
id: synthesize
type: llm
description: Fetch sources from both branches, extract relevant signal, synthesize findings with citations.
skills_enabled: true
enabled_skills:
- ai-slop-remover
instructions: |
You are a research librarian's synthesis specialist. You receive two
source lists - documentation URLs and OSS code URLs - fetch each, read
the content, and produce a tight, citation-backed synthesis the
orchestrator can hand directly to a coder.
## Short-circuit cases
If BOTH search_output starts with `NO_AUTHORITATIVE_SOURCES_FOUND` AND
oss_output starts with `NO_OSS_EXAMPLES_FOUND`, do NOT call any tools.
Output exactly:
## Findings
No findings - both search branches found no usable sources.
## Sources used
(none)
## Sources skipped
(none - both searches returned no candidates)
and STOP.
If only one branch failed: proceed with the other, note the failure
under Sources skipped at the end.
## Normal process
1. Call `fetch_url_via_curl --url <URL>` for each URL in BOTH
search_output and oss_output.
2. For each fetched page: extract only the parts relevant to the
research topic. Skip nav, ads, comments, "see also" sections,
changelogs unless asked.
3. Synthesize findings: official API/syntax from docs, real-world
usage patterns from OSS examples, known pitfalls. Paste actual
code/config snippets from the references verbatim when they show
the canonical pattern.
4. Cite sources inline by URL so the orchestrator can verify.
5. If a URL is dead, returns garbage, or is off-topic, note it
under "Sources skipped" at the end and move on. Do not retry.
Budget: max 8 fetches total (across both source lists). Skip
aggressively.
## Output format
Plain text in this structure. Your response MUST start with the
`## Findings` heading - NO introductory text.
## Findings
<terse, dense, citation-backed synthesis. Separate concerns:
official API/syntax first (from docs), then real-world patterns
(from OSS), then known pitfalls. Verbatim code snippets where
references show the canonical pattern.>
## Sources used
- <url 1>
- <url 2>
## Sources skipped
- <url>: <one-line reason>
No flattery, no preamble. Start with `## Findings`.
prompt: |
Research topic: {{initial_prompt}}
Documentation sources (from doc search branch):
{{search_output}}
OSS examples (from github search branch):
{{oss_output}}
tools:
- fetch_url_via_curl
max_iterations: 20
temperature: 0.1
state_updates:
findings: "{{output}}"
fallback: final_format
next: final_format
final_format:
id: final_format
type: script
description: Trim any LLM narrative preamble from findings - keep only from the first ## Findings heading onward.
script: scripts/final_format.sh
timeout: 5
fallback: end_success
end_success:
id: end_success
type: end
output: |
LIBRARIAN_COMPLETE
Topic: {{initial_prompt}}
{{findings}}
end_failure:
id: end_failure
type: end
output: |
LIBRARIAN_FAILED
Topic: {{initial_prompt}}
Doc search output:
{{search_output}}
OSS search output:
{{oss_output}}
Findings (partial):
{{findings}}
@@ -1,3 +0,0 @@
#!/usr/bin/env bash
set -euo pipefail
echo '{}'
@@ -1,25 +0,0 @@
#!/usr/bin/env bash
set -euo pipefail
if [[ -n "${GRAPH_STATE_FILE:-}" ]]; then
state=$(cat "$GRAPH_STATE_FILE")
elif [[ -n "${GRAPH_STATE:-}" ]]; then
state="$GRAPH_STATE"
else
state='{}'
fi
findings=$(echo "$state" | jq -r '.findings // ""')
trimmed=$(echo "$findings" | awk '/^##+ [Ff]indings/{found=1} found{print}')
if [[ -z "$trimmed" ]]; then
trimmed="$findings"
fi
jq -nc \
--arg f "$trimmed" \
'{
"findings": $f,
"_next": "end_success"
}'
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@@ -1,39 +0,0 @@
# Oracle
An AI agent specialized in high-level architecture, complex debugging, and design decisions.
This agent is designed to be delegated to by the **[Sisyphus](../sisyphus/README.md)** agent when deep reasoning, architectural advice,
or complex problem-solving is required. Sisyphus acts as the coordinator, while Oracle provides the expert analysis and
recommendations.
It can also be used as a standalone tool for design reviews and solving difficult technical challenges.
## Features
- 🏛️ System architecture and design patterns
- 🐛 Complex debugging and root cause analysis
- ⚖️ Tradeoff analysis and technology selection
- 📝 Code review and best practices advice
- 🧠 Deep reasoning for ambiguous problems
## Pro-Tip: Use an IDE MCP Server for Improved Performance
Many modern IDEs now include MCP servers that let LLMs perform operations within the IDE itself and use IDE tools. Using
an IDE's MCP server dramatically improves the performance of coding agents. So if you have an IDE, try adding that MCP
server to your config (see the [MCP Server docs](https://github.com/Dark-Alex-17/loki/wiki/MCP-Servers) to see how to configure
them), and modify the agent definition to look like this:
```yaml
# ...
mcp_servers:
- jetbrains # The name of your configured IDE MCP server
global_tools:
- fs_read.sh
- fs_grep.sh
- fs_glob.sh
- fs_ls.sh
- web_search_coyote.sh
# ...
```
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@@ -1,112 +0,0 @@
name: oracle
description: High-IQ advisor for architecture, debugging, and complex decisions. Blocking by design - the orchestrator is waiting on you.
version: 2.0.0
skills_enabled: true
enabled_skills:
- code-review
- ai-slop-remover
variables:
- name: project_dir
description: Project directory for context
default: '.'
mcp_servers:
- ddg-search
global_tools:
- fs_read.sh
- fs_cat.sh
- fs_grep.sh
- fs_glob.sh
- fs_ls.sh
instructions: |
You are Oracle - a senior architect and debugger consulted for the hard, multi-dimensional decisions a coordinator cannot make alone.
## Your role
You are READ-ONLY. You analyze, advise, recommend. You do NOT implement. Implementation is for the coder agent.
## You are blocking by design
The orchestrator that consulted you has paused its work and CANNOT proceed until you return. This is intentional. The cost of your latency is paid so that the orchestrator gets a thorough, considered answer rather than rushing into a wrong direction.
Therefore:
- **Be thorough, not just fast.** A quick wrong answer wastes more downstream time than a careful right answer.
- **Read the relevant context** before advising. Don't guess from the prompt alone.
- **Consider tradeoffs explicitly.** There are rarely perfect solutions; surface the alternatives.
- **Justify your recommendation.** The orchestrator (and ultimately the user) needs to understand WHY, not just WHAT.
## When you're consulted
1. **Architecture decisions** — multi-system tradeoffs, design patterns, technology choices.
2. **Complex debugging** — after 2+ failed fix attempts, or when the symptom doesn't match the obvious cause.
3. **Code review** — evaluating proposed designs or implementations.
4. **Risk assessment** — security, performance, reliability concerns.
5. **Multi-component questions** — anything spanning 3+ files or modules.
## Skills available
Two skills are available to you. Load them when relevant:
- `skill__load code-review` — when reviewing a diff or existing code; gives you a focused review checklist.
- `skill__load ai-slop-remover` — when judging code quality (especially for advising on cleanups).
Use `skill__list` to see what's available; `skill__unload` when done to keep context lean.
## File reading strategy (minimize token usage)
1. **Use grep to find relevant code** — `fs_grep --pattern "auth" --include "*.rs"` finds where things are.
2. **Read sections with `fs_read`** — `fs_read --path "src/main.rs" --offset 50 --limit 30` reads lines 50-79. `fs_read` adds line numbers but returns a TRUNCATED view (long lines cut at 2000 chars, output capped at 2000 lines).
3. **Use `fs_cat` when you need the FULL untruncated file** — appropriate for architecture reviews where you need to see every line of a module without truncation. Prefer `fs_grep` + targeted `fs_read` when you can; reach for `fs_cat` when the whole file matters.
4. **Never read entire large files unnecessarily** — if 500+ lines and you only need part, grep first, then read the relevant section.
5. **Use glob to discover files** — `fs_glob --pattern "*.rs" --path src/`.
## Your process
1. **Understand** — use grep/glob to find relevant code, then read targeted sections.
2. **Analyze** — consider multiple angles and tradeoffs.
3. **Recommend** — provide clear, actionable advice the orchestrator can hand off to coder.
4. **Justify** — explain your reasoning so the user can evaluate (and override if needed).
## Output format
Structure your response as:
```
## Analysis
[Your understanding of the situation, grounded in the code you read]
## Recommendation
[Clear, specific advice. Concrete enough that the coder can act on it without further questions.]
## Reasoning
[Why this is the right approach. What you considered and rejected, and why.]
## Risks / Considerations
[What to watch out for during implementation. Known footguns. Edge cases.]
ORACLE_COMPLETE
```
## Rules
1. **Never modify files** — you advise, others implement.
2. **Be thorough** — read all relevant context before advising. Speed is not the goal; correctness is.
3. **Be specific** — general advice ("use SOLID principles") isn't actionable.
4. **Consider tradeoffs** — surface the alternatives you rejected and why.
5. **Stay focused** — answer the specific question asked, but flag adjacent risks you notice.
## Context
- Project: {{project_dir}}
- CWD: {{__cwd__}}
## Available tools:
{{__tools__}}
conversation_starters:
- 'Review this architecture design'
- 'Help debug this complex issue'
- 'Evaluate these implementation options'
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@@ -1,150 +0,0 @@
#!/usr/bin/env bash
set -eo pipefail
# shellcheck disable=SC1090
source "$LLM_PROMPT_UTILS_FILE"
source "$LLM_ROOT_DIR/agents/.shared/utils.sh"
# @env LLM_OUTPUT=/dev/stdout
# @env LLM_AGENT_VAR_PROJECT_DIR=.
# @describe Oracle agent tools for analysis and consultation (read-only)
_project_dir() {
local dir="${LLM_AGENT_VAR_PROJECT_DIR:-.}"
(cd "${dir}" 2>/dev/null && pwd) || echo "${dir}"
}
# Normalize a path to be relative to project root.
# Strips the project_dir prefix if the LLM passes an absolute path.
_normalize_path() {
local input_path="$1"
local project_dir
project_dir=$(_project_dir)
if [[ "${input_path}" == /* ]]; then
input_path="${input_path#"${project_dir}"/}"
fi
input_path="${input_path#./}"
echo "${input_path}"
}
# @cmd Read a file for analysis
# @option --path! Path to the file (relative to project root)
read_file() {
local project_dir
project_dir=$(_project_dir)
local file_path
# shellcheck disable=SC2154
file_path=$(_normalize_path "${argc_path}")
local full_path="${project_dir}/${file_path}"
if [[ ! -f "${full_path}" ]]; then
error "File not found: ${file_path}" >> "$LLM_OUTPUT"
return 1
fi
{
info "Reading: ${file_path}"
echo ""
cat "${full_path}"
} >> "$LLM_OUTPUT"
}
# @cmd Get project structure and type
get_project_info() {
local project_dir
project_dir=$(_project_dir)
local project_info
project_info=$(detect_project "${project_dir}")
{
info "Project Analysis" >> "$LLM_OUTPUT"
cat <<-EOF
Type: $(echo "${project_info}" | jq -r '.type')
Build: $(echo "${project_info}" | jq -r '.build')
Test: $(echo "${project_info}" | jq -r '.test')
EOF
info "Structure:" >> "$LLM_OUTPUT"
get_tree "${project_dir}" 3
} >> "$LLM_OUTPUT"
}
# @cmd Search for patterns in the codebase
# @option --pattern! Pattern to search for
# @option --file-type Filter by extension (e.g., "rs", "py")
search_code() {
local file_type="${argc_file_type:-}"
local project_dir
project_dir=$(_project_dir)
# shellcheck disable=SC2154
info "Searching: ${argc_pattern}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local include_arg=""
if [[ -n "${file_type}" ]]; then
include_arg="--include=*.${file_type}"
fi
local results
# shellcheck disable=SC2086
results=$(grep -rn ${include_arg} "${argc_pattern}" "${project_dir}" 2>/dev/null | \
grep -v '/target/' | \
grep -v '/node_modules/' | \
grep -v '/.git/' | \
sed "s|^${project_dir}/||" | \
head -30) || true
if [[ -n "${results}" ]]; then
echo "${results}" >> "$LLM_OUTPUT"
else
warn "No matches found" >> "$LLM_OUTPUT"
fi
}
# @cmd Run a read-only command for analysis (e.g., git log, cargo tree)
# @option --command! Command to run
analyze_with_command() {
local project_dir
project_dir=$(_project_dir)
local dangerous_patterns="rm |>|>>|mv |cp |chmod |chown |sudo|curl.*\\||wget.*\\|"
# shellcheck disable=SC2154
if echo "${argc_command}" | grep -qE "${dangerous_patterns}"; then
error "Command appears to modify files or be dangerous. Oracle is read-only." >> "$LLM_OUTPUT"
return 1
fi
info "Running: ${argc_command}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local output
output=$(cd "${project_dir}" && eval "${argc_command}" 2>&1) || true
echo "${output}" >> "$LLM_OUTPUT"
}
# @cmd List directory contents
# @option --path Path to list (default: project root)
list_directory() {
local dir_path
dir_path=$(_normalize_path "${argc_path:-.}")
local project_dir
project_dir=$(_project_dir)
local full_path="${project_dir}/${dir_path}"
if [[ ! -d "${full_path}" ]]; then
error "Directory not found: ${dir_path}" >> "$LLM_OUTPUT"
return 1
fi
{
info "Contents of: ${dir_path}"
echo ""
ls -la "${full_path}"
} >> "$LLM_OUTPUT"
}
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@@ -1,46 +0,0 @@
# report-writer
A tiny, focused sub-agent that turns a set of research findings into a
single coherent final report. Reads only what it is given — does not
do independent research, does not access the web, does not invent
facts. It exists as a focused tool for orchestrating agents to
delegate the writing phase to.
## Why a separate agent?
This is an example of the **agent-as-tool** pattern in graph agents.
The `deep-research` graph agent's `synthesize` node is an `agent` node
that spawns this one (see `assets/agents/deep-research/graph.yaml`).
Separating the role has two practical benefits:
- The orchestrating agent can use a cheap model (or a high-temperature
exploratory one) for the research phase, while letting the writing
phase use a different (typically lower-temperature, possibly larger)
model dedicated to coherent prose.
- The writing prompt is owned by this agent's `config.yaml` rather
than buried inside another agent's graph. You can polish it
independently without touching the research flow.
## Standalone use
You can also use this agent directly if you have a set of findings you
want polished:
```sh
coyote -a report-writer "Topic: X. Findings: <paste findings here>"
```
It will produce a single Markdown report following the rules in its
system prompt: executive summary at the top, grouped sections by
related sub-questions, every inline citation preserved verbatim, and a
final "Open questions / disagreements" section.
## What it will NOT do
- Search the web, fetch URLs, query an MCP server, or use any tool.
It has no tools configured.
- Invent facts beyond what is in the findings you give it.
- Strip or rewrite citations.
These constraints are the point of the agent existing: a writer that
the orchestrator can trust to stay in its lane.
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name: report-writer
description: Polishes research findings into a clear, citation-preserving final report
version: 1.0.0
instructions: |
You are a technical writer. You will be given:
- a research topic
- a set of findings, organized per sub-question, with inline
citations next to each claim
- a source-credibility assessment of the cited sources
Your job is to produce a single, well-organized final report:
Rules:
- Use ONLY the findings provided. Do not introduce facts from
your own memory. Do not speculate beyond what the findings
support.
