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code-review

This code review skill systematically evaluates Python code across five dimensions: correctness for logic errors and edge cases, security for input validation and credential exposure, performance for inefficient queries and memory usage, style for naming and function length, and testing for coverage gaps. Use it when submitting pull requests or before deploying code to catch bugs, vulnerabilities, and maintainability issues before they reach production.

Ver fuente Repositorio: pydantic-deepagents

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git clone --depth 1 https://github.com/vstorm-co/pydantic-deepagents /tmp/code-review && cp -r /tmp/code-review/pydantic_deep/bundled_skills/code-review ~/.claude/skills/code-review

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Definición

SKILL.md

# Code Review

Perform a systematic code review covering these categories:

## Review Checklist

### 1. Correctness
- Logic errors, off-by-one, null/None handling
- Edge cases: empty inputs, large inputs, concurrent access
- Error handling: are exceptions caught and handled properly?

### 2. Security
- Input validation and sanitization
- SQL injection, XSS, command injection
- Secrets in code, hardcoded credentials
- Authentication and authorization checks

### 3. Performance
- Unnecessary loops, N+1 queries
- Missing indexes for database queries
- Large memory allocations, unbounded collections
- Blocking calls in async code

### 4. Style & Maintainability
- Naming clarity (variables, functions, classes)
- Function length — split if >30 lines
- Dead code, commented-out code
- Missing type annotations

### 5. Testing
- Are new code paths covered by tests?
- Are edge cases tested?
- Are error paths tested?

## Output Format

For each issue found:
- **File:line** — category — description — suggested fix
- Severity: critical / warning / suggestion

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