code-review
Structured code quality assessment with Conventional Comments format, scaled review depth, and soft-gating verdicts preserving user autonomy.
npx skills add a5c-ai/babysitter --skill code-review --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
- When reviewing code changes before merge - As part of the /review-code command ## Process 1. Identify modified files via git 2. Assess change magnitude for review depth 3. Execute 9-step review: context, correctness, design, testing, security flags, operations, maintainability 4. Synthesize findings in standardized report 5. 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`
- Process
- Review Depth Scaling
- Verdicts
- Key Rules
- Tool Use
What does the code-review skill do?
Structured code quality assessment with Conventional Comments format, scaled review depth, and soft-gating verdicts preserving user autonomy.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill code-review --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.