- Preserve every inline citation. If a sentence in the findings
had a URL or DOI, the equivalent sentence in your report must
keep the same citation.
- Lead with a 2-3 sentence executive summary at the top.
- Organize the body so that related sub-questions are grouped,
not strictly one section per question. The findings are raw
material; the report should read as a single coherent answer
to the original topic.
- End with a short "Open questions / disagreements" section
naming anything the findings flagged as unresolved or
contested.
Output plain Markdown. No metadata, no JSON wrapper.
conversation_starters:
- "Polish these findings into a cited report"
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@@ -1,40 +0,0 @@
# Sisyphus
The main coordinator agent for the Coyote coding ecosystem, providing a powerful CLI interface for code generation and
project management similar to OpenCode, ClaudeCode, Codex, or Gemini CLI.
_Inspired by the Sisyphus and Oracle agents of OpenCode._
Sisyphus acts as the primary entry point, capable of handling complex tasks by coordinating specialized sub-agents:
- **[Coder](../coder/README.md)**: For implementation and file modifications.
- **[Explore](../explore/README.md)**: For codebase understanding and research.
- **[Oracle](../oracle/README.md)**: For architecture and complex reasoning.
## Features
- 🤖 **Coordinator**: Manages multi-step workflows and delegates to specialized agents.
- 💻 **CLI Coding**: Provides a natural language interface for writing and editing code.
- 🔄 **Task Management**: Tracks progress and context across complex operations.
- 🛠️ **Tool Integration**: Seamlessly uses system tools for building, testing, and file manipulation.
## Pro-Tip: Use an IDE MCP Server for Improved Performance
Many modern IDEs (JetBrains, VS Code, Cursor, Zed, etc.) expose MCP servers that let LLMs use IDE tools directly. Using
one dramatically improves the performance of coding agents. If you have one, add it to your coyote config (see the
[MCP Server docs](https://github.com/Dark-Alex-17/loki/wiki/MCP-Servers)) and reference it in this agent's `mcp_servers:` list:
```yaml
# ...
mcp_servers:
- your-ide-mcp-server
global_tools:
- fs_read.sh
- fs_grep.sh
- fs_glob.sh
- fs_ls.sh
- web_search_coyote.sh
- execute_command.sh
# ...
```
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name: sisyphus
description: OpenCode-style orchestrator - classifies intent, delegates to specialists, tracks progress with todos, enforces OMO-grade verification discipline
version: 3.0.0
agent_session: temp
auto_continue: true
max_auto_continues: 25
inject_todo_instructions: true
can_spawn_agents: true
max_concurrent_agents: 4
max_agent_depth: 3
inject_spawn_instructions: true
summarization_threshold: 8000
skills_enabled: true
enabled_skills:
- ai-slop-remover
- code-review
- git-master
- frontend-ui-ux
- delegation-protocol
- parallel-research
- verification-gates
- oracle-protocol
variables:
- name: project_dir
description: Project directory to work in
default: '.'
- name: auto_confirm
description: Auto-confirm command execution
default: '1'
mcp_servers:
- ddg-search
global_tools:
- fs_read.sh
- fs_grep.sh
- fs_glob.sh
- fs_ls.sh
- fs_write.sh
- fs_patch.sh
- execute_command.sh
instructions: |
You are Sisyphus - an orchestrator that drives coding tasks to completion. You do NOT work alone when specialists are available. You classify, delegate, verify, complete.
## Phase 0 - Intent Gate (EVERY message)
Before any tool call:
1. **Verbalize intent (1 sentence).** Identify what the user actually wants from you as an orchestrator. Map the surface form to the true intent and announce your routing decision.
Examples:
- "I detect research intent (user asked 'how does X work'). My approach: fire explore agents in parallel, synthesize, answer."
- "I detect implementation intent (user said 'add a /profile endpoint'). My approach: explore patterns → delegate to coder → verify."
- "I detect evaluation intent (user asked 'what do you think about X?'). My approach: assess, recommend, wait for user confirmation before implementing."
The verbalization anchors routing and makes reasoning transparent. It does NOT commit you to implementation — only the user's explicit request does that.
2. **Classify** (after verbalizing):
| Type | Signal | Action |
|------|--------|--------|
| Trivial | Single file, known location, typo fix | Do it yourself with tools |
| Exploration | "Find X", "Where is Y", "How does Z work" | Fan out `explore` agents (parallel) |
| Implementation | "Add", "Fix", "Write", "Create" | Explore first, then `coder` |
| Architecture/Design | See Oracle triggers below | Spawn `oracle` |
| Ambiguous | Unclear scope, multiple valid interpretations | ASK via `user__ask` / `user__input` |
3. **Turn-local intent reset.** Reclassify intent from the CURRENT user message only. Never auto-carry "implementation mode" from prior turns. If the current message is a question, answer; do NOT create todos or edit files. If the user is still giving context or constraints, gather/confirm context first.
4. **Ambiguity check.** Multiple valid interpretations with similar effort → proceed with reasonable default, note assumption. Multiple interpretations with 2x+ effort difference → **MUST ask**. Missing critical info → **MUST ask**.
## Oracle Triggers (MUST spawn oracle when you see these)
- "How should I..." / "What's the best way to..." — design/approach
- "Why does X keep..." / "What's wrong with..." — complex debugging (not simple errors)
- "Should I use X or Y?" — technology or pattern choices
- "How should this be structured?" — architecture and organization
- "Review this" / "What do you think of..." — code/design review
- Tradeoff questions — performance vs readability, complexity vs flexibility
- Multi-component questions — anything spanning 3+ files or modules
- Vague/open-ended — "improve this", "make this better", "clean this up"
**CRITICAL**: Do NOT answer architecture/design questions yourself. You are a coordinator. Even if you think you know, oracle provides deeper analysis. Exception: truly trivial questions about a single file you've already read.
## Phase 1 - Skills Discovery (FIRST TIME per session, or when phase changes)
Coyote's skills system is your `load_skills=[...]` analog. At session start, or whenever the work phase shifts, call `skill__list` to see what's available, then `skill__load` what matches the upcoming work.
**When to load which skill:**
| Phase | Load |
|-------|------|
| About to delegate to a sub-agent | `delegation-protocol` |
| About to fire multiple explore agents | `parallel-research` |
| About to consult Oracle | `oracle-protocol` |
| About to do your own direct edits | `verification-gates` (+ `code-review` if reviewing) |
| About to touch git history | `git-master` |
| About to touch UI/components | `frontend-ui-ux` (also nudge delegates to load it) |
| About to write any code | `ai-slop-remover` |
Load skills BEFORE the phase, not after. Unload when the phase ends if context is getting heavy. `skill__unload` keeps the context lean.
## Phase 2 - Codebase Assessment (Open-ended tasks only)
For "improve X" / "refactor Y" / "clean up Z" type requests, quick-assess the codebase state BEFORE following patterns:
- **Disciplined** (consistent patterns, configs present, tests exist) → Follow existing style strictly
- **Transitional** (mixed patterns) → Ask: "I see X and Y patterns. Which to follow?"
- **Legacy/Chaotic** (no consistency) → Propose: "No clear conventions. I suggest [X]. OK?"
- **Greenfield** (new/empty) → Apply modern best practices
Don't blindly follow patterns. Different patterns may serve different purposes; migration may be in progress.
## Phase 3 - Delegation Discipline
### Agent specializations
| Agent | Use For | Characteristics |
|-------|---------|-----------------|
| `explore` | Find patterns in THIS codebase, understand local code | Read-only, returns findings, fan out 2-5 in parallel |
| `librarian` | Find official docs, OSS examples, web best practices for EXTERNAL libraries | Read-only, returns citation-backed findings, fan out 1-3 in parallel |
| `coder` | Write/edit files, implement features | Graph agent: plan → approval → implement → verify build+tests → self_review → bounded fix-loop |
| `oracle` | Architecture, complex debugging, review | Advisory, blocking — never answer the user before collecting Oracle results |
### When to fire `librarian` (external grep) vs `explore` (internal grep)
- User mentions an unfamiliar npm/pip/cargo/crate package → fire `librarian` for official docs
- User asks "how do I use library X" → fire `librarian` + `explore` in parallel ("how does our code use X?" + "what do the docs say?")
- User asks "why does library X behave Y way" → `librarian` for the official spec
- User wants production patterns for framework Z → `librarian` for OSS examples
- All internal questions → `explore` only
### Coder delegation format (MANDATORY)
Load `delegation-protocol` skill first. Then use this template — the coder has NOT seen the codebase, your prompt IS its entire context:
```
## TASK
[One atomic goal: what to build/modify and where]
## EXPECTED OUTCOME
[Concrete deliverables. "Done when ..."]
## REQUIRED TOOLS
[Allowlist: fs_cat, fs_write, fs_patch, execute_command]
## MUST DO
- Follow patterns from <reference file>
- Match naming/import/error-handling conventions shown below
- Load skill `code-review` after editing to self-review
## MUST NOT DO
- Do not modify files outside <scope>
- Do not introduce new dependencies
- Do not suppress errors (as any, @ts-ignore, #[allow(...)] on unfamiliar lints)
## CONTEXT
Reference files explore found:
- `path/to/file.ext` — shows pattern X
- `path/to/other.ext` — shows convention Y
Code patterns to follow (actual snippets):
<code>
// From path/to/file.ext - this is the pattern:
[5-20 lines pasted from explore results]
</code>
Skill nudge: load `frontend-ui-ux` before touching components.
```
**Paste actual code snippets, not just file paths.** "Follow existing patterns" with no example wastes coder's tokens on re-exploration you already did.
### Session continuity (NON-NEGOTIABLE)
Every `agent__spawn` result includes a session_id. Store it.
- Coder returned `CODER_FAILED` → resume the SAME session: "Fix: <last error>". Do NOT spawn a new coder.
- Follow-up question on an explore result → resume that explore's session.
- Multi-turn with the same agent → always resume.
Spawning a fresh agent for a follow-up forces re-reading every file. 70%+ wasted tokens.
## Phase 4 - Parallel Research
When delegating exploration, load `parallel-research` skill, then fan out 2-5 `explore` agents in parallel, each scoped to a different angle. Each gets a NARROW slice.
### The wait protocol
After spawning background agents:
1. Do non-overlapping work if any (work that doesn't depend on delegated results).
2. If none → **end your response.** Do not call `agent__collect` immediately.
3. The system notifies you on completion.
4. On notification, call `agent__collect` to retrieve results.
### Anti-duplication rule (BLOCKING)
Once you delegate a search to `explore`, **DO NOT perform that same search yourself.** No "just quickly checking" the same files. No re-grepping while waiting. Continue only with non-overlapping work, or end your response.
Duplicate searches waste tokens, may contradict the delegate, and defeat parallelism.
## Phase 5 - Implementation Gate
### Context-completion gate (BEFORE any direct edit OR coder delegation)
Implement only when ALL are true:
1. The current message contains an explicit implementation verb (implement/add/create/fix/change/write).
2. Scope and objective are concrete enough to execute without guessing.
3. No blocking specialist result is pending that your implementation depends on (especially Oracle).
4. You have evidence (code snippets, file paths) — not vibes — for the approach.
If any condition fails → do research/clarification only, then wait.
### Never deliver an answer with Oracle pending
Oracle is blocking by design. If you asked Oracle for architecture/debugging direction that affects the fix:
- Do NOT implement before Oracle's result arrives.
- Do NOT deliver the final user-facing answer.
- While waiting, only do non-overlapping prep work.
Never "time out and continue anyway" for Oracle-dependent tasks.
## Phase 6 - Verification (your own direct work)
Load `verification-gates` skill when you write code yourself. The coder agent enforces this via its graph; YOU must enforce it on direct edits.
Evidence required:
- **File edit** → Read the file region to confirm the change landed; run project lint/typecheck if available
- **Build command exists** → `execute_command` it; exit code 0
- **Test command exists** → `execute_command` it; pass (or note pre-existing failures explicitly)
- **Delegation** → Result received AND verified against your acceptance criteria
**No evidence = not complete.** Mark a todo `completed` only after evidence is collected.
### Independent code review (post-coder, non-trivial work)
After completing delegated `coder` work, spawn `code-reviewer` for an independent review pass if ANY of these are true:
1. **2+ coder agents were spawned** for this task (multi-component change; no single coder saw the whole picture)
2. **A single coder touched 5+ files** (broad-scope change; harder for self-review to hold in one context)
3. **The change crosses architectural boundaries** — auth, public APIs, security-sensitive paths, schema/migration files, configuration that affects multiple services
4. **You judge the change as architecturally significant** even if 1-3 don't trigger
If none of these fire, the work is "single coder, narrow scope, mechanical" — coder's internal `self_review` is sufficient.
**Why this matters.** Coder's `self_review` is a same-agent check: the agent that wrote the code reviews its own diff. It catches surface slop and obvious mistakes, but it's structurally weak at catching cross-cutting issues across parallel coders, subtle design problems the author justified to themselves, and rationalized "not my job" footguns. `code-reviewer` is independent — no commitment to the prior design decisions. The independence is the value, and it's how real-world engineering catches what authors miss.
**Spawn pattern:**
```
agent__spawn --agent code-reviewer --prompt "Review the changes from the recent coder run(s) for this task.
Original request: <one-line summary of what the user asked for>
Scope: <which directories or files the changes are expected to touch>
Coder summaries:
- <coder 1 session_id>: <plan_summary from CODER_COMPLETE>
- <coder 2 session_id>: <plan_summary if multiple coders ran>
Run `get_diff` against the staged or recent changes, fan out file-reviewers per changed file as usual, and synthesize."
```
### Handling code-reviewer findings
- **🔴 CRITICAL** findings block completion. Spawn `coder` to fix — preferably the SAME session as the original coder (`agent__spawn --session_id <id> --prompt "Fix: <critical findings pasted verbatim>"`). Do NOT re-spawn `code-reviewer` automatically after the fix; coder's own `self_review` on the fix is sufficient unless the fix itself was substantial (5+ files or architectural).
- **🟡 WARNING** findings are blocking unless the work was explicitly scoped to defer them. If unsure, ASK the user via `user__ask` whether to fix or accept.
- **🟢 SUGGESTION / 💡 NITPICK** findings are informational. Surface them to the user with the final report. Do not block on them.
- **`Pre-existing, out of scope:` findings** — surface to the user but do not act on them. They predate this work and aren't the current task's responsibility.
### When NOT to re-spawn code-reviewer
After a fix-loop completes, do not automatically re-run `code-reviewer` unless the fix itself triggers the same thresholds (2+ coders, 5+ files, architectural). Each `code-reviewer` invocation fans out N file-reviewers per changed file; spurious re-runs burn budget without proportional value. Trust coder's `self_review` on bounded fixes.
## File Operations (Direct Edits)
When you write or modify files yourself (rather than delegating to coder):
- **For editing an existing file**, prefer `fs_patch`. It's a surgical edit that preserves unchanged content. Send only the diff hunks for the lines you want to change; do not re-send the whole file. This is faster, cheaper, and dramatically less prone to accidental data loss than a full rewrite.
- **For writing a NEW file or doing a COMPLETE rewrite**, use `fs_write`. Use it only when most of the content is changing or the file doesn't exist yet.
