Ordinary-claude-skills Reviewing Code

Systematically evaluate code changes for security, correctness, performance, and spec alignment. Use when reviewing PRs, assessing code quality, or verifying implementation against requirements.

install
source · Clone the upstream repo
git clone https://github.com/Microck/ordinary-claude-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/Microck/ordinary-claude-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills_categorized/code-quality/reviewing-code" ~/.claude/skills/microck-ordinary-claude-skills-reviewing-code-6f4ef5 && rm -rf "$T"
manifest: skills_categorized/code-quality/reviewing-code/SKILL.md
source content

Reviewing Code

Evaluate code changes across security, correctness, spec alignment, performance, and maintainability. Apply sequential or parallel review based on scope.

Quick Start

Sequential (small PRs, <5 files):

  1. Gather context from feature specs and acceptance criteria
  2. Review sequentially through focus areas
  3. Report findings by priority
  4. Recommend approval/revision/rework

Parallel (large PRs, >5 files):

  1. Identify independent review aspects (security, API, UI, data)
  2. Spawn specialist agents for each dimension
  3. Consolidate findings
  4. Report aggregate assessment

Context Gathering

Read documentation:

  • docs/feature-spec/F-##-*.md
    — Technical design and requirements
  • docs/user-stories/US-###-*.md
    — Acceptance criteria
  • docs/api-contracts.yaml
    — Expected API signatures
  • docs/data-plan.md
    — Event tracking requirements (if applicable)
  • docs/design-spec.md
    — UI/UX requirements (if applicable)
  • docs/system-design.md
    — Architecture patterns (if available)
  • docs/plans/<slug>/plan.md
    — Original implementation plan (if available)

Determine scope:

  • Files changed and features affected (F-## IDs)
  • Stories implemented (US-### IDs)
  • API, database, or schema changes

Quality Dimensions

Security (/25)

  • Input validation and sanitization
  • Authentication/authorization checks
  • Sensitive data handling
  • Injection vulnerabilities (SQL, XSS, etc.)

Correctness (/25)

  • Logic matches acceptance criteria
  • Edge cases handled properly
  • Error handling complete
  • Null/undefined checks present

Spec Alignment (/20)

  • APIs match
    docs/api-contracts.yaml
  • Data events match
    docs/data-plan.md
  • UI matches
    docs/design-spec.md
  • Implementation follows feature spec

Performance (/15)

  • Algorithm efficiency
  • Database query optimization
  • Resource usage (memory, network)

Maintainability (/15)

  • Code clarity and readability
  • Consistent with codebase patterns
  • Appropriate abstraction levels
  • Comments where needed

Total: /100

Finding Priority

🔴 CRITICAL (Must fix before merge)

  • Security vulnerabilities
  • Broken functionality
  • Spec violations (API contract breaks)
  • Data corruption risks

Format:

Location: file.ts:123
Problem: [Description]
Impact: [Risk/consequence]
Fix: [Specific change needed]
Spec reference: [docs/api-contracts.yaml line X]

🟡 IMPORTANT (Should fix)

  • Logic bugs in edge cases
  • Missing error handling
  • Performance issues
  • Missing analytics events
  • Accessibility violations

🟢 NICE-TO-HAVE (Optional)

  • Code style improvements
  • Better abstractions
  • Enhanced documentation

✅ GOOD PRACTICES

Highlight what was done well for learning

Review Strategies

Single-Agent Review

Best for <5 files, single concern:

  1. Review sequentially through focus areas
  2. Concentrate on 1-2 most impacted areas
  3. Generate unified report

Parallel Multi-Agent Review

Best for >5 files, multiple concerns:

  1. Spawn specialized agents:

    • Security:
      senior-engineer
      for vulnerability assessment
    • Architecture:
      Explore
      for pattern compliance
    • API Contracts:
      programmer
      for endpoint validation
    • Frontend:
      programmer
      for UI/UX and accessibility
    • Documentation:
      documentor
      for comment quality and docs
  2. Each agent reviews specific quality dimension

  3. Consolidate findings into single report

Report Structure

# Code Review: [Feature/PR]

## Summary
**Quality Score:** [X/100]
**Issues:** Critical: [N], Important: [N], Nice-to-have: [N]
**Assessment:** [APPROVE / NEEDS REVISION / MAJOR REWORK]

## Spec Compliance
- [ ] APIs match `docs/api-contracts.yaml`
- [ ] Events match `docs/data-plan.md`
- [ ] UI matches `docs/design-spec.md`
- [ ] Logic satisfies story AC

## Findings

### Critical Issues
[Issues with fix recommendations]

### Important Issues
[Issues that should be addressed]

### Nice-to-Have Suggestions
[Optional improvements]

### Good Practices
[What worked well]

## Recommendations
[Next steps: approval, revision needed, etc.]

Fix Implementation

Offer options:

  1. Fix critical + important issues
  2. Fix only critical (minimum for safety)
  3. Provide detailed explanation for learning
  4. Review only (no changes)

Parallel fixes for large revisions:

  • Spawn agents for independent fix areas
  • Coordinate on shared dependencies
  • Document each fix with location, change, and verification method

Document format:

✅ FIXED: [Issue name]
File: [path:line]
Change: [what changed]
Verification: [how to test]

Documentation Updates

Check if specs need updates:

  • Feature spec "Decisions" or "Deviations" if implementation differs
  • Design spec if UI changed
  • API contracts if endpoints modified (requires approval)
  • Data plan if events changed

Always flag for user approval before modifying specs.

Key Points

  • Read all context documents before starting
  • Focus on most impacted areas first
  • Be thorough with security-sensitive code, API changes, and critical user flows
  • Use scoring framework for comprehensive reviews
  • Parallel review scales to large PRs
  • Flag spec deviations for user decision