Sentry Code Review
SkillMonitoring & opsPerform code reviews following Sentry engineering practices. Use when reviewing pull requests, examining code changes, or providing feedback on code quality. Covers security, performance, testing, and design review.
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Also: Claude Code · Cursor · Codex
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Details
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What this skill tells your AI
The instructions your AI receives, as published by stolinski/s-stack in skills/code-review/SKILL.md and read by ahel’s review.
Follow these guidelines when reviewing code for Sentry projects.
Review Checklist
Identifying Problems
Look for these issues in code changes:
- Runtime errors: Potential exceptions, null pointer issues, out-of-bounds access
- Performance: Unbounded O(n²) operations, N+1 queries, unnecessary allocations
- Side effects: Unintended behavioral changes affecting other components
- Backwards compatibility: Breaking API changes without migration path
- ORM queries: Complex Django ORM with unexpected query performance
- Security vulnerabilities: Injection, XSS, access control gaps, secrets exposure
Design Assessment
- Do component interactions make logical sense?
- Does the change align with existing project architecture?
- Are there conflicts with current requirements or goals?
Test Coverage
Every PR should have appropriate test coverage:
- Functional tests for business logic
- Integration tests for component interactions
- End-to-end tests for critical user paths
Verify tests cover actual requirements and edge cases. Avoid excessive branching or looping in test code.
Long-Term Impact
Flag for senior engineer review when changes involve:
- Database schema modifications
- API contract changes
- New framework or library adoption
- Performance-critical code paths
- Security-sensitive functionality
Feedback Guidelines
Tone
- Be polite and empathetic
- Provide actionable suggestions, not vague criticism
- Phrase as questions when uncertain: "Have you considered...?"
Approval
- Approve when only minor issues remain
- Don't block PRs for stylistic preferences
- Remember: the goal is risk reduction, not perfect code
Common Patterns to Flag
Python/Django
# Bad: N+1 query
for user in users:
print(user.profile.name) # Separate query per user
# Good: Prefetch related
users = User.objects.prefetch_related('profile')
TypeScript/React
// Bad: Missing dependency in useEffect
useEffect(() => {
fetchData(userId);
}, []); // userId not in deps
// Good: Include all dependencies
useEffect(() => {
fetchData(userId);
}, [userId]);
Security
# Bad: SQL injection risk
cursor.execute(f"SELECT * FROM users WHERE id = {user_id}")
# Good: Parameterized query
cursor.execute("SELECT * FROM users WHERE id = %s", [user_id])
References
Signals
- GitHub stars
- 84
- Forks
- 2
- Last commit
- Jul 2026
ahel recommends instead
Advanced
- Item type
- skill
- Key
code-review-stolinski- Source
- github.com/stolinski/s-stack
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