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Agent Skill · 研究检索

pr-draft-summary(公关草案摘要)

用于辅助 Python 项目开发、测试、依赖管理和常见框架工作流。它适合让 Agent 阅读 Python 代码、定位测试问题、整理运行命令、生成脚本或分析数据处理逻辑。使用时需要确认项目虚拟环境、依赖版本和测试入口;涉及执行脚本、读写文件、访问数据库或调用外部 API 时,应先明确运行目录和输入输出范围,避免误改生产数据。

pr-draft-summary 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 26,247。

总安装 3,608周安装 155GitHub Stars 25,582下载 1,265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:pr-draft-summary(公关草案摘要)
来源仓库:https://github.com/openai/openai-agents-python
仓库路径:skills/pr-draft-summary
安装命令:
npx skills add https://github.com/openai/openai-agents-python --skill pr-draft-summary
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/openai/openai-agents-python --skill pr-draft-summary

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于辅助 Python 项目开发、测试、依赖管理和常见框架工作流。适合阅读 Python 代码、定位测试问题、整理运行命令或生成脚本。使用时需确认项目虚拟环境、依赖版本和测试入口。涉及执行脚本、读写文件或调用外部 API 时,应先明确运行目录和输入输出范围。

pr-draft-summary 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/pr-draft-summary

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/openai/openai-agents-python --skill pr-draft-s…;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

    它需要需要联网,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

pr-draft-summary 怎么安装?

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

pr-draft-summary 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。

pr-draft-summary 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

pr-draft-summary 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。

pr-draft-summary 用的人多吗?

公开数据是累计安装 3,608 次,近一周安装 155 次,下载 1,265 次,GitHub 25,582 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。

pr-draft-summary 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
pr-draft-summary

公关草案摘要 · 本页

3,60825,582需要联网MIT通过

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

PR Draft Summary

Purpose

Produce the PR-ready summary required in this repository after substantive code work is complete: a concise summary plus a PR-ready title and draft description that begins with "This pull request...". The block should be ready to paste into a PR for openai-agents-python.

When to Trigger

  • The task for this repo is finished (or ready for review) and it touched runtime code, tests, examples, docs with behavior impact, or build/test configuration.
  • Treat this as the default final handoff step for substantive code work. Run it after any required verification or changeset work and before sending the "work complete" response.
  • Skip only for trivial or conversation-only tasks, repo-meta/doc-only tasks without behavior impact, or when the user explicitly says not to include the PR draft block.

Inputs to Collect Automatically (do not ask the user)

  • Current branch: git rev-parse --abbrev-ref HEAD.
  • Working tree: git status -sb.
  • Untracked files: git ls-files --others --exclude-standard (use with git status -sb to ensure they are surfaced; --stat does not include them).
  • Changed files: git diff --name-only (unstaged) and git diff --name-only --cached (staged); sizes via git diff --stat and git diff --stat --cached.
  • Latest release tag (prefer remote-aware lookup): LATEST_RELEASE_TAG=$(.agents/skills/final-release-review/scripts/find_latest_release_tag.sh origin 'v*' 2>/dev/null || git tag -l 'v*' --sort=-v:refname | head -n1).
  • Base reference (use the branch's upstream, fallback to origin/main):

- BASE_REF=$(git rev-parse --abbrev-ref --symbolic-full-name @{upstream} 2>/dev/null || echo origin/main). - BASE_COMMIT=$(git merge-base --fork-point "$BASE_REF" HEAD || git merge-base "$BASE_REF" HEAD || echo "$BASE_REF").

  • Commits ahead of the base fork point: git log --oneline --no-merges ${BASE_COMMIT}..HEAD.
  • Category signals for this repo: runtime (src/agents/), tests (tests/), examples (examples/), docs (docs/, mkdocs.yml), build/test config (pyproject.toml, uv.lock, Makefile, .github/).

Workflow

  1. Run the commands above without asking the user; compute BASE_REF/BASE_COMMIT first so later commands reuse them.
  2. If there are no staged/unstaged/untracked changes and no commits ahead of ${BASE_COMMIT}, reply briefly that no code changes were detected and skip emitting the PR block.
  3. Infer change type from the touched paths listed under "Category signals"; classify as feature, fix, refactor, or docs-with-impact, and flag backward-compatibility risk only when the diff changes released public APIs, external config, persisted data, serialized state, or wire protocols. Judge that risk against LATEST_RELEASE_TAG, not unreleased branch-only churn.
  4. Summarize changes in 1–3 short sentences using the key paths (top 5) and git diff --stat output; explicitly call out untracked files from git status -sb/git ls-files --others --exclude-standard because --stat does not include them. If the working tree is clean but there are commits ahead of ${BASE_COMMIT}, summarize using those commit messages.
  5. Choose the lead verb for the description: feature → adds, bug fix → fixes, refactor/perf → improves or updates, docs-only → updates.
  6. Suggest a branch name. If already off main, keep it; otherwise propose feat/<slug>, fix/<slug>, or docs/<slug> based on the primary area (e.g., docs/pr-draft-summary-guidance).
  7. If the current branch matches issue-<number> (digits only), keep that branch suggestion. Optionally pull light issue context (for example via the GitHub API) when available, but do not block or retry if it is not. When an issue number is present, reference https://github.com/openai/openai-agents-python/issues/<number> and include an auto-closing line such as This pull request resolves #<number>..
  8. Draft the PR title and description using the template below.
  9. Output only the block in "Output Format". Keep any surrounding status note minimal and in English.

Output Format

When closing out a task, add this concise Markdown block (English only) after any brief status note unless the task falls under the documented skip cases or the user says they do not want it.

# Pull Request Draft

## Branch name suggestion

git checkout -b <kebab-case suggestion, e.g., feat/pr-draft-summary-skill>

## Title

<single-line imperative title, which can be a commit message; if a common prefix like chore: and feat: etc., having them is preferred>

## Description

<include what you changed plus a draft pull request title and description for your local changes; start the description with prose such as "This pull request resolves/updates/adds ..." using a verb that matches the change (you can use bullets later), explain the change background (for bugs, clearly describe the bug, symptoms, or repro; for features, what is needed and why), any behavior changes or considerations to be aware of, and you do not need to mention tests you ran.>

Keep it tight—no redundant prose around the block, and avoid repeating details between Changes and the description. Tests do not need to be listed unless specifically requested.

平台分布

Claude Code

29.28%
按下载量换算370

OpenCode

22.48%
按下载量换算284

github-copilot

19.47%
按下载量换算246

Antigravity

13.28%
按下载量换算168

Gemini CLI

9.16%
按下载量换算116

Codex

3.98%
按下载量换算50

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

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