review-pr
Skill61 starsChanged 5 months agoMITView on GitHub
Generate structured PR review comments using Claude Code agents and post them to GitHub. No API key required - uses Claude Code's existing authentication.
- Reads credentials
- Installs packages
185 other projects use a version of this.
Adopted by
Most recently taken up by
- data-privacy-stack/presidiotheir own version, first seen Nov 9, 2026
- pgmpy/pgmpytheir own version, first seen Nov 9, 2026
- enes-alatas/spooltheir own version, first seen Nov 8, 2026
- microsoft/presidiotheir own version, first seen Nov 8, 2026
- domengabrovsek/agent-configtheir own version, first seen Nov 8, 2026
- domengabrovsek/claudetheir own version, first seen Nov 8, 2026
Used with
Projects with it also use diagnose-ci-failures, docs-loop and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
zerx-labSkilldiagnose-ci-failures
Diagnose CI failures for a PR using the GitHub CLI, extract error logs, and generate a plan to fix them. Use when the user asks to check CI status, pull CI issues, triage test failures, or investigate PR build failures.
Also in 3 projects that use this
GeiserXSkilldocs-loop
Use when the user asks to audit docs, check documentation for stale claims, or verify docs against the code.
Also in 3 projects that use this
GeiserXSkillfull-loop
Drive a repository to done with sergio-loop, then keep improving it with refine-loop at a descending theta. Use only when the user explicitly invokes full-loop.
Also in 3 projects that use this
n24q02mSkillreview-delta
Review only changes since last commit using impact analysis. Token-efficient delta review with automatic blast-radius detection.
Also in 3 projects that use this
n24q02mSkillbuild-graph
Build or update the code review knowledge graph. Run this first to initialize, or let hooks keep it updated automatically.
Also in 3 projects that use this
- +7 more from GeiserX
Other versions185 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 185
- denoland/deno★ 109ksame name
- ToolJet/ToolJet★ 41ksame name
- remix-run/remix★ 33ksame name
- tirth8205/code-review-graph★ 33ksame name
- alpinejs/alpine★ 32ksame name
- nrwl/nx★ 29ksame name
- PrefectHQ/fastmcp★ 28ksame name
- jlowin/fastmcp★ 28ksame name
- livewire/livewire★ 24ksame name
- microsoft/fluentui★ 20ksame name
175 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository3 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- review-stagedSkill
The file
We split this skill into the rules it gives an agent
7 rules. 0 are common, 7 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 178 edited versions we read keep these.
7
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 36
Automatic fallback: If GitHub API posting fails (e.g., Enterprise Managed User restrictions), automatically generates a markdown file with …
- Distinctive: line 78
No hardcoded secrets: Use environment variables or Azure Key Vault
- Distinctive: line 79
Credential management: Follow az cli patterns for CLI code.
- Distinctive: line 97
Add or remove comments
- Distinctive: line 115
If posting fails (API permissions), automatically generates markdown file for manual copy/paste
- Distinctive: line 134
Add your own comments if desired
- Distinctive: line 139
If API posting fails, automatically generates a markdown file for manual copy/paste
“Common” is measured: kept by at least 6 in 10 of the 178 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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>)
- Fetches PR details from GitHub using the gh CLI
- Performs architectural review (NEW!): Questions design decisions, checks for scope creep, validates use cases
- Analyzes changes for security, testing, design patterns, and code quality issues
- Differentiates contexts: CLI code vs GitHub Actions code (different standards)
- Creates actionable feedback: Specific refactoring suggestions based on file names and patterns
- Generates structured review comments in an editable YAML file
- Shows preview of all generated comments
Step 2: Post (/review-pr <number> --post)
- Reads the YAML file you reviewed/edited
- Posts to GitHub: Submits all enabled comments to the PR
- 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.mdentries 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.yamlYou 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):
- Claude Code reads
.claude/agents/pr-code-reviewer.mdfor review process guidelines. Read the working-tree (PR) version of this file and of.github/copilot-instructions.mdandCLAUDE.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. - Claude Code reads
.github/copilot-instructions.mdfor coding standards - Claude Code fetches PR details:
gh pr view <number> --json ... - Claude Code analyzes actual code changes:
gh pr diff <number> - Claude Code performs semantic analysis using its own capabilities
- Claude Code identifies specific issues with line numbers and code references
- Claude Code writes YAML file to
C:\Users\<username>\AppData\Local\Temp\pr-reviews\pr-<number>-review.yaml
Post mode (with --post flag):
- Python script reads the YAML file
- Python script posts comments to GitHub using
gh pr comment - If posting fails (API permissions), automatically generates markdown file for manual copy/paste
Key Advantages:
- ✅ No
ANTHROPIC_API_KEYrequired - 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
- Generate review:
/review-pr 180- Fetches PR details from GitHub
- Analyzes code and generates review comments
- Saves to YAML file (shows path in output)
- 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
- 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)
See Also
- README.md - Detailed documentation
- review-pr.py - Implementation script
Does it work?
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
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