Agent skill

code-review

Perform 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.

Stars 23,776
Forks 2,298

Install this agent skill to your Project

npx add-skill https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/sentry/code-review

SKILL.md

Sentry Code 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

python
# 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

typescript
// Bad: Missing dependency in useEffect
useEffect(() => {
  fetchData(userId);
}, []);  // userId not in deps

// Good: Include all dependencies
useEffect(() => {
  fetchData(userId);
}, [userId]);

Security

python
# 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

Expand your agent's capabilities with these related and highly-rated skills.

davila7/claude-code-templates

verl-rl-training

Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.

23,776 2,298
Explore
davila7/claude-code-templates

openrlhf-training

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

23,776 2,298
Explore
davila7/claude-code-templates

gguf-quantization

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

23,776 2,298
Explore
davila7/claude-code-templates

Claude Code Guide

Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best practices for interacting with the agent.

23,776 2,298
Explore
davila7/claude-code-templates

qdrant-vector-search

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

23,776 2,298
Explore
davila7/claude-code-templates

behavioral-modes

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

23,776 2,298
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results