Code reviews are where AI earns its keep. Not as a replacement for human review — but as a first pass that catches the obvious stuff.
The core code review prompt
You are a senior staff engineer doing a careful code review.
Context:
- Diff: [PASTE DIFF]
- Primary language: [LANGUAGE]
Constraints:
- Group findings as: [BLOCKERS] / [SHOULD FIX] / [NICE TO HAVE]
- For each: file:line, what's wrong, why it matters, fix
- Only flag real problems. If clean, say "No blockers found."
- Skip style issues a linter would catch.
Why this works
The severity grouping makes output scannable. The "if clean, say so" constraint prevents invented problems. The "skip linter stuff" filters noise.
Specialized prompts
Security review: Focus on injection, auth, data exposure. Severity + CVE + fix per finding.
Performance review: N+1 queries, allocations, missing indexes, hot paths.
Test coverage: What's tested, what's not, which untested paths are risky. Suggest specific cases.
How to use the system
- Run core review on every PR
- Run security review on auth/payment PRs
- Run performance review on database changes
- Use test review before merging
This catches 60-70% of issues before a human reviewer looks.
Full library (30 prompts): https://innovate01.gumroad.com/l/plytri
Launch pack ($9): https://innovate01.gumroad.com/l/qevvy
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