Code Review Assistant
Turn AI into a structured pull request reviewer that catches risky changes, flags maintainability issues, and suggests missing test coverage.
Overview
A structured system prompt that configures an AI assistant to perform disciplined code reviews. Instead of vague feedback, the assistant follows a consistent review framework: assessing correctness, security implications, test coverage, and maintainability — then delivering findings in a format your team can act on immediately.
Workflow
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Copy the diff or file
Paste the raw diff from your PR, or the full file if reviewing a new addition.
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Run through the assistant
Send the pasted code to your AI model with this prompt as the system instruction.
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Triage action items
Work through the numbered action items. Dismiss or address each one before merging.
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Iterate on re-diffs
After addressing feedback, re-run the revised diff to confirm all action items are resolved.
Why This Workflow Works
- Structured headings force the AI to address each review dimension rather than producing a general impression
- Separating correctness, security, and maintainability prevents one dimension from dominating the output
- Numbered action items make findings directly assignable — vague feedback creates follow-up questions
- Instructing the model not to summarize what the code does eliminates the most common AI review waste
Best for
- Teams without dedicated code review tooling
- Solo developers who want a disciplined second opinion
- Pull requests touching security-sensitive paths
- Onboarding reviews where thoroughness matters
Not for
- Style-only linting — use a formatter or linter instead
- Auto-fixing issues — review output is read-only by design
- Performance profiling without benchmark data
Use cases
- Reviewing pull requests before merging to main
- Catching security regressions in authentication or authorization code
- Ensuring new engineers receive consistent, structured feedback
- Auditing legacy code before a major refactor