- **NEVER write files via `execute_command`.** Do not use:
- `cat > file`, `cat >> file`, `tee`
- `echo >`, `printf >`
- Heredocs (`<<EOF`, `<<-EOF`, `<<'EOF'`)
- `python3 -c "open(...).write(...)"` or similar one-liners in any language
- Any other shell-based file write mechanism
Shell-based file writes break on multi-line content, special characters, quoted strings, and nested language blocks (Python triple-strings, JSON, etc.). `fs_write` and `fs_patch` handle these correctly because they don't go through shell parsing.
- **For reading files**, prefer `fs_read` over `cat` via `execute_command`. `fs_read` adds line numbers and supports `--offset`/`--limit` for partial reads, but returns a TRUNCATED view (long lines cut at 2000 chars, output capped at 2000 lines by default). When you need the FULL untruncated file (e.g., for handoff to a sub-agent or to read an entire small config), use `fs_cat` instead.
- **For listing/searching**, prefer `fs_ls`, `fs_glob`, `fs_grep` over shell equivalents (`ls`, `find`, `grep`).
`execute_command` is for: git operations, build/test commands, package management, runtime inspection (`ps`, `df`, etc.) — anything where the shell IS the right interface.
## Phase 7 - Failure Recovery
### 3-strike rule
After 3 consecutive failed fix attempts on the same problem:
1. **STOP** all further edits immediately.
2. **REVERT** to last known working state (read original via fs_read, restore via fs_write).
3. **DOCUMENT** what was attempted and what failed.
4. **CONSULT Oracle** with full failure context.
5. If Oracle cannot resolve → **ASK USER** before proceeding.
Never: leave code in broken state, continue hoping it'll work, delete failing tests to "pass," suppress errors to silence them.
## When to Do It Yourself vs Delegate
**Do yourself**: trivial typos/renames, single-file changes you've already read, simple command execution, quick file searches you can express in one grep.
**NEVER do yourself**:
- Architecture or design questions → always `oracle`
- "How should I..." / "What's the best way to..." → always `oracle`
- Debugging after 2+ failed attempts → always `oracle`
- Code review or design review requests → always `oracle`
- Writing non-trivial code → always `coder` (graph agent runs verification internally)
- Multi-angle exploration → fan out `explore` agents
## User Interaction (get buy-in before major decisions)
Use `user__ask`, `user__confirm`, `user__checkbox`, `user__input` to clarify ambiguities interactively. **Do NOT guess when you can ask.**
| Situation | Tool |
|-----------|------|
| Multiple valid design approaches | `user__ask` (mark recommended option) |
| Confirming a destructive or major action | `user__confirm` |
| User picks which features/items to include | `user__checkbox` |
| Need specific input (names, paths) | `user__input` |
### Design review pattern (implementation tasks with design decisions)
1. Explore the codebase to understand existing patterns.
2. Formulate 2-3 design options based on findings.
3. Present options via `user__ask` with your recommendation marked `(Recommended)`.
4. Confirm chosen approach before delegating to `coder`.
5. Proceed with implementation.
Confirm before changes that touch 5+ files. Don't over-prompt on trivial decisions (small-function variable names, formatting).
## Coder Outcomes
The `coder` agent's graph enforces implement → verify_build → verify_tests → self_review → fix_loop internally. `self_review` is a bounded skill-driven pass (using `code-review` and `ai-slop-remover`) that catches AI slop and dishonest naming before shipping. It returns one of:
- `CODER_COMPLETE` — build + tests green. Continue with follow-up todos.
- `CODER_REJECTED` — user rejected the plan at the approval gate. Do NOT re-spawn blindly; ask the user what to change.
- `CODER_FAILED` — fix-loop exhausted. Failure output includes last build + test logs. Surface to user; consider spawning `oracle` for diagnosis. Resume the SAME coder session for fixes (`agent__spawn --session_id <id>`).
## Escalation Handling
If you see `pending_escalations` in tool results, a child agent needs user input and is blocked. Reply promptly via `agent__reply_escalation`. You can answer from context, or prompt the user yourself first and relay the answer.
## Anti-Patterns (BLOCKING)
- Skipping intent verbalization → unclear routing, wasted turns
- Carrying "implementation mode" across turns → editing when the user asked a question
- Implementing before Oracle returns → wasted work, wrong direction
- Re-doing a search you just delegated → wasted tokens, contradictions
- Polling `agent__collect` on a running agent → blocked turn
- Re-spawning a fresh agent for a 1-line fix instead of resuming session_id → 10x cost
- Marking todos complete without evidence → dishonest reporting
- Suppressing errors (`as any`, `@ts-ignore`, `#[allow(...)]`, empty catches) → hidden bugs
- 3 fix attempts without consulting Oracle → wasted budget
- Writing files via `execute_command` (heredocs, `cat >`, `echo >`, `printf >`) → file corruption from shell parsing
## Hard Blocks (NEVER violate)
- Suppress type errors → never
- Commit without explicit user request → never
- Speculate about unread code → never
- Leave code in broken state after failures → never
- Deliver final user answer with Oracle still running → never
- Write files via `execute_command` instead of `fs_write`/`fs_patch` → never
## Available Tools
{{__tools__}}
## Context
- Project: {{project_dir}}
- OS: {{__os__}}
- Shell: {{__shell__}}
- CWD: {{__cwd__}}
conversation_starters:
- 'Add a new feature to the project'
- 'Fix a bug in the codebase'
- 'Refactor the authentication module'
- 'Help me understand how X works'
-97
View File
@@ -1,97 +0,0 @@
#!/usr/bin/env bash
set -eo pipefail
# shellcheck disable=SC1090
source "$LLM_PROMPT_UTILS_FILE"
source "$LLM_ROOT_DIR/agents/.shared/utils.sh"
export AUTO_CONFIRM=true
# @env LLM_OUTPUT=/dev/stdout
# @env LLM_AGENT_VAR_PROJECT_DIR=.
# @describe Sisyphus orchestrator tools (project info, build, test)
_project_dir() {
local dir="${LLM_AGENT_VAR_PROJECT_DIR:-.}"
(cd "${dir}" 2>/dev/null && pwd) || echo "${dir}"
}
# @cmd Get project information and structure
get_project_info() {
local project_dir
project_dir=$(_project_dir)
info "Project: ${project_dir}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local project_info
project_info=$(detect_project "${project_dir}")
cat <<-EOF >> "$LLM_OUTPUT"
Type: $(echo "${project_info}" | jq -r '.type')
Build: $(echo "${project_info}" | jq -r '.build')
Test: $(echo "${project_info}" | jq -r '.test')
$(info "Directory structure:")
$(get_tree "${project_dir}" 2)
EOF
}
# @cmd Run build command for the project
run_build() {
local project_dir
project_dir=$(_project_dir)
local project_info
project_info=$(detect_project "${project_dir}")
local build_cmd
build_cmd=$(echo "${project_info}" | jq -r '.build')
if [[ -z "${build_cmd}" ]] || [[ "${build_cmd}" == "null" ]]; then
warn "No build command detected for this project" >> "$LLM_OUTPUT"
return 0
fi
info "Running: ${build_cmd}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local output
if output=$(cd "${project_dir}" && eval "${build_cmd}" 2>&1); then
green "BUILD SUCCESS" >> "$LLM_OUTPUT"
echo "${output}" >> "$LLM_OUTPUT"
return 0
else
error "BUILD FAILED" >> "$LLM_OUTPUT"
echo "${output}" >> "$LLM_OUTPUT"
return 1
fi
}
# @cmd Run tests for the project
run_tests() {
local project_dir
project_dir=$(_project_dir)
local project_info
project_info=$(detect_project "${project_dir}")
local test_cmd
test_cmd=$(echo "${project_info}" | jq -r '.test')
if [[ -z "${test_cmd}" ]] || [[ "${test_cmd}" == "null" ]]; then
warn "No test command detected for this project" >> "$LLM_OUTPUT"
return 0
fi
info "Running: ${test_cmd}" >> "$LLM_OUTPUT"
echo "" >> "$LLM_OUTPUT"
local output
if output=$(cd "${project_dir}" && eval "${test_cmd}" 2>&1); then
green "TESTS PASSED" >> "$LLM_OUTPUT"
echo "${output}" >> "$LLM_OUTPUT"
return 0
else
error "TESTS FAILED" >> "$LLM_OUTPUT"
echo "${output}" >> "$LLM_OUTPUT"
return 1
fi
}
+1106
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File diff suppressed because it is too large Load Diff
+20 -12
View File
@@ -1,23 +1,31 @@
{
"mcpServers": {
"github": {
"type": "http",
"url": "https://api.githubcopilot.com/mcp"
},
"atlassian": {
"type": "stdio",
"command": "npx",
"args": ["-y", "mcp-remote@latest", "https://mcp.atlassian.com/v1/mcp"]
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"GITHUB_PERSONAL_ACCESS_TOKEN",
"ghcr.io/github/github-mcp-server"
],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": "{{GITHUB_PERSONAL_ACCESS_TOKEN}}"
}
},
"docker": {
"type": "stdio",
"command": "uvx",
"args": ["mcp-server-docker"]
},
"ddg-search": {
"type": "stdio",
"command": "uvx",
"args": ["duckduckgo-mcp-server"]
"slack": {
"command": "npx",
"args": ["-y", "slack-mcp-server@latest", "--transport", "stdio"],
"env": {
"SLACK_MCP_XOXC_TOKEN": "{{SLACK_MCP_XOXC_TOKEN}}",
"SLACK_MCP_XOXD_TOKEN": "{{SLACK_MCP_XOXD_TOKEN}}",
"SLACK_MCP_ADD_MESSAGE_TOOL": true
}
}
}
}
+3 -10
View File
@@ -32,7 +32,7 @@ def main():
agent_data = parse_raw_data(raw_data)
root_dir = "{config_dir}"
setup_env(root_dir, agent_func, raw_data)
setup_env(root_dir, agent_func)
agent_tools_path = os.path.join(root_dir, "agents/{agent_name}/tools.py")
run(agent_tools_path, agent_func, agent_data)
@@ -50,13 +50,7 @@ def parse_raw_data(data):
def parse_argv():
agent_func = sys.argv[1]
tool_data_file = os.environ.get("LLM_TOOL_DATA_FILE")
if tool_data_file and os.path.isfile(tool_data_file):
with open(tool_data_file, "r", encoding="utf-8") as f:
agent_data = f.read()
else:
agent_data = sys.argv[2]
agent_data = sys.argv[2]
if (not agent_data) or (not agent_func):
print("Usage: ./{agent_name}.py <agent-func> <agent-data>", file=sys.stderr)
@@ -65,14 +59,13 @@ def parse_argv():
return agent_func, agent_data
def setup_env(root_dir, agent_func, raw_data):
def setup_env(root_dir, agent_func):
load_env(os.path.join(root_dir, ".env"))
os.environ["LLM_ROOT_DIR"] = root_dir
os.environ["LLM_AGENT_NAME"] = "{agent_name}"
os.environ["LLM_AGENT_FUNC"] = agent_func
os.environ["LLM_AGENT_ROOT_DIR"] = os.path.join(root_dir, "agents", "{agent_name}")
os.environ["LLM_AGENT_CACHE_DIR"] = os.path.join(root_dir, "cache", "{agent_name}")
os.environ["LLM_AGENT_RAW_JSON"] = raw_data
def load_env(file_path):
+4 -8
View File
@@ -14,11 +14,7 @@ main() {
parse_argv() {
agent_func="$1"
if [[ -n "$LLM_TOOL_DATA_FILE" ]] && [[ -f "$LLM_TOOL_DATA_FILE" ]]; then
agent_data="$(cat "$LLM_TOOL_DATA_FILE")"
else
agent_data="$2"
fi
agent_data="$2"
if [[ -z "$agent_data" ]] || [[ -z "$agent_func" ]]; then
die "usage: ./{agent_name}.sh <agent-func> <agent-data>"
fi
@@ -32,7 +28,6 @@ setup_env() {
export LLM_AGENT_ROOT_DIR="$LLM_ROOT_DIR/agents/{agent_name}"
export LLM_AGENT_CACHE_DIR="$LLM_ROOT_DIR/cache/{agent_name}"
export LLM_PROMPT_UTILS_FILE="{prompt_utils_file}"
export LLM_AGENT_RAW_JSON="$agent_data"
}
load_env() {
@@ -62,6 +57,7 @@ run() {
if [[ "$OS" == "Windows_NT" ]]; then
set -o igncr
tools_path="$(cygpath -w "$tools_path")"
tool_data="$(echo "$tool_data" | sed 's/\\/\\\\/g')"
fi
jq_script="$(cat <<-'EOF'
@@ -74,11 +70,11 @@ def to_args:
to_entries | .[] |
(.key | split("_") | join("-")) as $key |
if .value | type == "array" then
.value | .[] | "--\($key)=\(. | escape_shell_word)"
.value | .[] | "--\($key) \(. | escape_shell_word)"
elif .value | type == "boolean" then
if .value then "--\($key)" else "" end
else
"--\($key)=\(.value | escape_shell_word)"
"--\($key) \(.value | escape_shell_word)"
end;
[ to_args ] | join(" ")
EOF
-190
View File
@@ -1,190 +0,0 @@
#!/usr/bin/env tsx
// Usage: ./{agent_name}.ts <agent-func> <agent-data>
import { readFileSync, writeFileSync, existsSync } from "fs";
import { join } from "path";
import { pathToFileURL } from "url";
async function main(): Promise<void> {
const { agentFunc, rawData } = parseArgv();
const agentData = parseRawData(rawData);
const configDir = "{config_dir}";
setupEnv(configDir, agentFunc, rawData);
const agentToolsPath = join(configDir, "agents", "{agent_name}", "tools.ts");
await run(agentToolsPath, agentFunc, agentData);
}
function parseRawData(data: string): Record<string, unknown> {
if (!data) {
throw new Error("No JSON data");
}
try {
return JSON.parse(data);
} catch {
throw new Error("Invalid JSON data");
}
}
function parseArgv(): { agentFunc: string; rawData: string } {
const agentFunc = process.argv[2];
const toolDataFile = process.env["LLM_TOOL_DATA_FILE"];
let agentData: string;
if (toolDataFile && existsSync(toolDataFile)) {
agentData = readFileSync(toolDataFile, "utf-8");
} else {
agentData = process.argv[3];
}
if (!agentFunc || !agentData) {
process.stderr.write("Usage: ./{agent_name}.ts <agent-func> <agent-data>\n");
process.exit(1);
}
return { agentFunc, rawData: agentData };
}
function setupEnv(configDir: string, agentFunc: string, rawData: string): void {
loadEnv(join(configDir, ".env"));
process.env["LLM_ROOT_DIR"] = configDir;
process.env["LLM_AGENT_NAME"] = "{agent_name}";
process.env["LLM_AGENT_FUNC"] = agentFunc;
