Created initial assets

assets/functions/scripts/run-agent.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python
+
+# Usage: ./{agent_name}.py <agent-func> <agent-data>
+
+import os
+import re
+import json
+import sys
+import importlib.util
+from pathlib import Path
+
+def _ensure_cwd_venv():
+    cwd = Path.cwd()
+    venv_dir = cwd / ".venv"
+    if not venv_dir.is_dir():
+        return
+
+    py = venv_dir / ("Scripts/python.exe" if os.name == "nt" else "bin/python")
+    if not py.exists():
+        return
+
+    if Path(sys.prefix).resolve() == venv_dir.resolve():
+        return
+
+    os.execv(str(py), [str(py)] + sys.argv)
+
+_ensure_cwd_venv()
+
+
+def main():
+    (agent_func, raw_data) = parse_argv()
+    agent_data = parse_raw_data(raw_data)
+
+    root_dir = "{config_dir}"
+    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)
+
+
+def parse_raw_data(data):
+    if not data:
+        raise ValueError("No JSON data")
+
+    try:
+        return json.loads(data)
+    except Exception:
+        raise ValueError("Invalid JSON data")
+
+
+def parse_argv():
+    agent_func = sys.argv[1]
+    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)
+        sys.exit(1)   
+
+    return agent_func, agent_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}")
+
+
+def load_env(file_path):
+    try:
+        with open(file_path, "r") as f:
+            lines = f.readlines()
+    except:
+        return
+
+    env_vars = {}
+
+    for line in lines:
+        line = line.strip()
+        if line.startswith("#") or not line:
+            continue
+
+        key, *value_parts = line.split("=")
+        env_name = key.strip()
+
+        if env_name not in os.environ:
+            env_value = "=".join(value_parts).strip()
+            if (env_value.startswith('"') and env_value.endswith('"')) or (env_value.startswith("'") and env_value.endswith("'")):
+                env_value = env_value[1:-1]
+            env_vars[env_name] = env_value
+
+    os.environ.update(env_vars)
+
+
+def run(agent_path, agent_func, agent_data):
+    spec = importlib.util.spec_from_file_location(
+        os.path.basename(agent_path), agent_path
+    )
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+
+    if not hasattr(mod, agent_func):
+        raise Exception(f"Not module function '{agent_func}' at '{agent_path}'")
+
+    value = getattr(mod, agent_func)(**agent_data)
+    return_to_llm(value)
+    dump_result('{agent_name}' + f':{agent_func}')
+
+
+def return_to_llm(value):
+    if value is None:
+        return
+
+    if "LLM_OUTPUT" in os.environ:
+        writer = open(os.environ["LLM_OUTPUT"], "w")
+    else:
+        writer = sys.stdout
+
+    value_type = type(value).__name__
+    if value_type in ("str", "int", "float", "bool"):
+        writer.write(str(value))
+    elif value_type == "dict" or value_type == "list":
+        value_str = json.dumps(value, indent=2)
+        assert value == json.loads(value_str)
+        writer.write(value_str)
+
+
+def dump_result(name):
+    if (not os.getenv("LLM_DUMP_RESULTS")) or (not os.getenv("LLM_OUTPUT")) or (not os.isatty(1)):
+        return
+
+    show_result = False
+    try:
+        if re.search(rf'\b({os.environ["LLM_DUMP_RESULTS"]})\b', name):
+            show_result = True
+    except:
+        pass
+
+    if not show_result:
+        return
+
+    try:
+        with open(os.environ["LLM_OUTPUT"], "r", encoding="utf-8") as f:
+            data = f.read()
+    except:
+        return
+
+    print(f"\x1b[2m----------------------\n{data}\n----------------------\x1b[0m")
+
+
+if __name__ == "__main__":
+    main()

