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Agent Skill · 运维和基础设施

github-pr-review(GitHub PR 审查)

用于围绕 GitHub 仓库、Issue、Pull Request、分支、提交和代码协作流程提供辅助能力。它适合让 Agent 查询项目状态、整理变更、辅助创建或检查协作事项,并把仓库中的信息转成可执行的下一步。使用时需要区分只读查询和写入操作;涉及创建 PR、修改 Issue、推送分支或访问私有仓库时,应确认 token 权限、目标仓库范围和用户授权。

github-pr-review 是运维和基础设施类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证为 MIT,第三方扫描有提醒项,按安装量在运维和基础设施类的 2,449 个 Skill 里排第 208。

总安装 9,813周安装 413GitHub Stars 62下载 3,436核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:github-pr-review(GitHub PR 审查)
来源仓库:https://github.com/fvadicamo/dev-agent-skills
仓库路径:skills/github-pr-review
安装命令:
npx skills add https://github.com/fvadicamo/dev-agent-skills --skill github-pr-review
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/fvadicamo/dev-agent-skills --skill github-pr-review

这个 Skill 是做什么的

运维和基础设施类 Skill 处理部署、监控、日志和云资源,多数需要较高的执行权限。

通过基于严重性的优先级、修复应用程序和线程回复来解决 PR 审查意见。

从 GitHub 获取内联评论和评论正文,按严重性分类(CRITICAL > HIGH > MEDIUM > LOW),并在处理前显示结构化摘要表 解析 CodeRabbit 审查部分(外部差异、重复、挑剔)并使用嵌入式“AI 代理提示”上下文来了解问题和建议的修复 在用户确认后应用修复,提交功能上独立的更改并批量修复,然后使用标准模板回复线程 通过验证所有引用的位置是否已修复(包括“也适用于”范围)来处理多问题评论,并在推送之前运行测试 提交正式的 PR 审核状态并提醒完成时缺少的里程碑

来源仓库内路径:skills/github-pr-review

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/fvadicamo/dev-agent-skills --skill github-pr-r…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

github-pr-review 怎么安装?

在终端执行 npx skills add https://github.com/fvadicamo/dev-agent-skills --skill github-pr-review。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

github-pr-review 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

github-pr-review 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/fvadicamo/dev-agent-skills --skill github-pr-review;npx skills add fvadicamo/dev-agent-skills --skill "github-pr-review" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

github-pr-review 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

github-pr-review 用的人多吗?

公开数据是累计安装 9,813 次,近一周安装 413 次,下载 3,436 次,GitHub 62 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

github-pr-review 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理运维和基础设施类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

运维和基础设施类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
brainstorming

头脑风暴 · Codex、Claude

3,148,776173,131操作浏览器MIT通过
copywriting

文案写作 · Codex、Claude

1,996,72425,583需要联网MIT通过
openclaw-secure-linux-cloud

OpenClaw secure linux cloud 命令行 · Codex、Claude

1,853,08840接触敏感数据未明确未通过
xget

xget 命令行 · Codex、Claude

1,799,28040执行命令未明确未通过
requesting-code-review

代码审查请求 · Codex、Claude

1,590,144173,103权限未标注MIT通过
github-pr-review

GitHub PR 审查 · 本页

9,81362执行命令MIT有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

GitHub PR review

Resolves Pull Request review comments with severity-based prioritization, fix application, and thread replies.

Current PR

!gh pr view --json number,title,state,milestone -q '"PR #\(.number): \(.title) (\(.state)) | Milestone: \(.milestone.title // "none")"' 2>/dev/null

Core workflow

1. Fetch, filter, and classify comments

REPO=$(gh repo view --json nameWithOwner -q '.nameWithOwner')
PR=$(gh pr view --json number -q '.number')
LAST_PUSH=$(git log -1 --format=%cI HEAD)

# Inline review comments - filter out replies (keep only originals)
gh api repos/$REPO/pulls/$PR/comments?per_page=100 --jq '
  [.[] | select(.in_reply_to_id == null) |
   {id, path, user: .user.login, created_at, body: .body[0:200]}]
'

# PR-level reviews with non-empty body (CodeRabbit sections, Gemini, etc.)
gh api repos/$REPO/pulls/$PR/reviews?per_page=100 --jq '
  [.[] | select(.body | length > 0) |
   {id, user: .user.login, state, submitted_at, body: .body[0:500]}]
'

Cross-check review-attached comments: CodeRabbit's review body states "Actionable comments posted: N". If the general pulls/$PR/comments endpoint returns fewer than N new originals from that reviewer, some comments are only available via the review-specific endpoint. Fetch them and merge by comment ID:

# $REVIEW_ID from the reviews fetch above; $EXPECTED from parsing "Actionable comments posted: N"
gh api repos/$REPO/pulls/$PR/reviews/$REVIEW_ID/comments?per_page=100 --jq '
  [.[] | select(.in_reply_to_id == null) |
   {id, path, user: .user.login, created_at, body: .body[0:200]}]
'

Deduplicate by id before continuing. Comments found only via the review-specific endpoint are valid inline comments and should be treated identically (same classification, same in_reply_to reply mechanism).

