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pr-review

pr-review

github.com/minimax-aiTesting & quality13.7k1.8k installsUpdated 28w ago
Repository

About pr-review

pr-review is a skill for reviewing pull requests that add repository skills. It combines a script-based structural check with a manual review of content quality and repository conventions.

Key features

  1. 01Validate skill files and YAML frontmatter
  2. 02Check required fields and directory-name matches
  3. 03Detect hardcoded secrets
  4. 04Review scope, descriptions, scripts, and documentation
  5. 05Separate blocking errors from flagged guidance

Use cases

  1. 01Check a skill contribution before merging
  2. 02Find missing or malformed SKILL.md metadata
  3. 03Review API-key handling and script quality

Install

Copy the command and run it in a terminal.

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

After installing, type/pr-reviewin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
quality-guidelines.md
structure-rules.md
scripts/
validate_skills.py

Details

From the project README

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)

Comments

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