Load with: base.md + [codex-review.md for OpenAI Codex] + [gemini-review.md for Google Gemini]
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncode-reviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches code-review from alinaqi/claude-bootstrap and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate code-review. Access via /code-review in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Load with: base.md + [codex-review.md for OpenAI Codex] + [gemini-review.md for Google Gemini]
Purpose: Enforce automated code reviews as a mandatory guardrail before every commit and deployment. Choose between Claude, OpenAI Codex, Google Gemini, or multiple engines for comprehensive analysis.
When running /code-review, users can choose their preferred review engine:
┌─────────────────────────────────────────────────────────────────┐
│ CODE REVIEW - Choose Your Engine │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ○ Claude (default) │
│ Built-in, no extra setup, full conversation context │
│ │
│ ○ OpenAI Codex CLI │
│ GPT-5.2-Codex specialized for code review, 88% detection │
│ Requires: npm install -g @openai/codex │
│ │
│ ○ Google Gemini CLI │
│ Gemini 2.5 Pro with 1M token context, free tier available │
│ Requires: npm install -g @google/gemini-cli │
│ │
│ ○ Dual Engine (any two) │
│ Run two engines, compare findings, catch more issues │
│ │
│ ○ All Three (maximum coverage) │
│ Run Claude + Codex + Gemini for critical/security code │
│ │
└─────────────────────────────────────────────────────────────────┘
| Aspect | Claude | Codex | Gemini | Multi-Engine |
|---|---|---|---|---|
| Setup | None | npm + OpenAI API | npm + Google Account | All setups |
| Speed | Fast | Fast | Fast | 2-3x time |
| Context | Conversation | Fresh per review | 1M tokens | N/A |
| Detection | Good | 88% (best) | 63.8% SWE-Bench | Combined |
| Free Tier | N/A | Limited | 1,000/day | Varies |
| Best for | Quick reviews | High accuracy | Large codebases | Critical code |
# ~/.claude/settings.toml or project CLAUDE.md
[code-review]
default_engine = "claude" # Options: claude, codex, gemini, dual, all
# Use default engine
/code-review
# Explicitly choose engine
/code-review --engine claude
/code-review --engine codex
/code-review --engine gemini
# Dual engine (pick any two)
/code-review --engine claude,codex
/code-review --engine claude,gemini
/code-review --engine codex,gemini
# All three engines
/code-review --engine all
# Quick shortcuts
/code-review # Uses default
/code-review --codex # Use Codex
/code-review --gemini # Use Gemini
/code-review --all # All three engines
When using multiple engines, findings are compared and deduplicated:
┌─────────────────────────────────────────────────────────────────┐
│ CODE REVIEW RESULTS - DUAL ENGINE (Claude + Codex) │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ✅ AGREED (Found by both): │
│ 🔴 SQL injection in auth.ts:45 │
│ 🟡 Missing error handling in api.ts:112 │
│ │
│ 🔷 CLAUDE ONLY: │
│ 🟠 Potential race condition in worker.ts:89 │
│ 🟢 Consider extracting helper function │
│ │
│ 🔶 CODEX ONLY: │
│ 🟠 Memory leak - unclosed stream in upload.ts:34 │
│ 🟡 N+1 query pattern in orders.ts:156 │
│ │
├─────────────────────────────────────────────────────────────────┤
│ SUMMARY │
│ Agreed: 2 | Claude only: 2 | Codex only: 2 │
│ Critical: 1 | High: 2 | Medium: 2 | Low: 1 │
│ Status: ❌ BLOCKED - Fix critical/high issues │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ CODE REVIEW RESULTS - TRIPLE ENGINE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ✅ UNANIMOUS (All 3 found): │
│ 🔴 SQL injection in auth.ts:45 │
│ │
│ ✅ MAJORITY (2 of 3 found): │
│ 🟠 Memory leak - unclosed stream in upload.ts:34 (Codex+Gemini)│
│ 🟡 Missing error handling in api.ts:112 (Claude+Codex) │
│ │
│ 🔷 CLAUDE ONLY: │
│ 🟠 Potential race condition in worker.ts:89 │
│ │
│ 🔶 CODEX ONLY: │
│ 🟡 N+1 query pattern in orders.ts:156 │
│ │
│ 🟢 GEMINI ONLY: │
│ 🟡 Consider using batch API for better performance │
│ 🟢 Type could be more specific in types.ts:23 │
│ │
├─────────────────────────────────────────────────────────────────┤
│ SUMMARY │
│ Unanimous: 1 | Majority: 2 | Single: 5 │
│ Critical: 1 | High: 2 | Medium: 3 | Low: 2 │
│ Status: ❌ BLOCKED - Fix critical/high issues │
└─────────────────────────────────────────────────────────────────┘
| Mode | Use When |
|---|---|
| Single (Claude) | Quick in-flow reviews, exploration |