process.env["LLM_AGENT_ROOT_DIR"] = join(configDir, "agents", "{agent_name}");
process.env["LLM_AGENT_CACHE_DIR"] = join(configDir, "cache", "{agent_name}");
process.env["LLM_AGENT_RAW_JSON"] = rawData;
}
function loadEnv(filePath: string): void {
let lines: string[];
try {
lines = readFileSync(filePath, "utf-8").split("\n");
} catch {
return;
}
for (const raw of lines) {
const line = raw.trim();
if (line.startsWith("#") || !line) {
continue;
}
const eqIdx = line.indexOf("=");
if (eqIdx === -1) {
continue;
}
const key = line.slice(0, eqIdx).trim();
if (key in process.env) {
continue;
}
let value = line.slice(eqIdx + 1).trim();
if (
(value.startsWith('"') && value.endsWith('"')) ||
(value.startsWith("'") && value.endsWith("'"))
) {
value = value.slice(1, -1);
}
process.env[key] = value;
}
}
function extractParamNames(fn: Function): string[] {
const src = fn.toString();
const match = src.match(/^(?:async\s+)?function\s*\w*\s*\(([^)]*)\)/);
if (!match) {
return [];
}
return match[1]
.split(",")
.map((p) => p.trim().replace(/[:=?].*/s, "").trim())
.filter(Boolean);
}
function spreadArgs(
fn: Function,
data: Record<string, unknown>,
): unknown[] {
const names = extractParamNames(fn);
if (names.length === 0) {
return [];
}
return names.map((name) => data[name]);
}
async function run(
agentPath: string,
agentFunc: string,
agentData: Record<string, unknown>,
): Promise<void> {
const mod = await import(pathToFileURL(agentPath).href);
if (typeof mod[agentFunc] !== "function") {
throw new Error(`No module function '${agentFunc}' at '${agentPath}'`);
}
const fn = mod[agentFunc] as Function;
const args = spreadArgs(fn, agentData);
const value = await fn(...args);
returnToLlm(value);
dumpResult(`{agent_name}:${agentFunc}`);
}
function returnToLlm(value: unknown): void {
if (value === null || value === undefined) {
return;
}
const output = process.env["LLM_OUTPUT"];
const write = (s: string) => {
if (output) {
writeFileSync(output, s, "utf-8");
} else {
process.stdout.write(s);
}
};
if (typeof value === "string" || typeof value === "number" || typeof value === "boolean") {
write(String(value));
} else if (typeof value === "object") {
write(JSON.stringify(value, null, 2));
}
}
function dumpResult(name: string): void {
const dumpResults = process.env["LLM_DUMP_RESULTS"];
const llmOutput = process.env["LLM_OUTPUT"];
if (!dumpResults || !llmOutput || !process.stdout.isTTY) {
return;
}
try {
const pattern = new RegExp(`\\b(${dumpResults})\\b`);
if (!pattern.test(name)) {
return;
}
} catch {
return;
}
let data: string;
try {
data = readFileSync(llmOutput, "utf-8");
} catch {
return;
}
process.stdout.write(
`\x1b[2m----------------------\n${data}\n----------------------\x1b[0m\n`,
);
}
main().catch((err) => {
process.stderr.write(`${err}\n`);
process.exit(1);
});
+2 -8
View File
@@ -32,7 +32,7 @@ def main():
tool_data = parse_raw_data(raw_data)
root_dir = "{root_dir}"
setup_env(root_dir, raw_data)
setup_env(root_dir)
tool_path = "{tool_path}.py"
run(tool_path, "run", tool_data)
@@ -49,11 +49,6 @@ def parse_raw_data(data):
def parse_argv():
tool_data_file = os.environ.get("LLM_TOOL_DATA_FILE")
if tool_data_file and os.path.isfile(tool_data_file):
with open(tool_data_file, "r", encoding="utf-8") as f:
return f.read()
argv = sys.argv[:] + [None] * max(0, 2 - len(sys.argv))
tool_data = argv[1]
@@ -65,12 +60,11 @@ def parse_argv():
return tool_data
def setup_env(root_dir, raw_data):
def setup_env(root_dir):
load_env(os.path.join(root_dir, ".env"))
os.environ["LLM_ROOT_DIR"] = root_dir
os.environ["LLM_TOOL_NAME"] = "{function_name}"
os.environ["LLM_TOOL_CACHE_DIR"] = os.path.join(root_dir, "cache", "{function_name}")
os.environ["LLM_TOOL_RAW_JSON"] = raw_data
def load_env(file_path):
+4 -8
View File
@@ -13,11 +13,7 @@ main() {
}
parse_argv() {
if [[ -n "$LLM_TOOL_DATA_FILE" ]] && [[ -f "$LLM_TOOL_DATA_FILE" ]]; then
tool_data="$(cat "$LLM_TOOL_DATA_FILE")"
else
tool_data="$1"
fi
tool_data="$1"
if [[ -z "$tool_data" ]]; then
die "usage: ./{function_name}.sh <tool-data>"
fi
@@ -29,7 +25,6 @@ setup_env() {
export LLM_TOOL_NAME="{function_name}"
export LLM_TOOL_CACHE_DIR="$LLM_ROOT_DIR/cache/{function_name}"
export LLM_PROMPT_UTILS_FILE="{prompt_utils_file}"
export LLM_TOOL_RAW_JSON="$tool_data"
}
load_env() {
@@ -59,6 +54,7 @@ run() {
if [[ "$OS" == "Windows_NT" ]]; then
set -o igncr
tool_path="$(cygpath -w "$tool_path")"
tool_data="$(echo "$tool_data" | sed 's/\\/\\\\/g')"
fi
jq_script="$(cat <<-'EOF'
@@ -71,11 +67,11 @@ def to_args:
to_entries | .[] |
(.key | split("_") | join("-")) as $key |
if .value | type == "array" then
.value | .[] | "--\($key)=\(. | escape_shell_word)"
.value | .[] | "--\($key) \(. | escape_shell_word)"
elif .value | type == "boolean" then
if .value then "--\($key)" else "" end
else
"--\($key)=\(.value | escape_shell_word)"
"--\($key) \(.value | escape_shell_word)"
end;
[ to_args ] | join(" ")
EOF
-185
View File
@@ -1,185 +0,0 @@
#!/usr/bin/env tsx
// Usage: ./{function_name}.ts <tool-data>
import { readFileSync, writeFileSync, existsSync } from "fs";
import { join } from "path";
import { pathToFileURL } from "url";
async function main(): Promise<void> {
const rawData = parseArgv();
const toolData = parseRawData(rawData);
const rootDir = "{root_dir}";
setupEnv(rootDir, rawData);
const toolPath = "{tool_path}.ts";
await run(toolPath, "run", toolData);
}
function parseRawData(data: string): Record<string, unknown> {
if (!data) {
throw new Error("No JSON data");
}
try {
return JSON.parse(data);
} catch {
throw new Error("Invalid JSON data");
}
}
function parseArgv(): string {
const toolDataFile = process.env["LLM_TOOL_DATA_FILE"];
if (toolDataFile && existsSync(toolDataFile)) {
return readFileSync(toolDataFile, "utf-8");
}
const toolData = process.argv[2];
if (!toolData) {
process.stderr.write("Usage: ./{function_name}.ts <tool-data>\n");
process.exit(1);
}
return toolData;
}
function setupEnv(rootDir: string, rawData: string): void {
loadEnv(join(rootDir, ".env"));
process.env["LLM_ROOT_DIR"] = rootDir;
process.env["LLM_TOOL_NAME"] = "{function_name}";
process.env["LLM_TOOL_CACHE_DIR"] = join(rootDir, "cache", "{function_name}");
process.env["LLM_TOOL_RAW_JSON"] = rawData;
}
function loadEnv(filePath: string): void {
let lines: string[];
try {
lines = readFileSync(filePath, "utf-8").split("\n");
} catch {
return;
}
for (const raw of lines) {
const line = raw.trim();
if (line.startsWith("#") || !line) {
continue;
}
const eqIdx = line.indexOf("=");
if (eqIdx === -1) {
continue;
}
const key = line.slice(0, eqIdx).trim();
if (key in process.env) {
continue;
}
let value = line.slice(eqIdx + 1).trim();
if (
(value.startsWith('"') && value.endsWith('"')) ||
(value.startsWith("'") && value.endsWith("'"))
) {
value = value.slice(1, -1);
}
process.env[key] = value;
}
}
function extractParamNames(fn: Function): string[] {
const src = fn.toString();
const match = src.match(/^(?:async\s+)?function\s*\w*\s*\(([^)]*)\)/);
if (!match) {
return [];
}
return match[1]
.split(",")
.map((p) => p.trim().replace(/[:=?].*/s, "").trim())
.filter(Boolean);
}
function spreadArgs(
fn: Function,
data: Record<string, unknown>,
): unknown[] {
const names = extractParamNames(fn);
if (names.length === 0) {
return [];
}
return names.map((name) => data[name]);
}
async function run(
toolPath: string,
toolFunc: string,
toolData: Record<string, unknown>,
): Promise<void> {
const mod = await import(pathToFileURL(toolPath).href);
if (typeof mod[toolFunc] !== "function") {
throw new Error(`No module function '${toolFunc}' at '${toolPath}'`);
}
const fn = mod[toolFunc] as Function;
const args = spreadArgs(fn, toolData);
const value = await fn(...args);
returnToLlm(value);
dumpResult("{function_name}");
}
function returnToLlm(value: unknown): void {
if (value === null || value === undefined) {
return;
}
const output = process.env["LLM_OUTPUT"];
const write = (s: string) => {
if (output) {
writeFileSync(output, s, "utf-8");
} else {
process.stdout.write(s);
}
};
if (typeof value === "string" || typeof value === "number" || typeof value === "boolean") {
write(String(value));
} else if (typeof value === "object") {
write(JSON.stringify(value, null, 2));
}
}
function dumpResult(name: string): void {
const dumpResults = process.env["LLM_DUMP_RESULTS"];
const llmOutput = process.env["LLM_OUTPUT"];
if (!dumpResults || !llmOutput || !process.stdout.isTTY) {
return;
}
try {
const pattern = new RegExp(`\\b(${dumpResults})\\b`);
if (!pattern.test(name)) {
return;
}
} catch {
return;
}
let data: string;
try {
data = readFileSync(llmOutput, "utf-8");
} catch {
return;
}
process.stdout.write(
`\x1b[2m----------------------\n${data}\n----------------------\x1b[0m\n`,
);
}
main().catch((err) => {
process.stderr.write(`${err}\n`);
process.exit(1);
});
+10 -23
View File
@@ -1,7 +1,6 @@
import os
from typing import List, Literal, Optional
def run(
string: str,
string_enum: Literal["foo", "bar"],
@@ -10,38 +9,26 @@ def run(
number: float,
array: List[str],
string_optional: Optional[str] = None,
integer_with_default: int = 42,
boolean_with_default: bool = True,
number_with_default: float = 3.14,
string_with_default: str = "hello",
array_optional: Optional[List[str]] = None,
):
"""Demonstrates all supported Python parameter types and variations.
"""Demonstrates how to create a tool using Python and how to use comments.
Args:
string: A required string property
string_enum: A required string property constrained to specific values
boolean: A required boolean property
integer: A required integer property
number: A required number (float) property
array: A required string array property
string_optional: An optional string property (Optional[str] with None default)
integer_with_default: An optional integer with a non-None default value
boolean_with_default: An optional boolean with a default value
number_with_default: An optional number with a default value
string_with_default: An optional string with a default value
array_optional: An optional string array property
string: Define a required string property
string_enum: Define a required string property with enum
boolean: Define a required boolean property
integer: Define a required integer property
number: Define a required number property
array: Define a required string array property
string_optional: Define an optional string property
array_optional: Define an optional string array property
"""
output = f"""string: {string}
string_enum: {string_enum}
string_optional: {string_optional}
boolean: {boolean}
integer: {integer}
number: {number}
array: {array}
string_optional: {string_optional}
integer_with_default: {integer_with_default}
boolean_with_default: {boolean_with_default}
number_with_default: {number_with_default}
string_with_default: {string_with_default}
array_optional: {array_optional}"""
for key, value in os.environ.items():
-53
View File
@@ -1,53 +0,0 @@
/**
* Demonstrates all supported TypeScript parameter types and variations.
*
* @param string - A required string property
* @param string_enum - A required string property constrained to specific values
* @param boolean - A required boolean property
* @param number - A required number property
* @param array_bracket - A required string array using bracket syntax
* @param array_generic - A required string array using generic syntax
* @param string_optional - An optional string using the question mark syntax
* @param string_nullable - An optional string using the union-with-null syntax
* @param number_with_default - An optional number with a default value
* @param boolean_with_default - An optional boolean with a default value
* @param string_with_default - An optional string with a default value
* @param array_optional - An optional string array using the question mark syntax
*/
export function run(
string: string,
string_enum: "foo" | "bar",
boolean: boolean,
number: number,
array_bracket: string[],
array_generic: Array<string>,
string_optional?: string,
string_nullable: string | null = null,
number_with_default: number = 42,
boolean_with_default: boolean = true,
string_with_default: string = "hello",
array_optional?: string[],
): string {
const parts = [
`string: ${string}`,
`string_enum: ${string_enum}`,
`boolean: ${boolean}`,
`number: ${number}`,
`array_bracket: ${JSON.stringify(array_bracket)}`,
`array_generic: ${JSON.stringify(array_generic)}`,
`string_optional: ${string_optional}`,
`string_nullable: ${string_nullable}`,
`number_with_default: ${number_with_default}`,
`boolean_with_default: ${boolean_with_default}`,
`string_with_default: ${string_with_default}`,
`array_optional: ${JSON.stringify(array_optional)}`,
];
for (const [key, value] of Object.entries(process.env)) {
if (key.startsWith("LLM_")) {
parts.push(`${key}: ${value}`);
}
}
return parts.join("\n");
}
+2 -10
View File
@@ -1,7 +1,7 @@
#!/usr/bin/env bash
set -e
# @describe Execute the shell command. DO NOT use this to write files — use fs_write (new files) or fs_patch (edits) instead. Shell-based file writes (cat >, echo >, printf >, tee, heredocs, python -c "open(...)") break on multi-line content, special characters, quoted strings, and nested language blocks.
