code-reviewing

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Use when completing implementation, before escalating to human review, or when human checkpoint is reached - performs AI-assisted code review covering security, AI-specific issues, logic errors, and architecture to ensure humans see fresh analysis

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[Code Review]

When & Why to Use This Skill

This Claude skill provides a comprehensive AI-assisted code review framework designed to enhance software quality and developer productivity. It automates the detection of security vulnerabilities, logic errors, and architectural inconsistencies while specifically addressing common AI-generated code mistakes, ensuring that human reviewers receive only the most refined and high-quality code submissions.

Use Cases

  • Pre-PR Quality Assurance: Automatically analyze code changes for logic flaws and security risks before creating a pull request to reduce review cycles.
  • Security Vulnerability Scanning: Identify critical security issues such as hardcoded secrets, improper input validation, and unsafe query building during the execution stage.
  • Architectural Alignment: Verify that new code follows established design patterns, maintains correct dependency flows, and avoids circular dependencies.
  • AI-Generated Code Validation: Detect and correct specific issues common in AI-authored code, such as hallucinated APIs, outdated patterns, or silent failures.
  • Automated Reporting & Documentation: Generate structured AI review reports that integrate results from linters and type-checkers with deep semantic analysis for better project tracking.
namecode-reviewing
descriptionUse when completing implementation, before escalating to human review, or when human checkpoint is reached - performs AI-assisted code review covering security, AI-specific issues, logic errors, and architecture to ensure humans see fresh analysis

Code Reviewing

Perform AI-assisted code review immediately before escalating to human review. Ensures humans always see fresh, relevant code analysis when making decisions.

When to Use

  • After completing implementation work (executing stage)
  • Before any human checkpoint/escalation
  • When /AI-review is invoked
  • Before creating PRs or requesting human review

Quick Reference

Stage Focus Areas
concept Clarity, scope, testable criteria
planning Design quality, pattern adherence
executing Security, logic, architecture, tests
testing Integration, coverage, regressions
releasing All findings addressed, docs complete

Review Process

Phase 1: Gather Context

  1. Identify changed files - git diff or compare to last review
  2. Load task context - Read task description, acceptance criteria
  3. Check stage - Adjust review focus based on current stage

Phase 2: Run Automated Checks

# Python projects
python -m flake8 src/ || echo "No flake8"
python -m mypy src/ || echo "No mypy"

# JavaScript/TypeScript projects
npm run lint || echo "No linter"
npm run typecheck || echo "No type checker"

Phase 3: AI Analysis

See references/review-checklist.md for complete checklist.

Key areas:

Category Critical Checks
Security Input validation, no hardcoded secrets, parameterized queries
AI-specific No hallucinated APIs, follows project patterns, appropriate abstraction
Logic Edge cases (null, empty, boundary), error handling, no infinite loops
Architecture Follows conventions, correct dependency flow, no circular deps

Phase 4: Generate Report

## AI Code Review Report

**Task:** [ID] - [Title]
**Stage:** [current_stage]
**Files reviewed:** [count]
**Review date:** [timestamp]

### Summary
[1-2 sentence overall assessment]

### Findings

#### Critical (must fix)
- [ ] [Finding with file:line reference]

#### Warnings (should fix)
- [ ] [Finding with file:line reference]

#### Suggestions (consider)
- [ ] [Finding with file:line reference]

### Automated Check Results
- Linting: [pass/fail/skipped]
- Type check: [pass/fail/skipped]

### Recommended Actions
1. [Specific action item]

Phase 5: Save Review

  1. Update task notes - Append review summary
  2. Create artifact - Save to .claude/data/reviews/{task_id}_{timestamp}.md

Stage-Specific Focus

Concept Stage

  • Concept clearly defined?
  • Scope bounded and reasonable?
  • Acceptance criteria verifiable?

Planning Stage

  • Design follows established patterns?
  • Avoids known anti-patterns?
  • Plan is implementable?

Executing Stage (Full Review)

  • All security checks
  • All AI-specific checks
  • All logic checks
  • All architecture checks
  • Tests adequate for changes?
  • Code matches the plan?

Testing Stage

  • Integration interfaces properly defined?
  • Test coverage adequate?
  • Regression test coverage exists?

Releasing Stage

  • All previous findings addressed?
  • Documentation complete?
  • Release checklist items documented?

Common AI Code Mistakes

See references/common-ai-mistakes.md for complete patterns.

Issue Detection
Hallucinated APIs Verify imports/methods exist
Outdated patterns Check for deprecation warnings
Missing edge cases Test null, empty, boundaries
Security blind spots Check input validation, query building
Over-abstraction Is complexity justified?
Silent failures Look for bare except: pass

Reference Files

File Purpose
references/best-practices-research.md Industry research on AI code review
references/tool-capabilities.md What CodeRabbit, Copilot, Semgrep, SonarQube check
references/review-checklist.md Complete checklist by category
references/common-ai-mistakes.md Patterns specific to AI-generated code

Invocation

/AI-review --task-id=[ID] --stage=[current_stage]

Or simply /AI-review when context is clear from current work.