review-pr

Genera comentarios estructurados de revisión de PR utilizando agentes de Claude Code y publícalos en GitHub. No se requiere clave API: utiliza la autenticación existente de Claude Code.

npx skills add https://github.com/microsoft/agent365-devtools --skill review-pr

PR Review Skill

Generate and post AI-powered PR review comments to GitHub following engineering best practices.

Usage

/review-pr <pr-number>         # Generate review (step 1)
/review-pr <pr-number> --post  # Post review to GitHub (step 2)

Examples:

  • /review-pr 180 - Generate review and save to YAML file
  • /review-pr 180 --post - Post the reviewed YAML to GitHub

What this skill does

Step 1: Generate (/review-pr <number>)

  1. Fetches PR details from GitHub using the gh CLI
  2. Performs architectural review (NEW!): Questions design decisions, checks for scope creep, validates use cases
  3. Analyzes changes for security, testing, design patterns, and code quality issues
  4. Differentiates contexts: CLI code vs GitHub Actions code (different standards)
  5. Creates actionable feedback: Specific refactoring suggestions based on file names and patterns
  6. Generates structured review comments in an editable YAML file
  7. Shows preview of all generated comments

Step 2: Post (/review-pr <number> --post)

  1. Reads the YAML file you reviewed/edited
  2. Posts to GitHub: Submits all enabled comments to the PR
  3. Automatic fallback: If GitHub API posting fails (e.g., Enterprise Managed User restrictions), automatically generates a markdown file with formatted comments for manual copy/paste

Engineering Review Principles

This skill enforces the following principles:

Architectural Review (NEW!)

  • Design Decision Validation: Questions "why" before reviewing "how"
  • Scope Creep Detection: Flags expansions beyond Agent365 deployment/management
  • Use Case Validation: Requires concrete scenarios for new features
  • Overlap Detection: Identifies duplication with existing tools (Azure CLI, Portal)
  • YAGNI Enforcement: Questions features without documented need

Architecture & Patterns

  • .NET architect patterns: Reviews follow .NET best practices
  • Azure CLI alignment: Ensures consistency with az cli patterns and conventions
  • Cross-platform compatibility: Validates Windows, Linux, and macOS compatibility (for CLI code)

Design Patterns

  • KISS (Keep It Simple, Stupid): Prefers simple, straightforward solutions
  • DRY (Don't Repeat Yourself): Identifies code duplication
  • SOLID principles: Especially Single Responsibility Principle
  • YAGNI (You Aren't Gonna Need It): Avoids over-engineering
  • One class per file: Enforces clean code organization

Code Quality

  • No large files: Flags files over 500 additions
  • Function reuse: Encourages reusing functions across commands
  • No special characters: Avoids emojis in logs/output (Windows compatibility)
  • Self-documenting code: Prefers clear code over excessive comments
  • Crisp comments (pr-code-reviewer #30): Flags added comments that run past 1-2 lines, restate the code, or narrate design history — a comment says why in one line; long-form reasoning belongs in the commit/PR.
  • Release-note-ready CHANGELOG (pr-code-reviewer #31): Flags CHANGELOG.md entries that name internals, explain mechanism, or run multiple sentences — each entry is one crisp consumer-facing sentence (it ships verbatim to nuget.org release notes).
  • Minimal changes: Makes only necessary changes to solve the problem

Testing Standards

  • Framework: xUnit, FluentAssertions, NSubstitute for .NET; pytest/unittest for Python
  • Quality over quantity: Focus on critical paths and edge cases
  • CLI reliability: CLI code without tests is BLOCKING
  • GitHub Actions tests: Strongly recommended (HIGH severity) but not blocking
  • Mock external dependencies: Proper mocking patterns

Security

  • No hardcoded secrets: Use environment variables or Azure Key Vault
  • Credential management: Follow az cli patterns for CLI code; use GitHub Secrets for Actions

Context Awareness

The skill differentiates between:

  • CLI code (strict requirements): Cross-platform, reliable, must have tests
  • GitHub Actions code (GitHub-specific): Linux-only is acceptable, tests strongly recommended

Review Comments Output

Generated comments are saved to:

C:\Users\<username>\AppData\Local\Temp\pr-reviews\pr-<number>-review.yaml

You can edit this file to:

  • Disable comments by setting enabled: false
  • Modify comment text
  • Adjust severity levels (blocking, high, medium, low, info)
  • Add or remove comments

Implementation

The skill uses Claude Code directly for semantic code analysis (inspired by Agent365-dotnet). No separate API key required!

Generate mode (default):

  1. Claude Code reads .claude/agents/pr-code-reviewer.md for review process guidelines. Read the working-tree (PR) version of this file and of .github/copilot-instructions.md and CLAUDE.md — not the base-branch copy. When the PR under review adds or changes a review rule (as PR #461 did with rules #30/#31), the new rule must be applied to that same PR in the same run; reading the base copy would skip it.
  2. Claude Code reads .github/copilot-instructions.md for coding standards
  3. Claude Code fetches PR details: gh pr view <number> --json ...
  4. Claude Code analyzes actual code changes: gh pr diff <number>
  5. Claude Code performs semantic analysis using its own capabilities
  6. Claude Code identifies specific issues with line numbers and code references
  7. Claude Code writes YAML file to C:\Users\<username>\AppData\Local\Temp\pr-reviews\pr-<number>-review.yaml

Post mode (with --post flag):

  1. Python script reads the YAML file
  2. Python script posts comments to GitHub using gh pr comment
  3. If posting fails (API permissions), automatically generates markdown file for manual copy/paste

Key Advantages:

  • ✅ No ANTHROPIC_API_KEY required - uses Claude Code's existing authentication
  • ✅ Better semantic analysis - Claude Code has full context and conversation history
  • ✅ Simpler Python script - only handles posting logic (~240 lines vs ~1500 lines)
  • ✅ Easier to maintain and debug

Workflow

  1. Generate review: /review-pr 180

    • Fetches PR details from GitHub
    • Analyzes code and generates review comments
    • Saves to YAML file (shows path in output)
  2. Review and edit: Open the YAML file

    • Review all generated comments
    • Edit comment text if needed
    • Disable comments by setting enabled: false
    • Add your own comments if desired
  3. Post to GitHub: /review-pr 180 --post

    • Reads the YAML file
    • Posts all enabled comments to the PR
    • If API posting fails, automatically generates a markdown file for manual copy/paste

Requirements

  • GitHub CLI (gh) installed and authenticated
  • Python 3.x (only for --post mode)
  • PyYAML library: pip install pyyaml (only for --post mode)
  • Repository must be a GitHub repository
  • GitHub API permissions to post reviews (Enterprise Managed Users may have restrictions)

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