Agent skill
ai-review
Use when reviewing code changes (PRs, diffs, or files) with parallel specialized agents. 8-agent review with self-challenge protocol and cross-agent corroboration.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/ai-review-arcasilesgroup-ai-engineering
SKILL.md
Review
Purpose
Parallel specialized code review. Dispatches 8 review agents, each analyzing the same code from a different angle. Every agent argues AGAINST its own findings (self-challenge). Cross-agent corroboration filters noise from signal.
When to Use
- Before merging a PR
- After completing a feature (pre-commit review)
- When reviewing someone else's code
- Periodic architecture review
Process
- Explore first -- run
/ai-exploreon the changed files to build architectural context - Dispatch reviewers -- follow
handlers/review.md(8 parallel specialized agents) - Aggregate findings -- correlate, deduplicate, confidence-score
- Self-challenge -- each finding is argued against by its own agent
- Filter -- drop solo findings below 40% confidence
- Report -- produce review summary with actionable findings
See handlers/review.md for the full review workflow, handlers/find.md for finding existing reviews, and handlers/learn.md for the continuous improvement loop.
Quick Reference
| Mode | What it does |
|---|---|
review |
Full parallel review (default) |
find |
Find and summarize existing review comments on a PR |
learn |
Extract lessons from past reviews for future improvement |
The 8 Review Agents
| Agent | Focus | Looks for |
|---|---|---|
| Security | OWASP, injection, auth | SQL injection, XSS, auth bypass, secret exposure |
| Performance | Speed, memory, I/O | N+1 queries, O(n^2), memory leaks, blocking I/O |
| Correctness | Logic, edge cases | Off-by-one, null handling, race conditions, missing cases |
| Maintainability | Readability, complexity | God functions, deep nesting, unclear naming, magic numbers |
| Testing | Coverage, quality | Missing tests, weak assertions, testing implementation |
| Compatibility | Breaking changes, API | Public API changes, backward compat, deprecation |
| Architecture | Boundaries, patterns | Layer violations, circular deps, pattern inconsistency |
| Frontend | UX, a11y, rendering | Missing aria labels, layout shifts, unhandled states |
Confidence Scoring
Each finding gets a confidence score (20-100%):
| Score | Meaning | Action |
|---|---|---|
| 80-100% | High confidence, clear evidence | Must address |
| 60-79% | Moderate confidence | Should address |
| 40-59% | Low confidence, single agent | Consider |
| 20-39% | Solo finding, uncertain | Dropped unless critical severity |
Corroboration bonus: when 2+ agents flag the same issue, confidence increases by 20%. Solo penalty: single-agent findings below 40% are dropped from the report.
Self-Challenge Protocol
For each finding, the reviewing agent must:
- State the finding (what is wrong)
- Argue against it (why this might be acceptable)
- Resolve (finding stands, confidence adjusted, or finding withdrawn)
Example:
Finding: Function handles 5 different concerns (god function)
Counter: This is a CLI command handler -- some breadth is expected in the entry point
Resolution: Finding stands but severity reduced to minor. The handler delegates
to helpers for the complex logic. Confidence: 55%
Common Mistakes
- Reviewing without architectural context (always explore first)
- Treating all findings equally (use confidence scoring)
- Not self-challenging (every finding must be argued against)
- Reviewing only the diff without understanding the surrounding code
- Flagging style preferences as bugs
Integration
- Called by: user directly,
/ai-pr(pre-merge review) - Calls:
/ai-explore(context),handlers/review.md,handlers/find.md,handlers/learn.md - Read-only: never modifies code -- produces review findings
$ARGUMENTS
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