agentic-code-review

name

agentic-code-review

description

Evidence-gated, risk-tiered AI-assisted code review workflow compatible with Codex and Claude Code. Use when reviewing a PR/current branch/diff, reviewing agent-generated code, validating review findings, triaging batches of PRs, checking merge readiness, auditing high-volume agent review loops, calibrating AI reviewers, or reviewing AI-generated code. Defaults to review-only. Emphasizes reviewability intake, Not reviewable verdicts, cheap review-effort circuit breakers, risk tiering, test-change scrutiny, heterogeneous AI review evidence, CI/gate integrity, LLM/prompt security, optional diff measurement, optional fresh re-review via review-fix-loop, and human merge ownership.

Agentic Code Review

Purpose

Use this Skill to review code changes by risk, not by author. Preserve human attention for changes where being wrong is expensive.

Default to review-only. Do not edit files, commit, push, publish, install tools, or contact external systems unless the user explicitly asks for that action.

Workflow

1. Load repo-local instructions first: AGENTS.md, CLAUDE.md, module rules, and project review docs.
2. Determine the review target: current diff, branch, PR, pasted finding, or merge-readiness question.
3. Check reviewability before deep review. Read references/review-intake.md when intent, scope, decision log, validation evidence, test output, human owner, or diff readability is incomplete.
4. Use cheap review-effort signals before expensive review. Read references/review-effort-signals.md for large, mixed, generated, high-churn, or unclear changes.
5. Classify risk using blast radius, code lifetime, and shared understanding. Read references/risk-model.md when risk is not obvious.
6. Map the highest-risk slice to L0-L4 review depth. Read references/review-depth.md for required depth.
7. Inspect test changes separately before trusting implementation changes. Read references/test-change-review.md whenever tests changed.
8. Read references/llm-security-review.md for changes that route untrusted text into prompts, LLM calls, agent loops, retrieval, or tool execution.
9. Preserve CI and gate integrity. Read references/ci-gate-integrity.md when workflows, lint, coverage, security scans, dependency policy, release metadata, or required checks changed.
10. Separate confirmed defects from Needs confirmation. Do not present assumptions as verified facts.
11. Use references/output-format.md for final review output.

Reference Routing

For batch PR or multi-diff triage, read references/batch-triage.md and treat the output as attention allocation, not merge approval.

For high-risk review, multi-pass review, or AI-reviewer comparison, read references/heterogeneous-reviewers.md. Read references/reviewer-prompts.md when concrete independent reviewer prompts are useful.

When the user asks to run local independent reviewer passes, use assets/review-prompt-manifest.json, assets/review-runner.config.example.json, and scripts/run_review_passes.py. Treat the runner report as review evidence, not merge approval.

For high-volume agent review loops, sampling, spot-checking, or model-only review audit, read references/human-on-the-loop-audit.md.

For solo, small-team, or large-system adoption profiles and transition triggers, read references/adoption.md.

For team rollout, process metrics, review-capacity questions, or reviewer calibration, read references/team-adoption-metrics.md.

For output examples or forward-testing the Skill on realistic review tasks, read references/examples.md.

Optional review-fix-loop

review-fix-loop is optional. Use it only when the user explicitly asks for it, a repository configuration is present, or the task asks for review/fix/re-review until clean.

When active, read references/integrations/review-fix-loop.md and use it as the fresh-snapshot execution contract.

Merge Verdict

Give Ready, Not ready, Needs confirmation, or Not reviewable only when the evidence supports it. For high-risk changes, cite validation commands or state what could not be run.