assets/functions/scripts/run-agent.sh

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+#!/usr/bin/env bash
+
+# Usage: ./{agent_name}.sh <agent-func> <agent-data>
+
+set -e
+
+main() {
+    root_dir="{config_dir}"
+    parse_argv "$@"
+    setup_env
+    tools_path="$root_dir/agents/{agent_name}/tools.sh"
+    run
+}
+
+parse_argv() {
+		agent_func="$1"
+		agent_data="$2"
+    if [[ -z "$agent_data" ]] || [[ -z "$agent_func" ]]; then
+        die "usage: ./{agent_name}.sh <agent-func> <agent-data>"
+    fi
+}
+
+setup_env() {
+    load_env "$root_dir/.env"
+    export LLM_ROOT_DIR="$root_dir"
+    export LLM_AGENT_NAME="{agent_name}"
+    export LLM_AGENT_FUNC="$agent_func"
+    export LLM_AGENT_ROOT_DIR="$LLM_ROOT_DIR/agents/{agent_name}"
+    export LLM_AGENT_CACHE_DIR="$LLM_ROOT_DIR/cache/{agent_name}"
+}
+
+load_env() {
+    local env_file="$1" env_vars
+    if [[ -f "$env_file" ]]; then
+        while IFS='=' read -r key value; do
+            if [[ "$key" == $'#'* ]] || [[ -z "$key" ]]; then
+                continue
+            fi
+
+            if [[ -z "${!key+x}" ]]; then
+                env_vars="$env_vars $key=$value"
+            fi
+        done < <(cat "$env_file"; echo "")
+
+        if [[ -n "$env_vars" ]]; then
+            eval "export $env_vars"
+        fi
+    fi
+}
+
+run() {
+    if [[ -z "$agent_data" ]]; then
+        die "error: no JSON data"
+    fi
+
+    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'
+def escape_shell_word:
+  tostring
+  | gsub("'"; "'\"'\"'")
+  | gsub("\n"; "'$'\\n''")
+  | "'\(.)'";
+def to_args:
+    to_entries | .[] |
+    (.key | split("_") | join("-")) as $key |
+    if .value | type == "array" then
+        .value | .[] | "--\($key) \(. | escape_shell_word)"
+    elif .value | type == "boolean" then
+        if .value then "--\($key)" else "" end
+    else
+        "--\($key) \(.value | escape_shell_word)"
+    end;
+[ to_args ] | join(" ")
+EOF
+)"
+    args="$(echo "$agent_data" | jq -r "$jq_script" 2>/dev/null)" || {
+        die "error: invalid JSON data"
+    }
+
+    if [[ -z "$LLM_OUTPUT" ]]; then
+        is_temp_llm_output=1
+        # shellcheck disable=SC2155
+        export LLM_OUTPUT="$(mktemp)"
+    fi
+
+    eval "'$tools_path' '$agent_func' $args"
+
+    if [[ "$is_temp_llm_output" -eq 1 ]]; then
+        cat "$LLM_OUTPUT"
+    else
+        dump_result "{agent_name}:${LLM_AGENT_FUNC}"
+    fi
+}
+
+dump_result() {
+    if [[ "$LLM_OUTPUT" == "/dev/stdout" ]] || [[ -z "$LLM_DUMP_RESULTS" ]] ||  [[ ! -t 1 ]]; then
+        return;
+    fi
+
+    if grep -q -w -E "$LLM_DUMP_RESULTS" <<<"$1"; then
+        cat <<EOF
+$(echo -e "\e[2m")----------------------
+$(cat "$LLM_OUTPUT")
+----------------------$(echo -e "\e[0m")
+EOF
+    fi
+}
+
+die() {
+    echo "$*" >&2
+    exit 1
+}
+
+main "$@"
+