Filter new vs already-seen: compare created_at/submitted_at with $LAST_PUSH. Comments posted after the last push are new. Mark older comments as "previous round" in the summary table.

Parse CodeRabbit review bodies: the initial fetch truncates bodies for classification. For reviews from CodeRabbit (user.login starts with coderabbitai), fetch the full body separately:

gh api repos/$REPO/pulls/$PR/reviews?per_page=100 --jq '
  [.[] | select(.user.login | startswith("coderabbitai")) |
   {id, submitted_at, body}]
'

CodeRabbit posts structured <details> blocks containing outside-diff, duplicate, and nitpick comments. Each block includes file path, line range, severity, and optionally a "Prompt for AI Agents" with pre-built context. See references/coderabbit_parsing.md for full parsing guide.

Use CodeRabbit AI prompts when available: if a comment (or the review body) contains a "Prompt for AI Agents" <details> block, use it to understand the issue and suggested approach. Always read the actual code before proposing a fix. If the review body contains a "Prompt for all review comments with AI agents" block, read it first for cross-comment context before processing individual comments.

Classify all comments by severity and process in order: CRITICAL > HIGH > MEDIUM > LOW.

SeverityIndicatorsAction
CRITICALcritical.svg, _🔒 Security_, _🚨 Critical_, _🔴 Critical_, "security", "vulnerability"Must fix
HIGHhigh-priority.svg, _⚠️ Potential issue_, _🐛 Bug_, _⚡ Performance_, _🟠 Major_, "High Severity"Should fix
MEDIUMmedium-priority.svg, _🛠️ Refactor suggestion_, _💡 Suggestion_, "Medium Severity"Recommended
LOWlow-priority.svg, _🧹 Nitpick_, _🔧 Optional_, _🟡 Minor_, _🔵 Trivial_, _⚪ Info_, "style", "nit"Optional

When a comment has both a type label and a secondary color badge (e.g., _💡 Suggestion_ | _🟠 Major_), the color badge is the binding severity and overrides the type-based default.

See references/severity_guide.md for full detection patterns (Gemini badges, CodeRabbit emoji, Cursor comments, keyword fallback, related comments heuristics).

2. Show review summary table

Before processing, display a structured overview of all comments:

| # | ID         | Severity | File:Line          | Type     | Status   | Summary            |
|---|------------|----------|--------------------|----------|----------|--------------------|
| 1 | 123456789  | CRITICAL | src/auth.py:45     | inline   | new      | SQL injection risk |
| 2 | 987654321  | HIGH     | src/db.py:346-350  | outside  | new      | Missing join cond  |
| 3 | 555555555  | HIGH     | src/chunk.py:188   | duplicate| previous | Stale metadata     |
| 4 | 444444444  | LOW      | tests/test_q.py:12 | nitpick  | previous | Naming convention  |
  • Type: inline, outside (outside diff), duplicate, minor, nitpick (from CodeRabbit sections), or review (generic PR-level)
  • Status: new (posted after last push) or previous (from earlier rounds)
  • Group related comments (same file, same root cause, "also applies to" ranges) and note clusters
  • Deduplicate: if the same issue appears both as an inline comment and in a CodeRabbit review body section (e.g., duplicate), keep one entry and note both sources

If there are more than 10 comments, suggest saving a review summary to Claude's memory for tracking across sessions. The summary should include: PR number, comment IDs, severity, status (new/addressed/deferred/won't fix), and brief description. This helps maintain continuity when new comments arrive after subsequent pushes.

3. Process each comment

For each comment, in severity order:

  1. Show context: comment ID, severity, file:line, quote
  2. Check for AI prompt: if CodeRabbit "Prompt for AI Agents" is available for this comment, use it to understand the issue and suggested approach
  3. Check for proposed fix: if CodeRabbit includes a "Proposed fix" or "Suggested fix" code block, use it as a starting point (but verify correctness)
  4. Read affected code and propose fix (always read the actual code, even when an AI prompt or proposed fix provides context)
  5. Handle "also applies to": if the comment references additional line ranges, include all locations in the fix
  6. Confirm with user before applying
  7. Apply fix if approved
  8. Verify ALL issues in the comment are addressed (multi-issue comments are common)

4. Commit changes

Use git-commit skill format. Functional fixes get separate commits, cosmetic fixes are batched:

Change typeStrategy
Functional (CRITICAL/HIGH)Separate commit per fix
Cosmetic (MEDIUM/LOW)Single batch style: commit

Reference the comment ID in the commit body.

