evalview/examples/skills/code-reviewer/SKILL.md
Performs comprehensive code reviews with security, quality, and best practice checks
npx skillsauth add hidai25/eval-view code-reviewerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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A comprehensive code review assistant that analyzes code for bugs, security vulnerabilities, performance issues, and best practices.
Activate this skill when the user asks you to:
First, determine what code to review:
git diff --stagedgit diff HEAD~1Analyze the code across these dimensions:
Create a review document with this structure:
# Code Review Summary
## Overview
[Brief summary of what was changed and overall assessment]
## Critical Issues 🔴
[Issues that MUST be fixed before merging]
## Important Issues 🟡
[Issues that should be addressed soon]
## Suggestions 🔵
[Nice-to-have improvements]
## Security Analysis
[Security-specific findings]
## Positives ✅
[What was done well - be specific]
## Next Steps
[Concrete action items with priority]
Good Review Comment:
❌ Instead of: "This is bad"
✅ Better: "This SQL query is vulnerable to injection. Consider using parameterized queries:
// Current (unsafe):
query = f"SELECT * FROM users WHERE id = {user_id}"
// Better (safe):
query = "SELECT * FROM users WHERE id = ?"
params = [user_id]
When suggesting changes, show before/after code:
# Before
def process_data(data):
result = []
for item in data:
if item['status'] == 'active':
result.append(item)
return result
# After (more Pythonic)
def process_data(data):
return [item for item in data if item.get('status') == 'active']
Save the review to a file:
code-review.md (default)Before finalizing, ensure you've checked:
User: "Review my staged changes"
You:
git diff --staged to see changesUser: "Review the authentication code in auth.py"
You:
auth.pyUser: "Review PR #123"
You:
gh pr diff 123 to get the diffAlways save reviews to a file (don't just print to console). Use clear markdown formatting with:
testing
Start EvalView watch mode to automatically re-run regression checks whenever project files change.
development
Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes.
testing
Generate EvalView test cases — either from a SKILL.md file using LLM-powered generation, or by capturing real agent interactions through a proxy.
development
A skill that helps review code for best practices, bugs, and security issues