code-review

Solid

Systematic code review for bugs, security, style, and performance

Code & Development 859 stars 98 forks Updated yesterday MIT

Install

View on GitHub

Quality Score: 93/100

Stars 20%
98
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
53
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# 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

Details

Author
vstorm-co
Repository
vstorm-co/pydantic-deepagents
Created
6 months ago
Last Updated
yesterday
Language
Python
License
MIT

Integrates with

Similar Skills

Semantically similar based on skill content — not just same category