AgentSkillsCN

coderabbit-review-skill

使用 coderabbit CLI 运行代码审查,并查看输出结果。

SKILL.md
--- frontmatter
name: coderabbit-review-skill
description: Run a codereview using coderabbit cli and review the output.

CodeRabbit Review

Run the command coderabbit --prompt-only > .ai_agents/coderabbit_output.txt and wait for it to finish (it may take a while). If it's slow, run it in the background and keep working. Give it a timeout of up to 5 minutes since it can take a while on large codebases.

Redirect its output to a file, e.g., coderabbit --prompt-only > .ai_agents/coderabbit_output.txt. Read the file once the command completes. If the file already exists, delete it first to avoid confusion.

the coderabbit cli is installed and available in PATH.

Workflow

  1. Collect and triage comments

    • Collect ALL CodeRabbit comments and de-duplicate them

    • The output will contain multiple prompts, each already in this shape:

      ============================================================================ File: <path> Line: <line number or range> Type: <critical|major|potential_issue|refactor_suggestion|nitpick|...>

      Prompt for AI Agent: <one paragraph, specific instruction>

    • Prioritize by Type:

      • Handle ALL non-nitpick items first. Start with highest severity and work down.
        • Focus first on: critical, major, potential_issue, refactor_suggestion (and similar non-nitpick types).
      • Only after there are zero remaining non-nitpick items, process nitpick items.
      • If any non-nitpick items exist, ignore nitpicks for now (do not implement, do not bikeshed).
    • Triage each comment into:

      • (a) valid + actionable
      • (b) valid but out-of-scope
      • (c) invalid/incorrect
      • (d) needs more info
  2. Validate comments with parallel sub-agents

    • For each major comment/theme, spawn a sub-agent to find supporting evidence in the repo (file paths, symbols, concrete snippets)
    • Each sub-agent returns: verdict (valid/invalid), reasoning, exact location(s), and suggested minimal fix
  3. Produce validated findings list

    • For each valid item: include category (bug/security/perf/style/test/docs), impacted files, risk, and proposed fix
    • For invalid items: briefly explain why, citing repo evidence
  4. Implement fixes for valid + actionable items

    • Use parallel sub-agents for independent fix groups (don't overlap files)
    • Keep changes minimal; do not refactor unrelated code
    • Update tests or add new tests when behavior changes
  5. Verify

    • Run the narrowest relevant tests first, then broader tests if available
    • Run format/lint only on changed areas if feasible
  6. Final output

    • List of validated comments (valid/invalid/out-of-scope) with evidence
    • Summary of changes by file (what/why)
    • Commands run + results (tests/lint/format)
    • Any remaining items and recommended next steps

Constraints

  • Don't "fix" speculative issues without repo evidence
  • If a fix is ambiguous, propose 1-2 options and let the user pick the safest default
  • Stop and ask if blocked by missing context or conflicting requirements
  • Time budget: prioritize highest-risk issues first
  • Scope limit: Touch only relevant files unless required for correctness
  • Risk tolerance: Prefer smallest safe change; avoid API changes
  • Test strategy: Specify preferred commands and fallbacks
  • Parallelism: Use up to 4 sub-agents; do not overlap files
  • Run formatting and linters to ensure code quality standards are met

Optional arguments

ultrathink