| Single (Codex) | CI/CD automation, high accuracy needed |
| Single (Gemini) | Large codebases (100+ files), free tier |
| Dual | Important PRs, pre-merge reviews |
| Triple (All) | Security-critical code, payment systems, auth |
┌─────────────────────────────────────────────────────────────────┐
│ CODE REVIEW IS NON-NEGOTIABLE │
│ ───────────────────────────────────────────────────────────── │
│ │
│ Every commit must pass code review. │
│ Every PR must be reviewed before merge. │
│ Every deployment must include review sign-off. │
│ │
│ AI catches what humans miss. Humans catch what AI misses. │
│ Together: fewer bugs, cleaner code, better security. │
├─────────────────────────────────────────────────────────────────┤
│ INVOKE: /code-review │
│ PLUGIN: code-review@claude-plugins-official │
└─────────────────────────────────────────────────────────────────┘
| Trigger | Action | Command |
|---|---|---|
| Before commit | Review staged changes | /code-review |
| Before PR | Review all changes vs base | /code-review |
| Before merge | Final review of PR | /code-review |
| Before deploy | Review deployment diff | /code-review |
Run code review automatically before every commit:
┌─────────────────────────────────────────────────────────────────┐
│ COMMIT WORKFLOW │
│ ───────────────────────────────────────────────────────────── │
│ │
│ 1. Write code │
│ 2. Run tests (TDD - must pass) │
│ 3. Run /code-review ← MANDATORY │
│ 4. Address critical/high issues │
│ 5. Commit │
│ 6. Push │
│ │
│ Skip step 3? ❌ NO COMMIT ALLOWED │
└─────────────────────────────────────────────────────────────────┘
# Review current changes
/code-review
# Review specific files
/code-review src/auth/*.ts
# Review a PR
/code-review --pr 123
# Review with specific focus
/code-review --focus security
/code-review --focus performance
/code-review --focus architecture
The code review plugin analyzes:
| Category | What It Checks |
|---|---|
| Security | Vulnerabilities, injection risks, auth issues, secrets |
| Performance | N+1 queries, memory leaks, inefficient algorithms |
| Architecture | Design patterns, SOLID principles, coupling |
| Code Quality | Readability, complexity, duplication |
| Best Practices | Language idioms, framework conventions |
| Testing | Coverage gaps, test quality, edge cases |
| Documentation | Missing docs, outdated comments |
| Level | Action Required | Can Commit? |
|---|---|---|
| 🔴 Critical | Must fix immediately | ❌ NO |
| 🟠 High | Should fix before commit | ❌ NO |
| 🟡 Medium | Fix soon, can commit | ✅ YES |
| 🟢 Low | Nice to have | ✅ YES |
| ℹ️ Info | Suggestions only | ✅ YES |
#!/bin/bash
# .git/hooks/pre-commit
echo "🔍 Running code review..."
# Run Claude code review on staged files
STAGED_FILES=$(git diff --cached --name-only --diff-filter=ACM | grep -E '\.(ts|tsx|js|jsx|py|go|rs)$')
if [ -n "$STAGED_FILES" ]; then
# Invoke code review (requires claude CLI)
claude --print "/code-review $STAGED_FILES" > /tmp/code-review-result.txt 2>&1
# Check for critical/high issues
if grep -q "🔴\|Critical\|🟠\|High" /tmp/code-review-result.txt; then
echo "❌ Code review found critical/high issues:"
cat /tmp/code-review-result.txt
echo ""
echo "Fix these issues before committing."
exit 1
fi
echo "✅ Code review passed"
fi
exit 0
chmod +x .git/hooks/pre-commit
If you want to use Codex or Both modes, install the Codex CLI:
# Prerequisites: Node.js 22+
node --version # Must be 22+
# Install Codex CLI
npm install -g @openai/codex
# Authenticate (choose one):
# Option 1: ChatGPT subscription (Plus, Pro, Team, Enterprise)
codex # Follow prompts to sign in
# Option 2: API key
export OPENAI_API_KEY=sk-proj-...
✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.5★★★★★57 reviews
Useful defaults in code-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
code-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for code-review matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend code-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added code-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
code-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: code-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
code-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
code-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for code-review matched our evaluation — installs cleanly and behaves as described in the markdown.
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