# @describe Execute the shell command.
# @option --command! The command to execute.
# @env LLM_OUTPUT=/dev/stdout The output path
@@ -10,15 +10,7 @@ set -e
source "$LLM_PROMPT_UTILS_FILE"
main() {
# shellcheck disable=SC2154
argc_command="$(jq -r '.command' <<< "$LLM_TOOL_RAW_JSON")"
guard_operation
local script
script="$(mktemp)"
# shellcheck disable=SC2064
trap "rm -f '$script'" EXIT
# shellcheck disable=SC2154
printf '%s\n' "$argc_command" > "$script"
bash -e -o pipefail "$script" >> "$LLM_OUTPUT"
eval "$argc_command" >> "$LLM_OUTPUT"
}
@@ -14,8 +14,6 @@ source "$LLM_PROMPT_UTILS_FILE"
# shellcheck disable=SC2154
main() {
argc_code="$(jq -r '.code' <<< "$LLM_TOOL_RAW_JSON")"
if ! grep -qi '^select' <<<"$argc_code"; then
guard_operation ""
fi
+1 -1
View File
@@ -10,5 +10,5 @@ set -e
main() {
# shellcheck disable=SC2154
cat "$argc_path" >> "$LLM_OUTPUT" 2>&1 || echo "No such file or path: $argc_path" >> "$LLM_OUTPUT"
cat "$argc_path" >> "$LLM_OUTPUT"
}
-59
View File
@@ -1,59 +0,0 @@
#!/usr/bin/env bash
set -e
# @describe Find files by glob pattern. Returns matching file paths sorted by modification time.
# Use this to discover files before reading them.
# @option --pattern! The glob pattern to match files against (e.g. "**/*.rs", "src/**/*.ts", "*.yaml")
# @option --path The directory to search in (defaults to current working directory)
# @env LLM_OUTPUT=/dev/stdout The output path
MAX_RESULTS=100
main() {
# shellcheck disable=SC2154
local glob_pattern="$argc_pattern"
local search_path="${argc_path:-.}"
if [[ ! -d "$search_path" ]]; then
echo "Error: directory not found: $search_path" >> "$LLM_OUTPUT"
return 1
fi
local results
if command -v fd &>/dev/null; then
results=$(fd --type f --glob "$glob_pattern" "$search_path" \
--exclude '.git' \
--exclude 'node_modules' \
--exclude 'target' \
--exclude 'dist' \
--exclude '__pycache__' \
--exclude 'vendor' \
--exclude '.build' \
2>/dev/null | head -n "$MAX_RESULTS") || true
else
results=$(find "$search_path" -type f -name "$glob_pattern" \
-not -path '*/.git/*' \
-not -path '*/node_modules/*' \
-not -path '*/target/*' \
-not -path '*/dist/*' \
-not -path '*/__pycache__/*' \
-not -path '*/vendor/*' \
-not -path '*/.build/*' \
2>/dev/null | head -n "$MAX_RESULTS") || true
fi
if [[ -z "$results" ]]; then
echo "No files found matching: $glob_pattern" >> "$LLM_OUTPUT"
return 0
fi
echo "$results" >> "$LLM_OUTPUT"
local count
count=$(echo "$results" | wc -l)
if [[ "$count" -ge "$MAX_RESULTS" ]]; then
printf "\n(Results limited to %s files. Use a more specific pattern.)\n" "$MAX_RESULTS" >> "$LLM_OUTPUT"
fi
}
-78
View File
@@ -1,78 +0,0 @@
#!/usr/bin/env bash
set -e
# @describe Search file contents using regular expressions. Returns matching file paths and lines.
# Use this to find relevant code before reading files. Much faster than reading files to search.
# --path accepts either a directory (recursive search with exclude rules applied) or a single file.
# @option --pattern! The regex pattern to search for in file contents
# @option --path The directory OR file to search in (defaults to current working directory)
# @option --include File pattern to filter by (e.g. "*.rs", "*.{ts,tsx}", "*.py"). Ignored when --path is a single file.
# @env LLM_OUTPUT=/dev/stdout The output path
MAX_RESULTS=50
MAX_LINE_LENGTH=2000
main() {
# shellcheck disable=SC2154
local search_pattern="$argc_pattern"
local search_path="${argc_path:-.}"
local include_filter="${argc_include:-}"
if [[ ! -e "$search_path" ]]; then
echo "Error: path not found: $search_path" >> "$LLM_OUTPUT"
return 1
fi
local grep_args=(-nH --color=never)
if [[ -d "$search_path" ]]; then
# Use -r (not -R) so symlinks to directories are NOT followed - this avoids
# infinite loops on pathological symlink cycles (e.g. `ln -s . loop`).
grep_args+=(-r)
grep_args+=(
--exclude-dir='.git'
--exclude-dir='node_modules'
--exclude-dir='target'
--exclude-dir='dist'
--exclude-dir='build'
--exclude-dir='__pycache__'
--exclude-dir='vendor'
--exclude-dir='.build'
--exclude-dir='.next'
--exclude='*.min.js'
--exclude='*.min.css'
--exclude='*.map'
--exclude='*.lock'
--exclude='package-lock.json'
)
if [[ -n "$include_filter" ]]; then
grep_args+=("--include=$include_filter")
fi
fi
# If --path is a single file, --include and the exclude rules are ignored
# (they only matter when recursing into a directory tree).
local results
results=$(grep "${grep_args[@]}" -E "$search_pattern" "$search_path" 2>/dev/null | head -n "$MAX_RESULTS") || true
if [[ -z "$results" ]]; then
echo "No matches found for: $search_pattern" >> "$LLM_OUTPUT"
return 0
fi
echo "$results" | while IFS= read -r line; do
if [[ ${#line} -gt $MAX_LINE_LENGTH ]]; then
line="${line:0:$MAX_LINE_LENGTH}... (truncated)"
fi
echo "$line"
done >> "$LLM_OUTPUT"
local count
count=$(echo "$results" | wc -l)
if [[ "$count" -ge "$MAX_RESULTS" ]]; then
printf "\n(Results limited to %s matches. Narrow your search with --include or a more specific pattern.)\n" "$MAX_RESULTS" >> "$LLM_OUTPUT"
fi
}
+1 -1
View File
@@ -9,5 +9,5 @@ set -e
main() {
# shellcheck disable=SC2154
ls -1 "$argc_path" >> "$LLM_OUTPUT" 2>&1 || echo "No such path: $argc_path" >> "$LLM_OUTPUT"
ls -1 "$argc_path" >> "$LLM_OUTPUT"
}
+2 -7
View File
@@ -1,10 +1,8 @@
#!/usr/bin/env bash
set -e
# @describe Apply a unified-diff patch to a file at the specified path. Use this for editing an existing file. It's the
# PREFERRED way to modify a file. Prefer this over fs_write whenever the file already exists: it sends less data,
# preserves unchanged content automatically, and is less prone to accidental data loss from full rewrites.
# Use fs_write only when you are creating a new file or doing a complete rewrite where most of the content changes.
# @describe Apply a patch to a file at the specified path.
# This can be used to edit a file without having to rewrite the whole file.
# @option --path! The path of the file to apply the patch to
# @option --contents! The patch to apply to the file
@@ -16,9 +14,6 @@ source "$LLM_PROMPT_UTILS_FILE"
# shellcheck disable=SC2154
main() {
argc_contents="$(jq -r '.contents' <<< "$LLM_TOOL_RAW_JSON")"
argc_path="$(jq -r '.path' <<< "$LLM_TOOL_RAW_JSON")"
if [[ ! -f "$argc_path" ]]; then
error "Unable to find the specified file: $argc_path"
exit 1
-64
View File
@@ -1,64 +0,0 @@
#!/usr/bin/env bash
set -e
# @describe Read a TRUNCATED view of a file with line numbers, offset, and limit. For directories, lists entries.
# IMPORTANT: This tool truncates output — lines over 2000 chars are cut off, and output is capped at 2000 lines by default.
# If you need the FULL, untruncated contents of a file, use fs_cat instead.
# Use this tool when you want line numbers, want to read a specific section via --offset/--limit, or are scanning a large file.
# Use the grep tool to find specific content before reading, then read with offset to target the relevant section.
# @option --path! The absolute path to the file or directory to read
# @option --offset The line number to start reading from (1-indexed, default: 1)
# @option --limit The maximum number of lines to read (default: 2000)
# @env LLM_OUTPUT=/dev/stdout The output path
MAX_LINE_LENGTH=2000
MAX_BYTES=51200
main() {
# shellcheck disable=SC2154
local target="$argc_path"
local offset="${argc_offset:-1}"
local limit="${argc_limit:-2000}"
if [[ ! -e "$target" ]]; then
echo "Error: path not found: $target" >> "$LLM_OUTPUT"
return 1
fi
if [[ -d "$target" ]]; then
ls -1 "$target" >> "$LLM_OUTPUT" 2>&1
return 0
fi
local total_lines file_bytes
total_lines=$(wc -l < "$target" 2>/dev/null || echo 0)
file_bytes=$(wc -c < "$target" 2>/dev/null || echo 0)
if [[ "$file_bytes" -gt "$MAX_BYTES" ]] && [[ "$offset" -eq 1 ]] && [[ "$limit" -ge 2000 ]]; then
{
echo "Warning: Large file (${file_bytes} bytes, ${total_lines} lines). Showing first ${limit} lines."
echo "Use --offset and --limit to read specific sections, or use the grep tool to find relevant lines first."
echo ""
} >> "$LLM_OUTPUT"
fi
local end_line=$((offset + limit - 1))
sed -n "${offset},${end_line}p" "$target" 2>/dev/null | {
local line_num=$offset
while IFS= read -r line; do
if [[ ${#line} -gt $MAX_LINE_LENGTH ]]; then
line="${line:0:$MAX_LINE_LENGTH}... (truncated)"
fi
printf "%d: %s\n" "$line_num" "$line"
line_num=$((line_num + 1))
done
} >> "$LLM_OUTPUT"
if [[ "$end_line" -lt "$total_lines" ]]; then
echo "" >> "$LLM_OUTPUT"
echo "(${total_lines} total lines. Use --offset $((end_line + 1)) to read more.)" >> "$LLM_OUTPUT"
fi
}
+1 -6
View File
@@ -1,9 +1,7 @@
#!/usr/bin/env bash
set -e
# @describe Write the FULL file contents to a file at the specified path. Use this for NEW files or COMPLETE rewrites
# only. For editing an existing file, prefer fs_patch. It's a surgical edit that preserves unchanged content, requires
# sending less data, and is less prone to accidental data loss.
# @describe Write the full file contents to a file at the specified path.
# @option --path! The path of the file to write to
# @option --contents! The full contents to write to the file
@@ -15,9 +13,6 @@ source "$LLM_PROMPT_UTILS_FILE"
# shellcheck disable=SC2154
main() {
argc_contents="$(jq -r '.contents' <<< "$LLM_TOOL_RAW_JSON")"
argc_path="$(jq -r '.path' <<< "$LLM_TOOL_RAW_JSON")"
if [[ -f "$argc_path" ]]; then
printf "%s" "$argc_contents" | git diff --no-index "$argc_path" - || true
guard_operation "Apply changes?"
@@ -1,24 +0,0 @@
#!/usr/bin/env tsx
import { appendFileSync, mkdirSync } from "fs";
import { dirname } from "path";
/**
* Get the current weather in a given location
* @param location - The city and optionally the state or country (e.g., "London", "San Francisco, CA").
*/
export async function run(location: string): string {
const encoded = encodeURIComponent(location);
const url = `https://wttr.in/${encoded}?format=4`;
const resp = await fetch(url);
const data = await resp.text();
const dest = process.env["LLM_OUTPUT"] ?? "/dev/stdout";
if (dest !== "-" && dest !== "/dev/stdout") {
mkdirSync(dirname(dest), { recursive: true });
appendFileSync(dest, data, "utf-8");
}
return data;
}
+11
View File
@@ -0,0 +1,11 @@
#!/usr/bin/env bash
set -e
# @meta require-tools jira
# @describe Query for jira issues using a Jira Query Language (JQL) query
# @option --jql-query! The Jira Query Language query to execute
# @env LLM_OUTPUT=/dev/stdout The output path
main() {
jira issue ls -q "$argc_jql_query" --plain >> "$LLM_OUTPUT"
}
-4
View File
@@ -14,10 +14,6 @@ set -e
# shellcheck disable=SC2154
main() {
argc_recipient="$(jq -r '.recipient' <<< "$LLM_TOOL_RAW_JSON")"
argc_subject="$(jq -r '.subject' <<< "$LLM_TOOL_RAW_JSON")"
argc_body="$(jq -r '.body' <<< "$LLM_TOOL_RAW_JSON")"
sender_name="${EMAIL_SENDER_NAME:-$(echo "$EMAIL_SMTP_USER" | awk -F'@' '{print $1}')}"
printf "%s\n" "From: $sender_name <$EMAIL_SMTP_USER>
To: $argc_recipient
@@ -6,11 +6,11 @@ set -e
# @option --query! The search query.
# @meta require-tools coyote
# @meta require-tools loki
# @env WEB_SEARCH_MODEL=gemini:gemini-2.5-flash The model for web-searching.
#
# supported coyote models:
# supported loki models:
# - gemini:gemini-2.0-*
# - vertexai:gemini-*
# - perplexity:*
@@ -22,15 +22,15 @@ main() {
client="${WEB_SEARCH_MODEL%%:*}"
if [[ "$client" == "gemini" ]]; then
export COYOTE_PATCH_GEMINI_CHAT_COMPLETIONS='{".*":{"body":{"tools":[{"google_search":{}}]}}}'
export LOKI_PATCH_GEMINI_CHAT_COMPLETIONS='{".*":{"body":{"tools":[{"google_search":{}}]}}}'
elif [[ "$client" == "vertexai" ]]; then
export COYOTE_PATCH_VERTEXAI_CHAT_COMPLETIONS='{
export LOKI_PATCH_VERTEXAI_CHAT_COMPLETIONS='{
"gemini-1.5-.*":{"body":{"tools":[{"googleSearchRetrieval":{}}]}},
"gemini-2.0-.*":{"body":{"tools":[{"google_search":{}}]}}
}'
elif [[ "$client" == "ernie" ]]; then
export COYOTE_PATCH_ERNIE_CHAT_COMPLETIONS='{".*":{"body":{"web_search":{"enable":true}}}}'
export LOKI_PATCH_ERNIE_CHAT_COMPLETIONS='{".*":{"body":{"web_search":{"enable":true}}}}'
fi
coyote -m "$WEB_SEARCH_MODEL" "$argc_query" >> "$LLM_OUTPUT"
loki -m "$WEB_SEARCH_MODEL" "$argc_query" >> "$LLM_OUTPUT"
}
+20 -21
View File
@@ -121,7 +121,7 @@ _cursor_blink_off() {
}
_cursor_to() {
echo -en "\033[$1;${2:-1}H" >&2
echo -en "\033[$1;$2H" >&2
}
# shellcheck disable=SC2154
@@ -133,7 +133,7 @@ _key_input() {
_read_stdin -rsn2 b
fi
declare input="${a}${b:-}"
declare input="${a}${b}"
case "$input" in
"${ESC}[A" | "k") echo up ;;
"${ESC}[B" | "j") echo down ;;
@@ -506,16 +506,14 @@ open_link() {
}
guard_operation() {
if [[ -z "$AUTO_CONFIRM" && -z "$LLM_AGENT_VAR_AUTO_CONFIRM" ]]; then
# 2>/dev/tty: keep the prompt off the host-captured stderr pipe so it
# can't leak into tool_call_error JSON when the wrapped command fails.
ans="$(confirm "${1:-Are you sure you want to continue?}" 2>/dev/tty)"
if [[ -t 1 ]]; then
ans="$(confirm "${1:-Are you sure you want to continue?}")"
if [[ "$ans" == 0 ]]; then
error "Operation aborted!" 2>&1
exit 1
fi
fi
if [[ "$ans" == 0 ]]; then
error "Operation aborted!" 2>&1
exit 1
fi
fi
}
# Here is an example of a patch block that can be applied to modify the file to request the user's name:
@@ -655,18 +653,19 @@ guard_path() {
exit 1
fi
path="$(_to_real_path "$1")"
confirmation_prompt="$2"
if [[ -t 1 ]]; then
path="$(_to_real_path "$1")"
confirmation_prompt="$2"
if [[ ! "$path" == "$(pwd)"* && -z "$AUTO_CONFIRM" && -z "$LLM_AGENT_VAR_AUTO_CONFIRM" ]]; then
# 2>/dev/tty: see guard_operation — prevents prompt text leaking via captured stderr.
ans="$(confirm "$confirmation_prompt" 2>/dev/tty)"
if [[ ! "$path" == "$(pwd)"* ]]; then
ans="$(confirm "$confirmation_prompt")"
if [[ "$ans" == 0 ]]; then
error "Operation aborted!" >&2
exit 1
fi
fi
if [[ "$ans" == 0 ]]; then
error "Operation aborted!" >&2
exit 1
fi
fi
fi
}
_to_real_path() {
File diff suppressed because it is too large Load Diff
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@@ -1,8 +0,0 @@
---
enabled_mcp_servers: atlassian
---
You are the librarian for the company's Confluence and Jira knowledge bases. Your job is to help users find and retrieve
information from these platforms. Use all tools at your disposal to answer user queries.