assets/functions/scripts/run-tool.py

@@ -0,0 +1,153 @@
+#!/usr/bin/env python
+
+# Usage: ./{function_name}.py <tool-data>
+
+import os
+import re
+import json
+import sys
+import importlib.util
+from pathlib import Path
+
+def _ensure_cwd_venv():
+    cwd = Path.cwd()
+    venv_dir = cwd / ".venv"
+    if not venv_dir.is_dir():
+        return
+
+    py = venv_dir / ("Scripts/python.exe" if os.name == "nt" else "bin/python")
+    if not py.exists():
+        return
+
+    if Path(sys.prefix).resolve() == venv_dir.resolve():
+        return
+
+    os.execv(str(py), [str(py)] + sys.argv)
+
+_ensure_cwd_venv()
+
+
+def main():
+    raw_data = parse_argv()
+    tool_data = parse_raw_data(raw_data)
+
+    root_dir = "{config_dir}/functions"
+    setup_env(root_dir)
+
+    tool_path = os.path.join(root_dir, "tools/{function_name}.py")
+    run(tool_path, "run", tool_data)
+
+
+def parse_raw_data(data):
+    if not data:
+        raise ValueError("No JSON data")
+
+    try:
+        return json.loads(data)
+    except Exception:
+        raise ValueError("Invalid JSON data")
+
+
+def parse_argv():
+    argv = sys.argv[:] + [None] * max(0, 2 - len(sys.argv))
+
+    tool_data = argv[1]
+
+    if (not tool_data):
+        print("Usage: ./{function_name}.py <tool-data>", file=sys.stderr)
+        sys.exit(1)
+
+    return tool_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}")
+
+
+def load_env(file_path):
+    try:
+        with open(file_path, "r") as f:
+            lines = f.readlines()
+    except:
+        return
+
+    env_vars = {}
+
+    for line in lines:
+        line = line.strip()
+        if line.startswith("#") or not line:
+            continue
+
+        key, *value_parts = line.split("=")
+        env_name = key.strip()
+
+        if env_name not in os.environ:
+            env_value = "=".join(value_parts).strip()
+            if (env_value.startswith('"') and env_value.endswith('"')) or (env_value.startswith("'") and env_value.endswith("'")):
+                env_value = env_value[1:-1]
+            env_vars[env_name] = env_value
+
+    os.environ.update(env_vars)
+
+
+def run(tool_path, tool_func, tool_data):
+    spec = importlib.util.spec_from_file_location(
+        os.path.basename(tool_path), tool_path
+    )
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+
+    if not hasattr(mod, tool_func):
+        raise Exception(f"No module function '{tool_func}' at '{tool_path}'")
+
+    value = getattr(mod, tool_func)(**tool_data)
+    return_to_llm(value)
+    dump_result("{function_name}")
+
+
+def return_to_llm(value):
+    if value is None:
+        return
+
+    if "LLM_OUTPUT" in os.environ:
+        writer = open(os.environ["LLM_OUTPUT"], "w")
+    else:
+        writer = sys.stdout
+
+    value_type = type(value).__name__
+    if value_type in ("str", "int", "float", "bool"):
+        writer.write(str(value))
+    elif value_type == "dict" or value_type == "list":
+        value_str = json.dumps(value, indent=2)
+        assert value == json.loads(value_str)
+        writer.write(value_str)
+
+
+def dump_result(name):
+    if (not os.getenv("LLM_DUMP_RESULTS")) or (not os.getenv("LLM_OUTPUT")) or (not os.isatty(1)):
+        return
+
+    show_result = False
+    try:
+        if re.search(rf'\b({os.environ["LLM_DUMP_RESULTS"]})\b', name):
+            show_result = True
+    except:
+        pass
+
+    if not show_result:
+        return
+
+    try:
+        with open(os.environ["LLM_OUTPUT"], "r", encoding="utf-8") as f:
+            data = f.read()
+    except:
+        return
+
+    print(f"\x1b[2m----------------------\n{data}\n----------------------\x1b[0m")
+
+
+if __name__ == "__main__":
+    main()

assets/functions/scripts/run-tool.sh

@@ -0,0 +1,116 @@
+#!/usr/bin/env bash
+
+# Usage: ./{function_name}.sh <tool-data>
+
+set -e
+
+main() {
+    root_dir="{config_dir}/functions"
+    parse_argv "$@"
+    setup_env
+    tool_path="$root_dir/tools/{function_name}.sh"
+    run
+}
+
+parse_argv() {
+		tool_data="$1"
+    if [[ -z "$tool_data" ]]; then
+        die "usage: ./{function_name}.sh <tool-data>"
+    fi
+}
+
+setup_env() {
+    load_env "$root_dir/.env"
+    export LLM_ROOT_DIR="$root_dir"
+    export LLM_TOOL_NAME="{function_name}"
+    export LLM_TOOL_CACHE_DIR="$LLM_ROOT_DIR/cache/{function_name}"
+}
+
+load_env() {
+    local env_file="$1" env_vars
+    if [[ -f "$env_file" ]]; then
+        while IFS='=' read -r key value; do
+            if [[ "$key" == $'#'* ]] || [[ -z "$key" ]]; then
+                continue
+            fi
+
+            if [[ -z "${!key+x}" ]]; then
+                env_vars="$env_vars $key=$value"
+            fi
+        done < <(cat "$env_file"; echo "")
+
+        if [[ -n "$env_vars" ]]; then
+            eval "export $env_vars"
+        fi
+    fi
+}
+
+run() {
+    if [[ -z "$tool_data" ]]; then
+        die "error: no JSON data"
+    fi
+
+    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'
+def escape_shell_word:
+  tostring
+  | gsub("'"; "'\"'\"'")
+  | gsub("\n"; "'$'\\n''")
+  | "'\(.)'";
+def to_args:
+    to_entries | .[] |
+    (.key | split("_") | join("-")) as $key |
+    if .value | type == "array" then
+        .value | .[] | "--\($key) \(. | escape_shell_word)"
+    elif .value | type == "boolean" then
+        if .value then "--\($key)" else "" end
+    else
+        "--\($key) \(.value | escape_shell_word)"
+    end;
+[ to_args ] | join(" ")
+EOF
+)"
+    args="$(echo "$tool_data" | jq -r "$jq_script" 2>/dev/null)" || {
+        die "error: invalid JSON data"
+    }
+
+    if [[ -z "$LLM_OUTPUT" ]]; then
+        is_temp_llm_output=1
+        # shellcheck disable=SC2155
+        export LLM_OUTPUT="$(mktemp)"
+    fi
+
+    eval "'$tool_path' $args"
+
+    if [[ "$is_temp_llm_output" -eq 1 ]]; then
+        cat "$LLM_OUTPUT"
+    else
+        dump_result "{function_name}"
+    fi
+}
+
+dump_result() {
+    if [[ "$LLM_OUTPUT" == "/dev/stdout" ]] || [[ -z "$LLM_DUMP_RESULTS" ]] ||  [[ ! -t 1 ]]; then
+        return;
+    fi
+
+    if grep -q -w -E "$LLM_DUMP_RESULTS" <<<"$1"; then
+        cat <<EOF
+$(echo -e "\e[2m")----------------------
+$(cat "$LLM_OUTPUT")
+----------------------$(echo -e "\e[0m")
+EOF
+    fi
+}
+
+die() {
+    echo "$*" >&2
+    exit 1
+}
+
+main "$@"

