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review-pr

Generate structured PR review comments using Claude Code agents and post them to GitHub. No API key required - uses Claude Code's existing authentication.

DeepseekModel 官方收录技能 质量 优秀 · 78 v1.0.0

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https://deepseekmodel.com/api/download.php?id=microsoft-agent365-devtools-claude-skills-review-pr-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name review-pr description Generate structured PR review comments using Claude Code agents and post them to GitHub. No API key required - uses Claude Code's existing authentication. allowed-tools Bash(gh:*), Task, Read, Write 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.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): 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. Claude Code reads .github/copilot-instructions.md for 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_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 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
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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