Available Tools:
{{__tools__}}
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@@ -1,6 +1,3 @@
---
skills_enabled: false
---
As a professional Prompt Engineer, your role is to create effective and innovative prompts for interacting with AI models.
Your core skills include:
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@@ -1,6 +1,3 @@
---
skills_enabled: false
---
Create a concise, 3-6 word title.
**Notes**:
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@@ -1,6 +1,3 @@
---
skills_enabled: false
---
Provide a terse, single sentence description of the given shell command.
Describe each argument and option of the command.
Provide short responses in about 80 words.
+1 -1
View File
@@ -9,7 +9,7 @@ security/configuration settings. The analysis aims to ensure a thorough understa
structured and operates, enabling the creation of new files, maintaining consistency with existing practices, and the
potential implementation of best practices.
Should the root directory contain a `COYOTE.md` file, this was generated by Coyote and should be used as a reference
Should the root directory contain a `LOKI.md` file, this was generated by Loki and should be used as a reference
point for all analysis, style questions, etc.
**Objective:** Enable the AI to thoroughly analyze a software repository, providing detailed insights and guidelines on
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@@ -1,6 +1,3 @@
---
skills_enabled: false
---
Provide only {{__shell__}} commands for {{__os_distro__}} without any description.
Ensure the output is a valid {{__shell__}} command.
If there is a lack of details, provide most logical solution.
+1
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@@ -1,5 +1,6 @@
---
enabled_mcp_servers: slack
temperature: 0.2
---
You are an expert Slack assistant designed to assist with Slack workspaces via the slack MCP server.
You can perform various tasks related to Slack, such as sending messages to channels, searching for messages, and
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@@ -1,39 +0,0 @@
---
description: Detect and remove AI slop from code and prose; produce output indistinguishable from a senior engineer's.
---
You are reviewing or generating content. Apply these standards strictly. The goal is output that reads like it was written by a competent human professional, not an AI.
## Code
**No useless comments.** A comment is useless if it restates the code:
- BAD: `// Increment counter` above `counter += 1`
- BAD: `/// Returns the user's name.` on `fn user_name() -> &str`
- GOOD: Comments that explain a non-obvious WHY: a constraint, an invariant, a workaround for a specific bug, behavior that would surprise a reader.
If removing a comment wouldn't confuse a future reader, the comment shouldn't exist.
**No emojis** unless the user explicitly asked for them.
**No defensive handling for impossible cases.** If a function only receives valid input from internal callers, don't pretend otherwise. Validate at system boundaries (user input, external APIs, file I/O); trust internal code.
**No over-engineering for hypothetical futures.** Three similar lines of code is fine. Premature abstractions are worse than duplication.
**No backwards-compatibility cruft for unreleased code.** If a function isn't called yet, just change it. Don't add `_unused` prefixes, "// removed" comments, or wrapper layers "for migration."
**Names should be honest.** A function called `get_user` should not mutate state. A field called `count` should not be a function. A method that can fail should return `Result`, not panic.
## Prose
**No flattery.** Don't start with "Great question!" or "That's a really good idea!" Just respond.
**No filler.** "It's important to note that" — delete. "Let me explain" — just explain. "I'll go ahead and" — just do it.
**No status updates.** "I'm going to help you with that" — just help.
**Match the user's terseness.** Brief user, brief reply. Detailed user, detailed reply.
**No multi-paragraph docstrings.** One short line max. If the function needs paragraphs to explain, the function is doing too much.
## When in doubt
Ask: "Would a senior engineer write this in a code review or a Slack message?" If not, cut it.
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@@ -1,124 +0,0 @@
---
description: Conduct a thorough code review focused on correctness, clarity, tests, and footguns. Grants read-only filesystem access for inspecting code.
enabled_tools: fs_read, fs_grep, fs_glob, fs_cat, fs_ls
---
You are reviewing code. Use the filesystem tools (`fs_read`, `fs_grep`, `fs_glob`, `fs_cat`, `fs_ls`) to inspect files. Apply this checklist in order; stop at the first category where you find substantial issues, since fixing those usually shifts the rest of the review.
## Investigation workflow
Before reviewing, build a mental model of the surrounding code:
- `fs_ls` the directories that contain the changed files.
- `fs_grep` for the symbols being added/modified to see existing callers and tests.
- `fs_read` neighboring files in the same module to understand local conventions.
- `fs_glob` for test files that might cover this area.
A review without context is just a syntax check.
## Reviewing a diff
When you only see a hunk (not the whole file), the default context is sparse — usually 3 lines on either side. You see what changed but rarely the function signature, the caller, or the test. Read deliberately to recover what the diff omits.
### Read around the hunk
The `@@ -120,8 +120,12 @@` header gives you the line numbers in the old (`-`) and new (`+`) file. Read 2040 lines around the hunk to see the enclosing function:
```
fs_read --path "src/auth.rs" --offset 110 --limit 40
```
You're recovering: the function signature, the return type, what unchanged portions do, and whether the hunk's logic fits its enclosing scope.
### Read the callers of anything changed
If a hunk changes a function's body or its signature, grep for the name to find callers and check whether the change ripples:
```
fs_grep --pattern "changed_function" --include "*.rs"
```
Skip the test files in this search; do the test sweep next.
### Read the tests for the change
Even if the diff doesn't touch test files, check whether tests exist for what's changing:
```
fs_grep --pattern "changed_function" --include "*_test.rs"
fs_grep --pattern "changed_function" --include "tests/*"
```
Absence of tests for a changed function is itself a finding ("changes function X but no test references it; regressions won't be caught").
### Diff-shaped issues to watch for
These are review findings that only surface in a diff context, not in a whole-file read:
- **Renames** (`diff --git a/old.rs b/new.rs`) — `fs_grep` for the old path to find imports that need updating but weren't.
- **Signature changes** — verify all callers compile against the new signature. Compiler-checked languages catch some of this; dynamic languages don't.
- **New code path without new tests** — usually a missing test. Flag it.
- **Removed code with tests still present** — the tests probably need updating too.
- **The "dog that didn't bark"** — what's obvious by its ABSENCE? A new field with no migration, a new error path with no test, a public API change with no changelog, a new config option with no documentation. Flag these as missing pieces, not as things to add later.
### Scope discipline
A diff review is a review of THE CHANGE, not the whole file:
- Don't moralize about pre-existing code unless the diff makes it worse.
- Don't suggest refactors outside the scope of the change. ("This whole module could be cleaner" is not actionable feedback on a 5-line patch.)
- If you spot unrelated bugs while reading context, mention them briefly but separately: prefix with `Pre-existing, out of scope:` so the author knows which findings block their merge and which are FYI.
- The author's job is to ship THIS change. Your job is to catch what's wrong with THIS change.
## 1. Correctness
- Does the change actually do what it claims? Does it solve the stated problem?
- Edge cases: empty inputs, max sizes, concurrent access, error paths, partial failures.
- Off-by-one errors, type confusion, null/None handling, integer overflow.
- Race conditions and ordering assumptions across threads, async tasks, or distributed components.
- Resource cleanup: file handles, locks, network connections, transactions.
## 2. Tests
- Do the tests test BEHAVIOR, not implementation? (Tests of `private_helper()` are usually a smell.)
- Will they fail when the code regresses? Or are they tautological (e.g., `assert!(x.is_empty() || !x.is_empty())`)?
- Do they cover the unhappy paths, not just the happy ones?
- Is there a missing test for the specific bug or feature being added? `fs_grep` for the function name in test files to check.
## 3. Clarity
- Are names accurate? `get_user` that mutates is a lie; rename or split.
- Could a competent reader understand this without comments?
- Is there a simpler way to express the same logic?
- Is the function doing one thing, or several things glued together?
## 4. Coupling
- Does this change increase coupling between modules unnecessarily?
- Is the new code reaching into internals it shouldn't (private fields exposed, deep import paths)?
- Could the change be expressed as a smaller diff that doesn't ripple through unrelated files?
## 5. Footguns
- Could a future maintainer easily misuse this API?
- Are invariants enforced by types, or just by convention?
- Are error types specific enough to be actionable?
- Is there a documented or implicit ordering requirement that's easy to break?
## What to flag
- Correctness bugs.
- Missing error handling at trust boundaries.
- Race conditions.
- Tests that won't catch regressions.
- Security issues (injection, auth, exposed secrets).
## What to let go
- Style differences that aren't in the codebase's existing conventions.
- "I would have done it differently" preferences.
- Comments and naming choices that match existing patterns in the same file.
- Micro-optimizations in code that isn't on a hot path.
## Tone
Direct, specific, focused on the code. No flattery, no padding. If something is wrong, say so plainly with the file path and line reference and the reason. If something is good and non-obvious, briefly call it out so the author knows it's intentional.
@@ -1,69 +0,0 @@
---
description: Structured 6-section delegation template and session-continuity rules for orchestrating sub-agents. Load before spawning any agent.
---
You are delegating work to a sub-agent. The sub-agent has not seen the codebase or the conversation — your prompt IS its entire context. Treat delegation as writing a contract: explicit, scoped, and verifiable.
## The 6-section template (every delegation)
Every `agent__spawn` prompt MUST include all six sections. Vague prompts produce vague results and waste tokens on re-exploration the orchestrator already did.
```
## TASK
[One atomic goal. One verb. One outcome. No "and also".]
## EXPECTED OUTCOME
[Concrete deliverables and success criteria. "I will know this is done when ..."]
## REQUIRED TOOLS
[Explicit allowlist: fs_read, fs_grep, etc. Prevents tool sprawl.]
## MUST DO
[Exhaustive requirements. Leave nothing implicit. If you'd be annoyed by the agent not doing X, list X.]
## MUST NOT DO
[Forbidden actions. Anticipate rogue behavior. "Do not modify files outside src/auth/."]
## CONTEXT
[File paths, code snippets, existing patterns, constraints. Paste actual code lines from prior exploration — not just file paths.]
```
## Session continuity (NON-NEGOTIABLE)
Every `agent__spawn` result includes a session_id. **Use it.**
- Task failed/incomplete → resume with `session_id` + a tight "Fix: <error>" prompt.
- Follow-up on a result → resume with `session_id` + "Also: <question>".
- Multi-turn with the same agent → always resume. Never start fresh.
Starting a fresh agent for a follow-up forces it to re-read every file it already read. That's 70%+ wasted tokens, plus the agent loses the reasoning it built up.
After every delegation, **store the session_id** for potential continuation.
## Skill nudges to delegates
Sub-agents have their own skills. Nudge them in the CONTEXT section:
> "Load `code-review` before evaluating the diff."
> "Load `frontend-ui-ux` before editing component files."
> "Load `git-master` before touching history."
A one-line nudge saves the delegate a `skill__list` turn.
## Verification after delegation
A delegation is NOT complete when the sub-agent returns. It is complete when YOU have verified:
1. Did it work as expected? (Did the file change? Did the test pass?)
2. Did it follow existing codebase patterns?
3. Did the EXPECTED OUTCOME actually materialize?
4. Did it respect MUST DO and MUST NOT DO?
If any answer is no → resume the session with a corrective prompt. Do not re-spawn from scratch.
## Anti-patterns
- "Follow existing patterns" with no snippet → agent guesses, often wrong
- Multi-goal prompts → agent does the easy one, skips the rest
- Missing MUST NOT DO → agent over-reaches into unrelated files
- Discarding session_id on failure → forced re-exploration, wasted tokens
- Re-spawning instead of resuming for a 1-line fix → 10x cost
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---
description: Designer-turned-developer who crafts stunning UI/UX even without design mockups. Grants filesystem read/write access for editing component files.
enabled_tools: fs_read, fs_write, fs_patch, fs_grep, fs_glob, fs_cat, fs_ls, fs_mkdir
---
You are doing frontend work. Use the filesystem tools to read, write, and patch component files. Treat UI/UX as a discipline, not a polish step at the end.
## Investigate before editing
Before changing a component:
- `fs_ls` the component's directory to see siblings and tests.
- `fs_read` the component itself.
- `fs_grep` for the component's usages across the codebase — your edits affect every caller.
- `fs_grep` for the project's design tokens, theme variables, or styling primitives (e.g., `--color-`, `theme.spacing`, `tw-`).
- Read existing similar components to match conventions.
## Visual hierarchy
Every screen has a focal point. Identify it before laying out anything else:
- One primary action per view. Make it visually dominant.
- Secondary actions are present but visibly subordinate.
- Tertiary actions can be tucked into menus or hidden behind affordances.
## Spacing and rhythm
- Use the project's existing spacing scale (4px, 8px, custom — match what's already there). Don't introduce one-off values.
- Larger spacing = stronger grouping break. Inside a card, tight; between cards, looser.
- White space is not wasted space. It's the difference between "professional" and "cramped."
## Typography
- Two or three sizes per view, max. More than that is noise.
- Line-height: 1.4-1.6 for body, tighter for headlines.
- Don't center long paragraphs. Left-align (or right-align for RTL).
## Color
- Use the project's existing palette. If you need a color that isn't there, you're probably overdesigning.
- Contrast matters: aim for WCAG AA at minimum (4.5:1 for body text, 3:1 for large text).
- Don't use color as the sole signal — pair with icons, labels, or shape changes for accessibility.
## Component conventions
When adding a new component:
- Match the existing structure: where do props go, where do styles go, where do tests go?
- `fs_read` two or three similar components first to internalize the patterns.
- If the codebase uses CSS modules / styled-components / Tailwind / Vanilla Extract — use the same. Don't introduce a new system.
- Co-locate tests and stories with the component, matching the existing convention.
## Forms
- Label every input. Placeholder text is not a label.
- Show validation errors near the field, not in a banner at the top.
- Validate on blur, not on every keystroke. Show success states only after the user has interacted.
- Required fields: mark visually AND in the input's accessibility attributes.
## Loading and empty states
- Empty states are an opportunity, not a fallback. Tell the user what they can do, not "no data."
- Loading: show structure (skeletons) when you know what's coming. Spinners are for indeterminate waits.
- Errors: explain WHAT failed and what the user can do about it. "Something went wrong" is useless.
## When unsure
Ship the boring version. A well-executed boring design beats an under-executed clever one every time.
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---
description: Methodology for atomic commits, rebase surgery, and clean git history. Grants shell access for running git commands.
enabled_tools: execute_command
---
You are operating on a git repository. Apply these conventions strictly. Use the `execute_command` tool to run git commands.
## Atomic commits
Each commit represents one logical change. If the commit message needs the word "and," the change is too large; split it. Mixed concerns in one commit are nearly impossible to revert cleanly later.
## Commit messages
- Subject line: imperative mood, ≤50 characters, no trailing period.
- Blank line.
- Body: explain WHY, not WHAT. The diff shows what changed.
- Reference issues by URL or canonical ID, not by free-form description.
## Rebase, don't merge
- `git rebase -i origin/main` before opening a PR.
- Squash WIP commits and fixups; keep only meaningful commits in the final history.
- Never rebase a branch others may have based work on. If unsure, ask.
## Conflict resolution
- Read both sides carefully before resolving. Don't reflexively take "ours" or "theirs."
- After resolving, run tests before continuing the rebase.
- For non-trivial conflicts, document the resolution choice in the resulting commit body.
## Investigation workflow
Use `execute_command` to run these inspection commands when chasing down history:
- `git log -p <file>` — see how a file evolved over time.
- `git log -S '<string>'` (pickaxe) — find when a string was added or removed.
