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Agent Skill · 开发

python-package-management(Python package management 测试)

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。

python-package-management 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 16,443。

总安装 499周安装 21GitHub Stars 9,992下载 175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
unknown

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:python-package-management(Python package management 测试)
来源仓库:https://github.com/microsoft/agent-framework
仓库路径:skills/python-package-management
安装命令:
npx skills add https://github.com/microsoft/agent-framework --skill python-package-management
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/microsoft/agent-framework --skill python-package-management

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

协助 Python 项目的依赖管理和包发布流程自动化。适用于多环境部署和持续集成/持续交付场景。提供 requirements.txt 管理和虚拟环境配置的标准化方案。涉及生产环境变更时应先在测试环境验证依赖兼容性。

python-package-management 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/python-package-management

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/microsoft/agent-framework --skill python-packa…;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

    它需要调用外部服务,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

python-package-management 怎么安装?

在终端执行 npx skills add https://github.com/microsoft/agent-framework --skill python-package-managem…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

python-package-management 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。

python-package-management 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

python-package-management 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

python-package-management 用的人多吗?

公开数据是累计安装 499 次,近一周安装 21 次,下载 175 次,GitHub 9,992 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。

python-package-management 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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 9,992 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
python-package-management

Python package management 测试 · 本页

4999,992调用外部服务未明确通过

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

Python Package Management

Monorepo Structure

python/
├── pyproject.toml              # Root package (agent-framework)
├── packages/
│   ├── core/                   # agent-framework-core (main package)
│   ├── foundry/                # agent-framework-foundry
│   ├── anthropic/              # agent-framework-anthropic
│   └── ...                     # Other connector packages
  • agent-framework-core contains core abstractions and OpenAI/Azure OpenAI built-in
  • Provider packages extend core with specific integrations
  • Root agent-framework depends on agent-framework-core[all]

Dependency Management

Uses uv for dependency management and poethepoet for task automation.

# Full setup (venv + install + prek hooks)
uv run poe setup

# Install dependencies from lockfile (frozen resolution with prerelease policy)
uv run poe install

# Create venv with specific Python version
uv run poe venv --python 3.12

# Intentionally upgrade a specific dependency to reduce lockfile conflicts
uv lock --upgrade-package <dependency-name> && uv run poe install

# Refresh all dev dependency pins, lockfile, and validation in one run
uv run poe upgrade-dev-dependencies

# First, run workspace-wide lower/upper compatibility gates
uv run poe validate-dependency-bounds-test
# Defaults to --package "*"; pass a package to scope test mode
uv run poe validate-dependency-bounds-test --package core

# Then expand bounds for one dependency in the target package
uv run poe validate-dependency-bounds-project --mode both --package core --dependency "<dependency-name>"

# Repo-wide automation can reuse the same task
uv run poe validate-dependency-bounds-project --mode upper --package "*"

# Add a dependency to one project and run both validators for that project/dependency
uv run poe add-dependency-and-validate-bounds --package core --dependency "<dependency-spec>"

Dependency Bound Notes

  • Stable dependencies (>=1.0) should typically be bounded as >=<known-good>,<next-major>.
  • Prerelease (dev/a/b/rc) and <1.0 dependencies should use hard bounds with an explicit upper cap (avoid open-ended ranges).
  • For <1.0 dependencies, prefer the broadest validated range the package can really support. That may be a patch line, a minor line, or multiple minor lines when checks/tests show the broader lane is compatible.
  • Prefer supporting multiple majors when practical; if APIs diverge across supported majors, use version-conditional imports/paths.
  • For dependency changes, run workspace-wide bound gates first, then validate-dependency-bounds-project --mode both for the target package/dependency to keep minimum and maximum constraints current. The same task can also drive repo-wide upper-bound automation by using --package "*" and omitting --dependency.
  • Prefer targeted lock updates with uv lock --upgrade-package <dependency-name> to reduce uv.lock merge conflicts.
  • Use add-dependency-and-validate-bounds for package-scoped dependency additions plus bound validation in one command.
  • Use upgrade-dev-dependencies for repo-wide dev tooling refreshes; it repins dev dependencies, refreshes uv.lock, and reruns check, typing, and test.

Lazy Loading Pattern

Provider folders in core use __getattr__ to lazy load from connector packages:

# In agent_framework/foundry/__init__.py
_IMPORTS: dict[str, tuple[str, str]] = {
    "FoundryChatClient": ("agent_framework_foundry", "agent-framework-foundry"),
}

def __getattr__(name: str) -> Any:
    if name in _IMPORTS:
        import_path, package_name = _IMPORTS[name]
        try:
            return getattr(importlib.import_module(import_path), name)
        except ModuleNotFoundError as exc:
            raise ModuleNotFoundError(
                f"The package {package_name} is required to use `{name}`. "
                f"Install it with: pip install {package_name}"
            ) from exc

Adding a New Connector Package

Important: Do not create a new package unless approved by the core team.

Every new package starts as alpha.

