code-reviewer▌
google-gemini/gemini-cli · updated Apr 8, 2026
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Automated code review for local changes and remote pull requests with structured analysis across correctness, maintainability, and security.
- ›Supports both local file system changes (staged and unstaged) and remote PRs (by number or URL) with automatic GitHub CLI checkout
- ›Analyzes code across seven dimensions: correctness, maintainability, readability, efficiency, security, edge case handling, and test coverage
- ›Runs optional preflight verification suites (e.g., npm run preflight ) to
Code Reviewer
This skill guides the agent in conducting professional and thorough code reviews for both local development and remote Pull Requests.
Workflow
1. Determine Review Target
- Remote PR: If the user provides a PR number or URL (e.g., "Review PR #123"), target that remote PR.
- Local Changes: If no specific PR is mentioned, or if the user asks to "review my changes", target the current local file system states (staged and unstaged changes).
2. Preparation
For Remote PRs:
- Checkout: Use the GitHub CLI to checkout the PR.
gh pr checkout <PR_NUMBER> - Preflight: Execute the project's standard verification suite to catch automated failures early.
npm run preflight - Context: Read the PR description and any existing comments to understand the goal and history.
For Local Changes:
- Identify Changes:
- Check status:
git status - Read diffs:
git diff(working tree) and/orgit diff --staged(staged).
- Check status:
- Preflight (Optional): If the changes are substantial, ask the user if they want to run
npm run preflightbefore reviewing.
3. In-Depth Analysis
Analyze the code changes based on the following pillars:
- Correctness: Does the code achieve its stated purpose without bugs or logical errors?
- Maintainability: Is the code clean, well-structured, and easy to understand and modify in the future? Consider factors like code clarity, modularity, and adherence to established design patterns.
- Readability: Is the code well-commented (where necessary) and consistently formatted according to our project's coding style guidelines?
- Efficiency: Are there any obvious performance bottlenecks or resource inefficiencies introduced by the changes?
- Security: Are there any potential security vulnerabilities or insecure coding practices?
- Edge Cases and Error Handling: Does the code appropriately handle edge cases and potential errors?
- Testability: Is the new or modified code adequately covered by tests (even if preflight checks pass)? Suggest additional test cases that would improve coverage or robustness.
4. Provide Feedback
Structure
- Summary: A high-level overview of the review.
- Findings:
- Critical: Bugs, security issues, or breaking changes.
- Improvements: Suggestions for better code quality or performance.
- Nitpicks: Formatting or minor style issues (optional).
- Conclusion: Clear recommendation (Approved / Request Changes).
Tone
- Be constructive, professional, and friendly.
- Explain why a change is requested.
- For approvals, acknowledge the specific value of the contribution.
5. Cleanup (Remote PRs only)
- After the review, ask the user if they want to switch back to the default branch (e.g.,
mainormaster).
How to use code-reviewer on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add code-reviewer
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches code-reviewer from GitHub repository google-gemini/gemini-cli and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate code-reviewer. Access the skill through slash commands (e.g., /code-reviewer) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share 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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★47 reviews- ★★★★★Ganesh Mohane· Dec 24, 2024
code-reviewer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sofia Huang· Dec 24, 2024
code-reviewer has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ira Taylor· Dec 16, 2024
code-reviewer reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Alexander Wang· Dec 12, 2024
Useful defaults in code-reviewer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Hana Khan· Nov 19, 2024
Solid pick for teams standardizing on skills: code-reviewer is focused, and the summary matches what you get after install.
- ★★★★★Sakshi Patil· Nov 15, 2024
Keeps context tight: code-reviewer is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Xiao Gonzalez· Nov 15, 2024
Useful defaults in code-reviewer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ishan Srinivasan· Nov 7, 2024
Registry listing for code-reviewer matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Maya Okafor· Nov 3, 2024
code-reviewer has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Daniel Chen· Oct 26, 2024
code-reviewer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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