
Code Reviewer
- 11 installs
- 2 repo stars
- Updated March 18, 2026
- masanao-ohba/claude-manifests
Helps with ai & agent building tasks.
About
code-reviewer is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.
- code-reviewer
- AI & Agent Building
- AI-coding skill
Code Reviewer by the numbers
- 11 all-time installs (skills.sh)
- Ranked #11,769 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
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| Installs | 11 |
|---|---|
| repo stars | ★ 2 |
| Last updated | March 18, 2026 |
| Repository | masanao-ohba/claude-manifests ↗ |
What it does
Helps with ai & agent building tasks.
Files
Code Reviewer
A technology-agnostic skill for systematic code review and quality assessment.
Project-specific review constraints should be defined in the project's CLAUDE.md.
Review Methodology
Systematic Review Process
process: structured_review
description: "Multi-pass review for comprehensive coverage"
passes:
1_correctness:
focus: "Does the code do what it's supposed to?"
checks:
- Logic errors
- Edge cases
- Error handling
- Business rule compliance
2_security:
focus: "Are there security vulnerabilities?"
checks:
- Input validation
- Authentication/authorization
- Data exposure
- Injection vulnerabilities
3_maintainability:
focus: "Is the code maintainable?"
checks:
- Naming clarity
- Code organization
- Documentation
- Complexity (cyclomatic)
4_performance:
focus: "Are there performance concerns?"
checks:
- Algorithm efficiency
- Resource usage
- Query optimization
- Caching opportunitiesSeverity Classification
Issue Severity Levels
severity_levels:
critical:
description: "Must fix before merge"
examples:
- Security vulnerabilities
- Data loss risk
- Production-breaking bugs
action: "BLOCK merge"
major:
description: "Should fix before merge"
examples:
- Logic errors
- Missing error handling
- Performance issues
action: "Request changes"
minor:
description: "Should fix, but can merge"
examples:
- Code style violations
- Minor inefficiencies
- Documentation gaps
action: "Suggest improvement"
info:
description: "Suggestions for improvement"
examples:
- Alternative approaches
- Refactoring opportunities
- Best practices
action: "Comment only"Review Checklists
Security Review Checklist
security_checklist:
input_validation:
- "All user inputs validated?"
- "SQL injection prevention?"
- "XSS prevention?"
- "Path traversal prevention?"
authentication:
- "Auth checks on all protected routes?"
- "Session management secure?"
- "Password handling correct?"
data_protection:
- "Sensitive data encrypted?"
- "PII handling compliant?"
- "Logs sanitized?"
authorization:
- "Role-based access enforced?"
- "Resource ownership verified?"
- "Privilege escalation prevented?"Code Quality Checklist
quality_checklist:
readability:
- "Variable names descriptive?"
- "Functions single-purpose?"
- "Comments explain why, not what?"
- "Magic numbers replaced with constants?"
structure:
- "DRY principle followed?"
- "Appropriate abstraction level?"
- "Dependencies minimized?"
- "Cyclomatic complexity acceptable?"
error_handling:
- "All error cases handled?"
- "Errors logged appropriately?"
- "User-facing errors clear?"
- "No silent failures?"
testing:
- "New code has tests?"
- "Edge cases covered?"
- "Tests are meaningful (not just coverage)?"Review Feedback Patterns
Constructive Feedback Template
feedback_structure:
what: "Describe the issue clearly"
why: "Explain why it's a problem"
how: "Suggest a solution or alternative"
severity: "Classify the severity"
example:
what: "This function catches all exceptions silently"
why: "Silent failures hide bugs and make debugging difficult"
how: "Log the exception and re-throw or handle specifically"
severity: "major"Feedback Tone Guidelines
tone_guidelines:
do:
- Ask questions rather than make accusations
- Focus on the code, not the person
- Acknowledge good patterns when seen
- Provide actionable suggestions
avoid:
- "Why would you do this?"
- "This is wrong"
- "Obviously you should..."
- Sarcasm or condescensionReview Metrics
Quality Score Calculation
quality_scoring:
categories:
correctness: 30
security: 25
maintainability: 25
performance: 20
calculation:
base_score: 100
deductions:
critical_issue: -25
major_issue: -10
minor_issue: -3
info_issue: 0
thresholds:
excellent: 90-100
good: 75-89
acceptable: 60-74
needs_work: 40-59
poor: 0-39Integration
Used By Agents
primary_users:
- quality-reviewer: "Core review methodology"
secondary_users:
- code-developer: "Self-review before submission"
- deliverable-evaluator: "Quality assessment criteria"Related skills
AI & Agent Buildingagents