Provide implementations of advanced data structures
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a5c-ai/babysitter18572026年9月17日 更新
免费在 GitHub 公开
code-review
Structured code quality assessment with Conventional Comments format, scaled review depth, and soft-gating verdicts preserving user autonomy.
中文简介正在准备中。当前显示原文说明。
查看安装方法包含的文件（2）
- SKILL.md1.5 KB
- README.md445 B
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
- When reviewing code changes before merge
- As part of the /review-code command
Process
- Identify modified files via git
- Assess change magnitude for review depth
- Execute 9-step review: context, correctness, design, testing, security flags, operations, maintainability
- Synthesize findings in standardized report
- Deliver verdict with rationale
Review Depth Scaling
- Under 200 lines: full detail review
- 200-1000 lines: focused review on critical areas
- Over 1000 lines: architectural-level review only
Verdicts
- APPROVE: Ready for security review
- APPROVE WITH NITS: Non-blocking suggestions only
- REQUEST CHANGES: Blocking issues exist (user may override)
Key Rules
- Provide specific file paths and line numbers
- Include at least one positive comment per review
- Use Conventional Comments format with decorations
- Explain reasoning, not just observations
- Limit critical issues to top 5 per category
- Reviews are soft gates preserving user autonomy
Tool Use
Invoke via babysitter process: methodologies/rpikit/rpikit-review
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