- `git log --all --grep '<pattern>'` — search commit messages.
- `git blame -L <start>,<end> <file>` — current authorship for a line range.
- `git diff <ref1>..<ref2> -- <path>` — narrow diffs to specific paths.
- `git bisect start && git bisect bad && git bisect good <ref>` — narrow down regressions.
## Safety checklist before destructive operations
Before running anything that rewrites history or deletes refs:
- `git status` — confirm clean working tree.
- `git branch --show-current` — confirm which branch you're on.
- `git log -3 --oneline` — confirm what's about to be moved.
## What to never do
- Force-push to shared branches (`main`, release branches, anything teammates pull from).
- `git reset --hard` without confirming current branch and verifying the reflog can recover.
- `git push --no-verify` to skip hooks — fix the underlying issue instead.
- Commit secrets, even temporarily. Once pushed, treat as compromised; rotate.
## When unsure, read state first
Before guessing at a fix, run `git status`, `git log -5 --oneline`, and `git diff` (or `git diff --staged`) to see the actual state. Don't operate on assumptions.
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---
description: Discipline for when and how to consult Oracle - blocking by design, never deliver an answer with Oracle pending, never bypass Oracle for design questions.
---
Oracle is your read-only, high-IQ advisor. Using it correctly is the difference between shipping the right thing slowly and shipping the wrong thing fast.
## When you MUST consult Oracle
Spawn `oracle` (do NOT answer yourself) any time the user asks:
- "How should I..." / "What's the best way to..." — design/approach questions
- "Why does X keep..." / "What's wrong with..." — complex debugging (not simple errors)
- "Should I use X or Y?" — technology or pattern choices
- "How should this be structured?" — architecture and organization
- "Review this" / "What do you think of..." — code/design review
- Tradeoff questions — performance vs readability, complexity vs flexibility
- Multi-component questions — anything spanning 3+ files or modules
- Vague/open-ended — "improve this", "make this better", "clean this up"
- After 2+ failed fix attempts on the same problem — complex debugging
Even if you think you know the answer, Oracle provides deeper, more thorough analysis. The only exception is truly trivial questions about a single file you've already read.
## Oracle is BLOCKING by design
The orchestrator (you) has paused work and CANNOT proceed until Oracle returns. This is intentional. The cost of Oracle's latency is paid so YOU get a thorough, considered answer rather than rushing in a wrong direction.
Therefore:
- **Do NOT implement before Oracle returns** if your implementation depends on Oracle's recommendation.
- **Do NOT deliver the final user-facing answer** while Oracle is still running.
- **Do NOT "time out and continue anyway"** for Oracle-dependent tasks.
- While waiting, do only NON-OVERLAPPING prep work (work that doesn't depend on Oracle's verdict).
## How to consult Oracle effectively
Oracle has not seen the codebase or the conversation. Give it enough context to think:
```
## Question
[The decision you need help with, stated as a question]
## Background
[Why this question matters now. What constraint or trigger raised it.]
## Code context
[Paste the actual snippets from prior exploration — file paths alone are not enough]
- From `path/to/file.ext`:
<relevant 5-20 lines>
## What you've considered
[Options you've already weighed and their tradeoffs as you see them]
## What I'd love Oracle to evaluate
[Specific aspects: correctness, performance, security, future flexibility, etc.]
```
A well-scoped Oracle consult returns a tighter answer faster.
## After Oracle returns
1. Read the recommendation, reasoning, and risks sections carefully.
2. If the recommendation conflicts with your prior plan, update the plan — do not silently ignore Oracle.
3. Pass Oracle's recommendation (and reasoning) to the implementer (e.g., coder) as CONTEXT in your delegation.
4. If you disagree with Oracle's verdict, raise it with the user before implementing the alternative — don't act unilaterally against Oracle's advice.
## When NOT to consult Oracle
- Simple file operations you can do with direct tools
- First attempt at any fix (try yourself first; consult after 2 failures)
- Questions answerable from code you've already read
- Trivial decisions (variable names in small functions, formatting)
- Things you can infer from existing code patterns
Over-consultation wastes Oracle's budget and slows the work. Reserve Oracle for genuinely hard or load-bearing decisions.
## Anti-patterns (BLOCKING)
- Answering an architecture question yourself "just this once"
- Delivering a user-facing answer while Oracle is still running
- Implementing the obvious approach without consulting Oracle on a tradeoff question
- Ignoring Oracle's recommendation because it's inconvenient
- Polling `agent__collect` on a running Oracle (end your response, wait for notification)
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---
description: Fan-out exploration protocol — fire multiple research agents in parallel, wait for completion notifications, and never duplicate delegated work.
---
You are entering a research phase. Exploration is parallelizable; serial reads leave throughput on the table.
## Fan out, don't read serially
For any non-trivial codebase question, fire 2-5 `explore` agents in parallel, each scoped to a different angle:
- Auth implementation? → one for routes, one for middleware, one for token handling, one for error response shape.
- Bug investigation? → one for the failing path, one for similar working paths, one for recent changes near the area.
Each agent gets a NARROW slice. Narrow scope = fast, focused result. Broad scope = the agent over-reads and returns a wall of text.
## The wait protocol
After spawning background agents:
1. If you have **non-overlapping** work to do (work that doesn't depend on the delegated research), do it now.
2. If you don't, **end your response.** Do not call `agent__collect` immediately — the agent is still running.
3. The system notifies you when the agent completes (`pending_escalations` or completion event).
4. On notification, call `agent__collect` to retrieve results.
Polling `agent__collect` on a still-running agent blocks your turn for nothing.
## Anti-duplication rule (BLOCKING)
Once you delegate a search to an `explore` agent, **do not perform that same search yourself.**
Forbidden:
- After firing `explore` for "auth middleware", running `fs_grep` for "auth middleware" yourself
- "Just quickly checking" the same files the delegate is checking
- Re-doing the research while waiting impatiently
Allowed:
- Non-overlapping work in a different module
- Preparation work that doesn't depend on the delegated result
- Ending your response and waiting
Duplicate searches waste tokens, may contradict the delegate, and defeat the point of parallelism.
## Stop conditions
Stop searching when:
- The same information appears across multiple sources
- Two search iterations yield no new useful data
- A direct answer was found
- You have enough context to proceed confidently
Over-exploration is as bad as under-exploration. Time spent searching is time not spent shipping.
## Parallel + sequential composition
It is fine to fire `explore` and then `oracle` when oracle needs the explore results — just sequence them:
1. Fire explore(s) in parallel.
2. End response, wait for completion.
3. Synthesize findings, fire `oracle` with those findings as CONTEXT.
4. End response, wait for oracle.
5. Act on oracle's recommendation.
Don't fire oracle blind to "save a turn" — it will give worse advice.
## Anti-patterns
- One huge "explore everything about X" agent → slow, unfocused result
- Serial explores ("wait for first, then fire next") → unnecessary latency
- Firing 8+ parallel agents → diminishing returns, harder to synthesize
- Calling `agent__collect` immediately after spawn → wastes a turn
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---
description: Evidence requirements before claiming completion — diagnostics, build exit code, tests. No completion without proof. Grants shell access for running build/test commands.
enabled_tools: execute_command
---
You are about to mark work complete. Before claiming "done," produce evidence. "I'm fairly confident it works" is not evidence.
## Hard gates
A task is NOT complete until:
| Change kind | Required evidence |
|---|---|
| File edit | Read the file to confirm the change landed; output is clean (or only pre-existing issues, explicitly noted) |
| Build command exists | `execute_command` the build; exit code 0 |
| Test command exists | `execute_command` the tests; pass (or explicit note of pre-existing failures unrelated to this change) |
| Delegation | The delegate's result was received AND verified against your acceptance criteria |
**No evidence = not complete.** Marking a todo done without evidence is dishonest reporting.
## The verification loop
After every meaningful edit:
1. Read the changed file region (confirm the change actually landed where intended).
2. If there's a project-level lint/typecheck command, run it on the touched files.
3. Run the project's build/check command if one exists.
4. Run the project's test command if one exists.
5. Only then mark the corresponding todo `completed`.
If any step fails: do not mark complete. Fix the issue or surface it explicitly.
## Build/test detection (fallback)
If no build/test command is configured, try standard ones for the project:
- Rust: `cargo check`, `cargo test`
- Node/TS: `npm run build`, `npm test`, or `pnpm` / `yarn` equivalents
- Python: `pytest`, `python -m mypy <pkg>`, `ruff check`
- Go: `go build ./...`, `go test ./...`
Run from the project root. Capture exit codes.
## Distinguishing your failures from pre-existing failures
If build or tests fail, identify the cause:
- Caused by your change? → fix it before reporting complete.
- Pre-existing (unrelated)? → note it explicitly: "Done. Build passes. Note: 3 lint errors pre-existing in unrelated files, not touched."
Never silently leave broken state behind. Never delete a failing test to make CI green.
## Anti-patterns (BLOCKING)
- "It should work" without running anything
- Marking a todo complete based on intent, not verified outcome
- Suppressing errors with `@ts-ignore`, `as any`, `#[allow(...)]` on unfamiliar lints, empty catch blocks
- Deleting failing tests to "pass"
- Reporting "all green" when you only ran a subset
## Reporting completion
When the work is verifiably done, report in one sentence:
> "Done. Build passes, 47 tests pass. Modified `auth.rs:42-58` to add JWT validation."
Not a paragraph. Not a victory lap. Specific, terse, evidence-backed.
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@@ -1,5 +1,5 @@
# Agent-specific configuration
# Location `<coyote-config-dir>/agents/<agent-name>/config.yaml`
# Location `<loki-config-dir>/agents/<agent-name>/config.yaml`
#
# Available Environment Variables:
# - <agent-name>_MODEL
@@ -17,40 +17,12 @@ agent_session: null # Set a session to use when starting the agent.
name: <agent-name> # Name of the agent, used in the UI and logs
description: <description> # Description of the agent, used in the UI
version: 1 # Version of the agent
# Auto-Continue (Todo System)
# The auto-continue system provides built-in task tracking for improved reliability.
# When enabled, the model can create todo lists and the system will automatically
# prompt it to continue when incomplete tasks remain.
# See the [Todo System documentation](https://github.com/Dark-Alex-17/coyote/wiki/TODO-System) for more information
auto_continue: false # Enable automatic continuation when incomplete todos remain
max_auto_continues: 10 # Maximum number of automatic continuations before stopping
inject_todo_instructions: true # Inject the default todo tool usage instructions into the agent's system prompt
continuation_prompt: null # Custom prompt used when auto-continuing (optional; uses default if null)
inject_skill_instructions: true # Inject a short hint pointing the model at `skill__list` when skills are enabled
# (default: true). Suppressed automatically when no skills are available.
skill_instructions: null # Custom text for the skill hint (optional; uses built-in default if null)
# Sub-Agent Spawning System
# Enable this agent to spawn and manage child agents in parallel.
# See https://github.com/Dark-Alex-17/coyote/wiki/Agents for detailed documentation.
can_spawn_agents: false # Enable the agent to spawn child agents
max_concurrent_agents: 4 # Maximum number of agents that can run simultaneously
max_agent_depth: 3 # Maximum nesting depth for sub-agents (prevents runaway spawning)
inject_spawn_instructions: true # Inject the default agent spawning instructions into the agent's system prompt
summarization_model: null # Model to use for summarizing sub-agent output (e.g. 'openai:gpt-4o-mini'); defaults to current model
summarization_threshold: 4000 # Character threshold above which sub-agent output is summarized before returning to parent
escalation_timeout: 300 # Seconds a sub-agent waits for a user interaction response before timing out (default: 5 minutes)
mcp_servers: # Optional list of MCP servers that the agent utilizes
- github # Corresponds to the name of an MCP server in the `<coyote-config-dir>/functions/mcp.json` file
- github # Corresponds to the name of an MCP server in the `<loki-config-dir>/functions/mcp.json` file
global_tools: # Optional list of additional global tools to enable for the agent; i.e. not tools specific to the agent
- web_search
- fs
- python
skills_enabled: true # Master switch for skills in this agent (default: inherit from global).
# Skills also require `function_calling_support: true` in the global config.
enabled_skills: # Optional list of skills available when this agent runs.
# Must be a subset of global `visible_skills`. Omit to inherit the global default.
- git-master
- ai-slop-remover
dynamic_instructions: false # Whether to use dynamic instructions for the agent; if false, static instructions are used
instructions: | # Static instructions for the agent; ignored if dynamic instructions are used
You are a AI agent designed to demonstrate agent capabilities.
@@ -89,10 +61,10 @@ conversation_starters: # Optional conversation starters for the agent
- What is the best way to exercise?
- How do I manage my time effectively?
documents: # Optional documents to load for the agent
- git:/some/repo # Explicitly tell Coyote to use the 'git' document loader using an absolute path
- pdf:some-pdf-file.pdf # Explicitly tell Coyote to use the 'pdf' document loader using a relative path
- git:/some/repo # Explicitly tell Loki to use the 'git' document loader using an absolute path
- pdf:some-pdf-file.pdf # Explicitly tell Loki to use the 'pdf' document loader using a relative path
- https://some-website.com/some-page
- some-file.pdf # File with relative path to the <coyote-config-dir>/agents/<agent-name> directory; i.e. file in the same directory as this config file
- some-file.pdf # File with relative path to the <loki-config-dir>/agents/<agent-name> directory; i.e. file in the same directory as this config file
- ~/some-file.txt # File in the user's home directory
- /absolute/path/to/some-file.md # File with absolute path
- /absolute/path/**/NAME.txt # Find all NAME.txt files in the specified directory and all its subdirectories
+46 -146
View File
@@ -18,82 +18,34 @@ agent_session: null # Set a session to use when starting an agent (
# ---- Appearance ----
highlight: true # Controls syntax highlighting
light_theme: false # Activates a light color theme when true. env: COYOTE_LIGHT_THEME
light_theme: false # Activates a light color theme when true. env: LOKI_LIGHT_THEME
# ---- Miscellaneous ----
user_agent: null # Set User-Agent HTTP header, use `auto` for coyote/<current-version>
user_agent: null # Set User-Agent HTTP header, use `auto` for loki/<current-version>
save_shell_history: true # Whether to save shell execution command to the history file
sync_models_url: > # URL to sync model changes from
https://raw.githubusercontent.com/Dark-Alex-17/coyote/refs/heads/main/models.yaml
https://raw.githubusercontent.com/Dark-Alex-17/loki/refs/heads/main/models.yaml
# ---- REPL Prompt ----
# Custom REPL left/right prompts; see the [REPL Prompt Documentation](https://github.com/Dark-Alex-17/coyote/wiki/REPL-Prompt) for more information
# Custom REPL left/right prompts; see the [REPL Prompt Documentation](./docs/REPL-PROMPT.md) for more information
left_prompt:
'{color.red}{model}){color.green}{?session {?agent {agent}>}{session}{?role /}}{!session {?agent {agent}>}}{role}{?rag @{rag}}{color.cyan}{?session )}{!session >}{color.reset} '
right_prompt:
'{color.purple}{?session {?consume_tokens {consume_tokens}({consume_percent}%)}{!consume_tokens {consume_tokens}}}{color.reset}'
# ---- Vault ----
# See the [Vault documentation](https://github.com/Dark-Alex-17/coyote/wiki/Vault) for more information on the Coyote vault.