5. Reply to threads

Inline comments

Important: use --input - with JSON. The -f in_reply_to=... syntax does NOT work.

COMMIT=$(git rev-parse --short HEAD)
gh api repos/$REPO/pulls/$PR/comments \
  --input - <<< '{"body": "Fixed in '"$COMMIT"'. Brief explanation.", "in_reply_to": 123456789}'
Non-inline comments (CodeRabbit review body)

Comments embedded in the review body (outside diff, duplicate, nitpick) do not have inline threads. The GitHub API does not support replying to a review body directly. Post a general PR comment referencing the specific issue:

gh pr comment $PR --body "Fixed in $COMMIT. Addresses outside-diff comment on file/path.py:346-350."

Reply templates (no emojis, minimal and professional):

SituationTemplate
FixedFixed in [hash]. [brief description of fix]
Won't fixWon't fix: [reason]
By designBy design: [explanation]
DeferredDeferred to [issue/task]. Will address in future iteration.
AcknowledgedAcknowledged. [brief note]

6. Run tests and push

Run the project test suite. All tests must pass before pushing. Push all fixes together to minimize review loops.

7. Submit review (optional)

After addressing all comments, formally submit a review:

  • gh pr review $PR --approve --body "..." - all comments addressed, PR is ready
  • gh pr review $PR --request-changes --body "..." - critical issues remain
  • gh pr review $PR --comment --body "..." - progress update, no decision yet

8. Verify milestone

gh pr view $PR --json milestone -q '.milestone.title // "none"'

If the PR has no milestone, check for open milestones:

REPO=$(gh repo view --json nameWithOwner -q '.nameWithOwner')
gh api repos/$REPO/milestones --jq '[.[] | select(.state=="open")] | .[] | "\(.number): \(.title)"'

If open milestones exist, inform the user and suggest assigning:

gh pr edit $PR --milestone "[milestone-title]"

Do not assign automatically. This is a reminder only.

Avoiding review loops

When bots (Gemini, Codex, etc.) review every push:

  1. Batch fixes: accumulate all fixes, push once
  2. Draft PR: convert to draft during fixes
  3. Commit keywords: some bots respect [skip ci] or [skip review]

Important rules

  • ALWAYS fetch both inline comments (pulls/$PR/comments) and review bodies (pulls/$PR/reviews)
  • ALWAYS cross-check "Actionable comments posted: N" against found originals; fetch pulls/$PR/reviews/$REVIEW_ID/comments when count mismatches
  • ALWAYS parse CodeRabbit review bodies for all section types (outside diff, duplicate, minor, nitpick)
  • ALWAYS use CodeRabbit "Prompt for AI Agents" as primary context when available
  • ALWAYS show the review summary table before processing
  • ALWAYS confirm before modifying files
  • ALWAYS verify ALL issues in multi-issue comments are fixed, including "also applies to" ranges
  • ALWAYS run tests before pushing
  • ALWAYS reply to resolved threads using standard templates
  • ALWAYS submit formal review (gh pr review) after addressing all comments
  • ALWAYS check milestone at the end and remind if missing
  • ALWAYS suggest saving a review summary to memory when there are more than 10 comments
  • NEVER use emojis in commit messages or thread replies
  • NEVER skip HIGH/CRITICAL comments without explicit user approval
  • NEVER assign milestone automatically - suggest only
  • Functional fixes -> separate commits (one per fix)
  • Cosmetic fixes -> batch into single style: commit
  • Duplicate comments -> treat as higher priority than their label (issue was already flagged before)
  • Related comments -> group and fix together when they share root cause or file context

References

  • references/severity_guide.md - Severity detection patterns (Gemini badges, CodeRabbit emoji, Cursor comments, keyword fallback, related comments heuristics)
  • references/coderabbit_parsing.md - CodeRabbit review body structure, section parsing, "Prompt for AI Agents" usage, duplicate and "also applies to" handling

平台分布

Claude Code

29.59%
按下载量换算1,017

OpenCode

24.2%
按下载量换算832

Gemini CLI

15.19%
按下载量换算522

Antigravity

11.98%
按下载量换算412

Codex

8.09%
按下载量换算278

trae

3.14%
按下载量换算108

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/fvadicamo/dev-agent-skills --skill github-pr-review;npx skills add fvadicamo/dev-agent-skills --skill "github-pr-review" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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