| title | AI PR Review Automation & Hooks |
|---|---|
| summary | Use AI as a first-pass reviewer and automate fast, deterministic quality checks at the right events |
Let AI shorten the first review pass; keep humans responsible for the merge decision.
- AI review is useful for finding patterns, missing tests, insecure defaults, and documentation gaps early.
- Use pull-request events, comments, schedules, and webhooks to start the right workflow.
- Hooks should be fast, idempotent, scoped, documented, and impossible to confuse with a security boundary.
- Never auto-merge critical code solely because an AI reviewer approved it.
- Configure one deterministic pre-commit check: formatter, linter, or type check that finishes quickly.
- Add a pre-push or CI job for the full test suite.
- Give the AI PR reviewer a focused rubric: correctness, tests, security, backward compatibility, and documentation.
- Trigger reviews on pull requests or an explicit comment, then require a human response to meaningful findings.
- Track false positives, missed findings, time-to-review, and bypasses; tune rules weekly.
The pull-request diff is a powerful context boundary: it focuses an AI reviewer on a proposed change, while repository instructions provide stable conventions. AI can triage and explain issues, but its comments are evidence to investigate—not a replacement for ownership, testing, or domain knowledge.
Hooks move low-cost quality controls to the event where they are most useful. Use pre-commit for fast local feedback, pre-push or CI for heavier testing, and agent/tool hooks for bounded, well-understood automation. A hook must be safe to rerun and must explain its failure clearly.
| Event | Good use | Guardrail |
|---|---|---|
| Pre-commit | Format, lint, focused type checks | Keep it fast; do not build the world |
| Pre-push / CI | Full tests, contract tests, scans | Cache safely; report actionable failures |
| Pull request | AI first-pass review, summaries, risk flags | Human owns disposition and merge |
| Comment / schedule | On-demand audit, stale-issue triage | Use clear authorization and rate limits |
An effective review rubric asks: Is behavior correct? Who owns each invariant? Does tenant or user context flow correctly? Are authorization and idempotency correct? What fails, how is it observed, and what data enters logs? This works for human and AI reviewers alike.
- Review-rubric design: convert team standards into specific questions and expected evidence.
- Event-driven automation: select the trigger and permissions appropriate for a workflow.
- Hook engineering: fast, idempotent, scoped checks with clear remediation.
- Metrics and calibration: measure quality impact and correct noisy automation.
- Use when: a team needs consistent first-pass review or recurring developer quality checks.
- Use when: security and test practices should be applied early and repeatedly.
- Do not use: an AI approval as the only safeguard for a merge, deployment, or privileged action.
- Auto-merge by AI: keep protected-branch checks and human approval requirements.
- Slow hooks: developers will bypass slow, unreliable local gates.
- Generic reviews: give agents repository instructions and a concrete rubric.
- Set-and-forget rules: review false positives and incidents; improve the prompt and checks.
- GitHub Actions workflow events — supported triggers for PR, issue, schedule, and webhook workflows.
- GitHub Copilot code review — configuring and using AI-assisted review.
- pre-commit — framework for portable local quality hooks.
- OWASP Code Review Guide — security review practices to turn into an explicit rubric.
- Give reviewers durable standards via Context, Model & Prompt.
- Integrate approved tools with Model Context Protocol.