assets/roles/code.md

@@ -0,0 +1,9 @@
+Provide only code without comments or explanations.
+### INPUT:
+async sleep in js
+### OUTPUT:
+```javascript
+async function timeout(ms) {
+  return new Promise(resolve => setTimeout(resolve, ms));
+}
+```

assets/roles/create-prompt.md

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+As a professional Prompt Engineer, your role is to create effective and innovative prompts for interacting with AI models.
+
+Your core skills include:
+1. **CO-STAR Framework Application**: Utilize the CO-STAR framework to build efficient prompts, ensuring effective communication with large language models.
+2. **Contextual Awareness**: Construct prompts that adapt to complex conversation contexts, ensuring relevant and coherent responses.
+3. **Chain-of-Thought Prompting**: Create prompts that elicit AI models to demonstrate their reasoning process, enhancing the transparency and accuracy of answers.
+4. **Zero-shot Learning**: Design prompts that enable AI models to perform specific tasks without requiring examples, reducing dependence on training data.
+5. **Few-shot Learning**: Guide AI models to quickly learn and execute new tasks through a few examples.
+
+Your output format should include:
+- **Context**: Provide comprehensive background information for the task to ensure the AI understands the specific scenario and offers relevant feedback.
+- **Objective**: Clearly define the task objective, guiding the AI to focus on achieving specific goals.
+- **Style**: Specify writing styles according to requirements, such as imitating a particular person or industry expert.
+- **Tone**: Set an appropriate emotional tone to ensure the AI's response aligns with the expected emotional context.
+- **Audience**: Tailor AI responses for a specific audience, ensuring content appropriateness and ease of understanding.
+- **Response**: Specify output formats for easy execution of downstream tasks, such as lists, JSON, or professional reports.
+- **Workflow**: Instruct the AI on how to step-by-step complete tasks, clarifying inputs, outputs, and specific actions for each step.
+- **Examples**: Show a case of input and output that fits the scenario.
+
+Your workflow should be:
+1. Extract key information from user requests to determine design objectives.
+2. Based on user needs, create prompts that meet requirements, with each part being professional and detailed.
+3. Must only output the newly generated and optimized prompts, without explanation, without wrapping it in markdown code block.
+
+My first request is: __INPUT__

assets/roles/create-title.md

@@ -0,0 +1,11 @@
+Create a concise, 3-6 word title.
+
+**Notes**:
+- Avoid quotation marks or emojis
+- RESPOND ONLY WITH TITLE SLUG TEXT
+
+**Examples**:
+stock-market-trends
+perfect-chocolate-chip-recipe
+remote-work-productivity-tips
+video-game-development-insights

assets/roles/explain-shell.md

@@ -0,0 +1,4 @@
+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.
+APPLY MARKDOWN formatting when possible.

assets/roles/functions.md

@@ -0,0 +1,3 @@
+---
+use_tools: all
+---

assets/roles/mcp-servers.md

@@ -0,0 +1,3 @@
+---
+use_mcp_servers: all
+---

assets/roles/shell.md

@@ -0,0 +1,5 @@
+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.
+If multiple steps are required, try to combine them using '&&' (For PowerShell, use ';' instead).
+Output only plain text without any markdown formatting or <think> or </think> tokens.