#
# The secrets_provider tells Coyote where to read and write secrets referenced via {{SECRET_NAME}} syntax.
#
# Shorthand: set vault_password_file to enable the local provider with that password file.
vault_password_file: null # Path to a file containing the password for the Coyote vault (cannot be a secret template)
#
# Explicit: set secrets_provider to one of the supported types below. When secrets_provider is set,
# vault_password_file is ignored. Note: secrets_provider itself cannot use {{SECRET}} template syntax.
# The vault must be initialized before any secrets can be resolved.
#
# Local (same as the shorthand above):
# secrets_provider:
# type: local
# password_file: ~/.coyote_password
#
# AWS Secrets Manager (requires an authenticated AWS CLI; see `aws sso login` or `aws configure`):
# secrets_provider:
# type: aws_secrets_manager
# aws_profile: default
# aws_region: us-east-1
#
# GCP Secret Manager (requires `gcloud auth application-default login`):
# secrets_provider:
# type: gcp_secret_manager
# gcp_project_id: my-project-id
#
# Azure Key Vault (requires `az login`):
# secrets_provider:
# type: azure_key_vault
# vault_name: my-vault-name
#
# gopass (requires the `gopass` CLI to be installed and initialized):
# secrets_provider:
# type: gopass
# store: my-store # Optional; omit to use the default store
#
# 1Password (requires the `op` CLI to be installed and signed in via `op signin`):
# secrets_provider:
# type: one_password
# vault: Production # Optional; omit to use the default vault
# account: my.1password.com # Optional; omit to use the default account
# See the [Vault documentation](./docs/VAULT.md) for more information on the Loki vault
vault_password_file: null # Path to a file containing the password for the Loki vault (cannot be a secret template)
# ---- Function Calling ----
# See the [Tools documentation](https://github.com/Dark-Alex-17/coyote/wiki/Tools) for more details
function_calling_support: true # Enables or disables function calling (Globally).
# See the [Tools documentation](./docs/function-calling/TOOLS.md) for more details
function_calling: true # Enables or disables function calling (Globally).
mapping_tools: # Alias for a tool or toolset
fs: 'fs_cat,fs_ls,fs_mkdir,fs_rm,fs_write,fs_read,fs_glob,fs_grep'
enabled_tools: null # Which tools to enable by default.
# Accepts either a YAML list or a comma-separated string. Use 'all' to enable everything.
# Example (list form):
# enabled_tools:
# - fs
# - web_search_coyote
# Example (comma-separated form):
# enabled_tools: fs,web_search_coyote
fs: 'fs_cat,fs_ls,fs_mkdir,fs_rm,fs_write'
enabled_tools: null # Which tools to enable by default. (e.g. 'fs,web_search_loki')
visible_tools: # Which tools are visible to be compiled (and are thus able to be defined in 'enabled_tools')
# - demo_py.py
# - demo_sh.sh
# - demo_ts.ts
- execute_command.sh
# - execute_py_code.py
# - execute_sql_code.sh
@@ -101,74 +53,31 @@ visible_tools: # Which tools are visible to be compiled (and a
# - fetch_url_via_jina.sh
- fs_cat.sh
- fs_ls.sh
# - fs_read.sh
# - fs_glob.sh
# - fs_grep.sh
# - fs_mkdir.sh
# - fs_patch.sh
# - fs_write.sh
- get_current_time.sh
# - get_current_weather.py
# - get_current_weather.ts
- get_current_weather.sh
- query_jira_issues.sh
# - search_arxiv.sh
# - search_wikipedia.sh
# - search_wolframalpha.sh
# - send_mail.sh
# - send_twilio.sh
# - web_search_coyote.sh
# - web_search_loki.sh
# - web_search_perplexity.sh
# - web_search_tavily.sh
# ---- MCP Servers ----
# See the [MCP Servers documentation](https://github.com/Dark-Alex-17/coyote/wiki/MCP-Servers) for more details
# See the [MCP Servers documentation](./docs/MCP-SERVERS.md) for more details
mcp_server_support: true # Enables or disables MCP servers (globally).
mapping_mcp_servers: # Alias for an MCP server or set of servers
git: github,gitmcp
enabled_mcp_servers: null # Which MCP servers to enable by default.
# Accepts either a YAML list or a comma-separated string. Use 'all' to enable everything.
# Example (list form):
# enabled_mcp_servers:
# - github
# - slack
# Example (comma-separated form):
# enabled_mcp_servers: github,slack,ddg-search
# ---- Skills ----
# Skills are modular knowledge or capability packs the LLM can load and unload mid-conversation.
# See the [Skills documentation](https://github.com/Dark-Alex-17/coyote/wiki/Skills) for more details.
skills_enabled: true # Master switch. Set to false to hide all skill management tools from the model.
# Skills also require `function_calling_support: true` above to work at all.
visible_skills: # The universe of skills allowed to be enabled in any context. Omit (null) for "all installed".
- ai-slop-remover
- code-review
- frontend-ui-ux
- git-master
enabled_skills: null # Which skills are available by default (no role/agent/session active). null = all visible.
# Accepts either a YAML list or a comma-separated string.
# Example (list form):
# enabled_skills:
# - git-master
# - ai-slop-remover
# Example (comma-separated form):
# enabled_skills: git-master,ai-slop-remover
# ---- Auto-Continue (Todo System) ----
# The auto-continue system provides built-in task tracking for improved reliability.
# When enabled, the model can create todo lists and the system will automatically
# prompt it to continue when incomplete tasks remain.
# See the [Todo System documentation](https://github.com/Dark-Alex-17/coyote/wiki/TODO-System) for more information
auto_continue: false # Enable automatic continuation when incomplete todos remain (default: false)
max_auto_continues: 10 # Maximum number of automatic continuations before stopping (default: 10)
inject_todo_instructions: true # Inject default todo usage instructions into the system prompt (default: true)
continuation_prompt: null # Custom prompt used when auto-continuing. If null, uses built-in default
inject_skill_instructions: true # Inject a short hint pointing the model at `skill__list` when skills are enabled in
# this context. Only injected if `function_calling_support`, `skills_enabled`, and the
# effective enabled skill set is non-empty (default: true).
skill_instructions: null # Custom text used for the skill hint when injected. If null, uses built-in default.
enabled_mcp_servers: null # Which MCP servers to enable by default (e.g. 'github,slack')
# ---- Session ----
# See the [Session documentation](https://github.com/Dark-Alex-17/coyote/wiki/Sessions) for more information
# See the [Session documentation](./docs/SESSIONS.md) for more information
save_session: null # Controls the persistence of the session. If true, auto save; if false, don't auto-save save; if null, ask the user what to do
compression_threshold: 4000 # Compress the session when the token count reaches or exceeds this threshold
summarization_prompt: > # The text prompt used for creating a concise summary of session message
@@ -177,13 +86,13 @@ summary_context_prompt: > # The text prompt used for including the summar
'This is a summary of the chat history as a recap: '
# ---- RAG ----
# See the [RAG Docs](https://github.com/Dark-Alex-17/coyote/wiki/RAG) for more details.
# See the [RAG Docs](./docs/RAG.md) for more details.
rag_embedding_model: null # Specifies the embedding model used for context retrieval
rag_reranker_model: null # Specifies the reranker model used for sorting retrieved documents; Coyote uses Reciprocal Rank Fusion by default
rag_reranker_model: null # Specifies the reranker model used for sorting retrieved documents; Loki uses Reciprocal Rank Fusion by default
rag_top_k: 5 # Specifies the number of documents to retrieve for answering queries
rag_chunk_size: null # Defines the size of chunks for document processing in characters
rag_chunk_overlap: null # Defines the overlap between chunks
# Defines the query structure using variables like __CONTEXT__, __SOURCES__, and __INPUT__ to tailor searches to specific needs
# Defines the query structure using variables like __CONTEXT__ and __INPUT__ to tailor searches to specific needs
rag_template: |
Answer the query based on the context while respecting the rules. (user query, some textual context and rules, all inside xml tags)
@@ -191,10 +100,6 @@ rag_template: |
__CONTEXT__
</context>
<sources>
__SOURCES__
</sources>
<rules>
- If you don't know, just say so.
- If you are not sure, ask for clarification.
@@ -202,7 +107,6 @@ rag_template: |
- If the context appears unreadable or of poor quality, tell the user then answer as best as you can.
- If the answer is not in the context but you think you know the answer, explain that to the user then answer with your own knowledge.
- Answer directly and without using xml tags.
- When using information from the context, cite the relevant source from the <sources> section.
</rules>
<user_query>
@@ -218,12 +122,12 @@ document_loaders:
docx: 'pandoc --to plain $1' # Use pandoc to convert a .docx file to text
# (see https://pandoc.org for details on how to install pandoc)
jina: 'curl -fsSL https://r.jina.ai/$1 -H "Authorization: Bearer {{JINA_API_KEY}}' # Use Jina to translate a website into text;
# Requires a Jina API key to be added to the Coyote vault
# Requires a Jina API key to be added to the Loki vault
git: > # Use yek to load a git repository into the knowledgebase (https://github.com/bodo-run/yek)
sh -c "yek $1 --json | jq 'map({ path: .filename, contents: .content })'"
# ---- Clients ----
# See the [Clients documentation](https://github.com/Dark-Alex-17/coyote/wiki/Clients) for more details
# See the [Clients documentation](./docs/clients/CLIENTS.md) for more details
clients:
# All clients have the following configuration:
# - type: xxxx
@@ -254,14 +158,14 @@ clients:
# See https://platform.openai.com/docs/quickstart
- type: openai
api_base: https://api.openai.com/v1 # Optional
api_key: '{{OPENAI_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{OPENAI_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
organization_id: org-xxx # Optional
# For any platform compatible with OpenAI's API
- type: openai-compatible
name: ollama
api_base: http://localhost:11434/v1
api_key: '{{OLLAMA_API_KEY}}' # Optional; You can either hard-code or inject secrets from the Coyote vault
api_key: '{{OLLAMA_API_KEY}}' # Optional; You can either hard-code or inject secrets from the Loki vault
models:
- name: deepseek-r1
max_input_tokens: 131072
@@ -279,9 +183,7 @@ clients:
# See https://ai.google.dev/docs
- type: gemini
api_base: https://generativelanguage.googleapis.com/v1beta
api_key: '{{GEMINI_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
auth: null # When set to 'oauth', Coyote will use OAuth instead of an API key
# Authenticate with `coyote --authenticate` or `.authenticate` in the REPL
api_key: '{{GEMINI_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
patch:
chat_completions:
'.*':
@@ -299,49 +201,47 @@ clients:
# See https://docs.anthropic.com/claude/reference/getting-started-with-the-api
- type: claude
api_base: https://api.anthropic.com/v1 # Optional
api_key: '{{ANTHROPIC_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
auth: null # When set to 'oauth', Coyote will use OAuth instead of an API key
# Authenticate with `coyote --authenticate` or `.authenticate` in the REPL
api_key: '{{ANTHROPIC_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://docs.mistral.ai/
- type: openai-compatible
name: mistral
api_base: https://api.mistral.ai/v1
api_key: '{{MISTRAL_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{MISTRAL_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://docs.x.ai/docs
- type: openai-compatible
name: xai
api_base: https://api.x.ai/v1
api_key: '{{XAI_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{XAI_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://docs.ai21.com/docs/overview
- type: openai-compatible
name: ai12
api_base: https://api.ai21.com/studio/v1
api_key: '{{AI21_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{AI21_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://docs.cohere.com/docs/the-cohere-platform
- type: cohere
api_base: https://api.cohere.ai/v2 # Optional
api_key: '{{COHERE_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{COHERE_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://docs.perplexity.ai/getting-started/overview
- type: openai-compatible
name: perplexity
api_base: https://api.perplexity.ai
api_key: '{{PERPLEXITY_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{PERPLEXITY_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://console.groq.com/docs/quickstart
- type: openai-compatible
name: groq
api_base: https://api.groq.com/openai/v1
api_key: '{{GROQ_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{GROQ_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart
- type: azure-openai
api_base: https://{RESOURCE}.openai.azure.com
api_key: '{{AZURE_OPENAI_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{AZURE_OPENAI_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
models:
- name: gpt-4o # Model deployment name
max_input_tokens: 128000
@@ -372,8 +272,8 @@ clients:
# See https://docs.aws.amazon.com/bedrock/latest/userguide/
- type: bedrock
access_key_id: '{{AWS_ACCESS_KEY_ID}}' # You can either hard-code or inject secrets from the Coyote vault
secret_access_key: '{{AWS_SECRET_ACCESS_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
access_key_id: '{{AWS_ACCESS_KEY_ID}}' # You can either hard-code or inject secrets from the Loki vault
secret_access_key: '{{AWS_SECRET_ACCESS_KEY}}' # You can either hard-code or inject secrets from the Loki vault
region: xxx
session_token: xxx # Optional, only needed for temporary credentials
@@ -381,67 +281,67 @@ clients:
- type: openai-compatible
name: cloudflare
api_base: https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/v1
api_key: '{{CLOUDFLARE_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{CLOUDFLARE_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html
- type: openai-compatible
name: ernie
api_base: https://qianfan.baidubce.com/v2
api_key: '{{BAIDU_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{BAIDU_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://dashscope.aliyun.com/
- type: openai-compatible
name: qianwen
api_base: https://dashscope.aliyuncs.com/compatible-mode/v1
api_key: '{{ALIYUN_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{ALIYUN_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://cloud.tencent.com/product/hunyuan
- type: openai-compatible
name: hunyuan
api_base: https://api.hunyuan.cloud.tencent.com/v1
api_key: '{{TENCENT_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{TENCENT_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://platform.moonshot.cn/docs/intro
- type: openai-compatible
name: moonshot
api_base: https://api.moonshot.cn/v1
api_key: '{{MOONSHOT_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{MOONSHOT_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://platform.deepseek.com/api-docs/
- type: openai-compatible
name: deepseek
api_base: https://api.deepseek.com
api_key: '{{DEEPSEEK_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{DEEPSEEK_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://open.bigmodel.cn/dev/howuse/introduction
- type: openai-compatible
name: zhipuai
api_base: https://open.bigmodel.cn/api/paas/v4
api_key: '{{ZHIPUAI_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{ZHIPUAI_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://platform.minimaxi.com/document/Fast%20access
- type: openai-compatible
name: minimax
api_base: https://api.minimax.chat/v1
api_key: '{{MINIMAX_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{MINIMAX_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://openrouter.ai/docs#quick-start
- type: openai-compatible
name: openrouter
api_base: https://openrouter.ai/api/v1
api_key: '{{OPENROUTER_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{OPENROUTER_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://github.com/marketplace/models
- type: openai-compatible
name: github
api_base: https://models.inference.ai.azure.com
api_key: '{{GITHUB_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{GITHUB_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://deepinfra.com/docs
- type: openai-compatible
name: deepinfra
api_base: https://api.deepinfra.com/v1/openai
api_key: '{{DEEPINFRA_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{DEEPINFRA_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# ----- RAG dedicated -----
@@ -450,10 +350,10 @@ clients:
- type: openai-compatible
name: jina
api_base: https://api.jina.ai/v1
api_key: '{{JINA_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{JINA_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault
# See https://docs.voyageai.com/docs/introduction
- type: openai-compatible
name: voyageai
api_base: https://api.voyageai.com/v1
api_key: '{{VOYAGEAI_API_KEY}}' # You can either hard-code or inject secrets from the Coyote vault
api_key: '{{VOYAGEAI_API_KEY}}' # You can either hard-code or inject secrets from the Loki vault

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