Alpha package checklist

  1. Create directory under packages/ (e.g., packages/my-connector/)
  2. Add the package to tool.uv.sources in root pyproject.toml
  3. Set the package version to the alpha pattern: 1.0.0a<date>
  4. Set the package classifier to Development Status:: 3 - Alpha
  5. Include samples inside the package (e.g., packages/my-connector/samples/)
  6. Do NOT add to [all] extra in packages/core/pyproject.toml
  7. Do NOT create lazy loading in core yet
  8. Add the package to python/PACKAGE_STATUS.md and keep that file updated when packages are added, removed, renamed, or promoted. If the package exposes individually staged APIs, keep the feature list there current too.

Recommended dependency workflow during connector implementation:

  1. Add the dependency to the target package: uv run poe add-dependency-to-project --package core --dependency "<dependency-spec>"
  2. Implement connector code and tests.
  3. Validate dependency bounds for that package/dependency: uv run poe validate-dependency-bounds-project --mode both --package core --dependency "<dependency-name>"
  4. If the package has meaningful tests/checks that validate dependency compatibility, you can use the add + validation flow in one command: uv run poe add-dependency-and-validate-bounds --package core --dependency "<dependency-spec>" If compatibility checks are not in place yet, add the dependency first, then implement tests before running bound validation.

Promotion path

Promotion work is not isolated to the package being promoted. If a promotion changes dependency metadata for downstream packages, also update the dependent packages' own versions so they publish new metadata alongside the promoted dependency bounds. Apply the internal package dependency update rules from the versioning section below during promotions as well as standalone version update work.

Alpha -> Beta

Move a package to beta when it is stable enough to be part of the main install surface.

  1. Update the package version to the beta pattern: 1.0.0b<date>
  2. Update the classifier to Development Status:: 4 - Beta
  3. Add the package to [all] in packages/core/pyproject.toml
  4. Move samples to the root samples/ tree and remove package-local samples
  5. Create or update the relevant lazy-loading namespace in core when the package belongs under one
  6. Update python/PACKAGE_STATUS.md

After alpha, there should be no samples left inside a package folder.

Beta -> RC

Move a package to rc when its API is close to the final released shape.

  1. Update the package version to the release-candidate pattern: 1.0.0rc<number>
  2. Keep the classifier at Development Status:: 4 - Beta because PyPI does not have a separate release-candidate classifier
  3. Keep the package in core[all]
  4. Keep samples only in the root samples/ tree
  5. Update python/PACKAGE_STATUS.md to show the package as rc
RC -> Released

Move a package to released when it no longer carries a prerelease qualifier.

  1. Update the package version to the stable pattern: 1.0.0
  2. Update the classifier to Development Status:: 5 - Production/Stable
  3. Keep the package in core[all]
  4. Keep samples only in the root samples/ tree
  5. Update python/PACKAGE_STATUS.md to show the package as released
  6. Update all README.md files that install that package with pip install agent-framework-... --pre so they use pip install agent-framework-... without the --pre suffix

Versioning

Internal package dependency updates

  • If package A depends on package B within this repository, only update package A's dependency declaration when the work on package B actually affects package A.
  • If package A does not need anything from the package B change, leave package A's dependency declaration unchanged.
  • If package A does need something from the package B change, update package A's dependency declaration to the version or versioning scheme that matches what package A now requires.
  • If package B is promoted to a different lifecycle stage, update package A's dependency declaration to the new versioning scheme for package B even when the only change is the stage transition itself.
  • Use this guidance both for ordinary version updates and for package promotion work.
  • All non-core packages declare a lower bound on agent-framework-core
  • When core version bumps with breaking changes, update the lower bound in all packages
  • Non-core packages version independently; only raise core bound when using new core APIs
  • If promoting a package changes a dependent package's published dependency metadata, bump the dependent package's own version in the correct lifecycle pattern for its current stage
  • Lifecycle version patterns:

- alpha: 1.0.0a<date> - beta: 1.0.0b<date> - rc: 1.0.0rc<number> - released: 1.0.0

  • Keep the Development Status classifier in pyproject.toml aligned with the lifecycle stage:

- alpha -> Development Status:: 3 - Alpha - beta -> Development Status:: 4 - Beta - rc -> Development Status:: 4 - Beta - released -> Development Status:: 5 - Production/Stable

  • See the PyPI classifier list for the available classifier values: https://pypi.org/classifiers/

Installation Options

pip install agent-framework-core          # Core only
pip install agent-framework-core[all]     # Core + all connectors
pip install agent-framework               # Same as core[all]
pip install agent-framework-foundry       # Specific connector (pulls in core)

Maintaining Documentation

When changing a package, check if its AGENTS.md needs updates:

  • Adding/removing/renaming public classes or functions
  • Changing the package's purpose or architecture
  • Modifying import paths or usage patterns

Keep python/PACKAGE_STATUS.md updated when:

  • A package is added, removed, renamed, or promoted between lifecycle stages
  • A package starts or stops exposing individually staged experimental or release-candidate APIs

When a package adds, removes, or renames environment variables, update the related documentation in the same change:

  • The package's README.md for package-level configuration/env var guidance
  • samples/README.md if the package is included in packages/core/pyproject.toml [all] and the env var is part of the consolidated package env-var inventory
  • Any affected sample/package-local .env.example, .env.template, or sample README files when sample setup changes alongside the package

平台分布

Codex

40.09%
按下载量换算70

Claude

28.78%
按下载量换算50

Cursor

19.38%
按下载量换算34

Gemini CLI

9.71%
按下载量换算17

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。目前只有一个公开来源,建议同时核对源仓库。

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