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pr-review▌

minimax-ai/skills · updated Apr 8, 2026

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$npx skills add https://github.com/minimax-ai/skills --skill pr-review
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summary

Review pull requests against repository standards. Two-phase process: automated validation, then manual content review.

skill.md

PR Review Skill

Review pull requests against repository standards. Two-phase process: automated validation, then manual content review.

Phase 1: Automated Validation (Hard Rules)

Run the validation script to check structural requirements:

python .claude/skills/pr-review/scripts/validate_skills.py

The script checks:

  • SKILL.md exists in every skill directory
  • YAML frontmatter is parseable
  • Required fields present: name, description
  • name matches directory name
  • No hardcoded secrets detected

All ERROR-level checks must pass. WARNING-level items (missing license, metadata) should be flagged but are not blockers.

See references/structure-rules.md for the complete hard rules specification.

Phase 2: Content Review (Soft Guidelines)

After automated checks pass, review the PR against quality guidelines:

  1. Skill scope — Does it overlap with existing skills? Is the boundary clear?
  2. Description quality — Does the description include clear trigger conditions?
  3. File size — Are reference docs reasonably sized for context window consumption?
  4. API key handling — If external APIs are used, are credentials read from environment variables?
  5. Script quality — Do scripts have shebang, requirements.txt, and error handling?
  6. Language — Are SKILL.md and code written in English?
  7. README sync — Are README.md and README_zh.md updated for new skills?

See references/quality-guidelines.md for soft guidelines details.

Review Checklist Summary

Must Pass (Blockers)

  • validate_skills.py exits with code 0
  • PR title follows conventional commit format
  • One PR, one purpose

Should Pass (Flagged in Review)

  • No functional overlap with existing skills
  • Description includes trigger conditions
  • Files are reasonably sized
  • API keys via environment variables
  • README tables updated for new skills (Source column set to Community)
how to use pr-review

How to use pr-review on Cursor

AI-first code editor with Composer

1

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 pr-review
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/minimax-ai/skills --skill pr-review

The skills CLI fetches pr-review from GitHub repository minimax-ai/skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/pr-review

Reload or restart Cursor to activate pr-review. Access the skill through slash commands (e.g., /pr-review) 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.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.8★★★★★36 reviews
  • ★★★★★Shikha Mishra· Dec 28, 2024

    Useful defaults in pr-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Yuki Taylor· Dec 24, 2024

    pr-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Nikhil Mensah· Dec 4, 2024

    pr-review has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Yash Thakker· Nov 19, 2024

    pr-review has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Sakshi Patil· Nov 15, 2024

    Registry listing for pr-review matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Luis Agarwal· Nov 15, 2024

    pr-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Valentina Ghosh· Nov 7, 2024

    pr-review reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Mateo Torres· Oct 26, 2024

    Registry listing for pr-review matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Dhruvi Jain· Oct 10, 2024

    Solid pick for teams standardizing on skills: pr-review is focused, and the summary matches what you get after install.

  • ★★★★★Chaitanya Patil· Oct 6, 2024

    pr-review reduced setup friction for our internal harness; good balance of opinion and flexibility.

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