code-review
SolidSystematic code review for bugs, security, style, and performance
Code & Development 859 stars
98 forks Updated yesterday MIT
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Quality Score: 93/100
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Recency 20%
Frontmatter 20%
Documentation 15%
Issue Health 10%
License 10%
Description 5%
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
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