Code Review
SkillDev toolsSystematic code analysis with evidence collection
Available today. Use it from your connected AI after setup.
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Code Review skill
What this skill tells your AI
The instructions your AI receives, as published by simhacker/moollm in skills/code-review/SKILL.md and read by ahel’s review.
"Read with intent. Question with purpose. Document with care."
Systematic code analysis with evidence collection. Code review IS an adventure — the codebase is the dungeon, findings are clues.
Review Process
READ → NOTE ISSUES → CLASSIFY → REPORT
Step 1: Setup
- Create REVIEW.yml
- Identify files to review
- Define focus areas
Step 2: Overview
- List all changed files
- Read PR/commit description
- Note initial impressions
Step 3: Deep Review
For each file:
- Read the code
- Check against criteria
- Note findings
- Run relevant checks
Step 4: Verification
- Run tests
- Run linters
- Check regressions
Step 5: Synthesize
- Compile findings
- Prioritize issues
- Generate REVIEW.md
- State recommendation
Finding Severity
| Level | Symbol | Meaning | Action |
|---|---|---|---|
| Blocking | 🚫 | Must fix before merge | Request changes |
| Important | ⚠️ | Should fix or explain | Request changes |
| Minor | 💡 | Nice to fix | Comment only |
| Praise | 🎉 | Good work! | Celebrate |
Finding Types
- Security — Injection, auth, sensitive data
- Correctness — Logic errors, edge cases
- Performance — N+1 queries, memory leaks
- Maintainability — Clarity, DRY, naming
- Style — Formatting, conventions
Review Checklist
Security
- Input validation
- Output encoding
- Authentication/authorization
- Sensitive data handling
- Injection vulnerabilities
- Timing attacks
Correctness
- Logic errors
- Edge cases handled
- Null/undefined handling
- Error handling
- Race conditions
- Resource cleanup
Maintainability
- Code clarity
- Appropriate comments
- Consistent naming
- DRY (no duplication)
- Single responsibility
- Testability
Performance
- Algorithmic complexity
- Memory usage
- Database queries
- Caching
- Unnecessary operations
Core Files
REVIEW.yml
review:
name: "PR #123: Add user authentication"
status: "in_progress"
findings:
blocking:
- id: "B1"
file: "src/auth/login.ts"
line: 45
type: "security"
summary: "Timing attack vulnerability"
important: []
minor: []
praise: []
verification:
tests: { ran: true, passed: true }
linter: { ran: true, passed: false, issues: 3 }
REVIEW.md
Formatted document with:
- Summary and counts
- Issues by severity
- Verification results
- Recommendation
Verification Commands
tests:
- "npm test"
- "pytest"
- "go test ./..."
linters:
- "npm run lint"
- "flake8"
- "golangci-lint run"
Recommendation Output
| Outcome | Meaning |
|---|---|
approve | Good to merge |
request_changes | Has blocking/important issues |
comment | Minor feedback only |
See Also
- rubric — Explicit scoring criteria for code quality
- evaluator — Independent assessment pattern
- adversarial-committee — Multiple reviewers debating findings
Signals
- GitHub stars
- 52
- Forks
- 5
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
code-review-simhacker- Source
- github.com/simhacker/moollm