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 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.
🚨 Operational History (optional): Checks the change against past production incidents via the incident-prior-art skill.

Operational History Lane

Code review answers "is this code good?" — this lane answers "did we already get burned by this?" When the diff touches operationally-relevant surface (error handling, retries, timeouts, alerting, config controlling any of these), the orchestrator:

Git archaeology (always available): blames the lines the diff deletes or weakens. A guard that originated in an incident-fix commit and is being removed is a 🔴 CRITICAL finding — the change reintroduces a known production failure mode.
Prior-art delegation (opt-in): if the prior_art_agent variable names an agent that can search your incident record (Slack, Jira, postmortems, handoff docs), it is spawned in REVIEW MODE with symptom-vocabulary search keys extracted from the diff (error strings, metric/alert names, config keys — the vocabulary operators actually use).

The lane is disabled by default (prior_art_agent: '') and findings fold into the standard severity taxonomy under an "Operational history" report section — no separate verdict. Wire it up in a bundle or your local config:

variables:
  - name: prior_art_agent
    default: 'oncall-historian'  # any spawnable agent that can search your incident record

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 to see how to configure them), and modify the agent definition to look like this:

# ...

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

# ...