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Agent Skill · 开发

poetry-rye-dependency-management(诗歌黑麦依赖管理)

poetry-rye-dependency-management 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

poetry-rye-dependency-management 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有未通过项,按安装量在开发类的 21,746 个 Skill 里排第 14,075。

总安装 759周安装 31GitHub Stars 25下载 243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:poetry-rye-dependency-management(诗歌黑麦依赖管理)
来源仓库:https://github.com/oimiragieo/agent-studio
仓库路径:skills/poetry-rye-dependency-management
安装命令:
npx skills add https://github.com/oimiragieo/agent-studio --skill poetry-rye-dependency-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/oimiragieo/agent-studio --skill poetry-rye-dependency-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。

用于处理 GitHub 仓库、Issue 和 Pull Request 信息。适合围绕代码变更和协作事项进行整理。可结合来源仓库和原始 README 核验具体用法。安装前建议确认权限范围和维护状态。poetry-rye-dependency-management 属于开发类 Skill,可作为该场景下的辅助能力补充。

注意是否会触发命令执行或文件读写操作。

来源仓库内路径:skills/poetry-rye-dependency-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/oimiragieo/agent-studio --skill poetry-rye-dep…;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

poetry-rye-dependency-management 怎么安装?

在终端执行 npx skills add https://github.com/oimiragieo/agent-studio --skill poetry-rye-dependency-ma…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

poetry-rye-dependency-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。

poetry-rye-dependency-management 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。

poetry-rye-dependency-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。

poetry-rye-dependency-management 用的人多吗?

公开数据是累计安装 759 次,近一周安装 31 次,下载 243 次,GitHub 25 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。

poetry-rye-dependency-management 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。

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
poetry-rye-dependency-management

诗歌黑麦依赖管理 · 本页

75925需要联网未明确未通过

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

Poetry / Rye Dependency Management Skill

Overview

Poetry and Rye are Python dependency managers that enforce lockfile-driven, deterministic builds. Both use pyproject.toml as the single configuration file. Poetry is the established standard (since 2018); Rye is a newer Astral tool that bridges to uv. For greenfield projects, consider modern-python skill (uv-native). This skill covers Poetry/Rye for existing codebases and teams already invested in these tools.

When to Use

  • When maintaining existing Poetry or Rye projects
  • When a team has standardized on Poetry and migration to uv is not planned
  • When publishing Python packages to PyPI (Poetry has mature publishing support)
  • When managing monorepo Python workspaces
  • When auditing or upgrading dependency lockfiles

Iron Laws

  1. ALWAYS commit the lockfile (poetry.lock or requirements.lock) -- without it, builds are non-deterministic and CI/CD will resolve different versions than development.
  2. NEVER use pip install in a Poetry/Rye-managed project -- it bypasses the resolver and creates ghost dependencies invisible to the lockfile.
  3. ALWAYS use poetry add/rye add to add dependencies -- manual pyproject.toml edits without re-locking create stale lockfiles.
  4. NEVER pin transitive dependencies manually -- let the solver manage the full dependency graph; pinning transitive deps causes resolver conflicts.
  5. ALWAYS separate runtime and development dependencies into groups -- shipping dev/test dependencies in production images wastes space and expands attack surface.

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Editing pyproject.toml deps without running poetry lockLockfile becomes stale; CI installs different versions than intendedAlways run poetry lock or rye lock after any dependency change
Using poetry install without --no-root in CIInstalls the project in editable mode unnecessarily; slower CI buildsUse poetry install --no-root for library deps only, --only main for production
Committing .venv/ directory to version controlBloats repo; virtualenvs are platform-specific and non-portableAdd .venv/ to .gitignore; recreate with poetry install or rye sync
Mixing pip and Poetry in the same projectCreates two dependency graphs; pip-installed packages invisible to PoetryUse only poetry add/rye add for all dependency changes
Using * version constraints for all dependenciesNo upper bound protection; major version bumps break silentlyUse compatible release (^ in Poetry) or upper-bounded ranges

Workflow

Poetry Project Setup

# Initialize new project
poetry init --name my-project --python ">=3.12"

# Add dependencies by group
poetry add requests httpx
poetry add --group dev ruff pytest pytest-cov
poetry add --group docs sphinx

# Install all groups
poetry install

# Install production only
poetry install --only main

Rye Project Setup

# Initialize new project
rye init my-project
cd my-project

# Add dependencies
rye add requests httpx
rye add --dev ruff pytest pytest-cov

# Sync (install) dependencies
rye sync

pyproject.toml Configuration (Poetry)

[tool.poetry]
name = "my-project"
version = "0.1.0"
description = "Project description"
authors = ["Team <team@example.com>"]
readme = "README.md"

[tool.poetry.dependencies]
python = ">=3.12"
requests = "^2.31"
httpx = "^0.27"

[tool.poetry.group.dev.dependencies]
ruff = "^0.9"
pytest = "^8.0"
pytest-cov = "^6.0"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

CI/CD Integration (GitHub Actions)

name: CI
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.12'
      - name: Install Poetry
        run: pipx install poetry
      - name: Cache dependencies
        uses: actions/cache@v4
        with:
          path: ~/.cache/pypoetry
          key: poetry-$HASH_OF_LOCKFILE
      - name: Install dependencies
        run: poetry install --no-root
      - name: Lint
        run: poetry run ruff check .
      - name: Test
        run: poetry run pytest --cov

Security Audit

# Poetry: audit dependencies for known CVEs
poetry audit

# Rye: use pip-audit integration
rye run pip-audit

Migration to uv

When ready to migrate from Poetry/Rye to uv:

# Export Poetry dependencies
poetry export -f requirements.txt --output requirements.txt

# Initialize uv project
uv init
uv add $(grep -v '^#' requirements.txt | grep -v '^\-' | cut -d'=' -f1)

# Verify
uv sync
uv run pytest

See modern-python skill for the complete uv workflow.

Complementary Skills

SkillRelationship
modern-pythonuv-native workflow (recommended for greenfield projects)
python-backend-expertFramework-specific patterns (Django, FastAPI, Flask)
tddTest-driven development methodology
comprehensive-unit-testing-with-pytestTesting strategies and patterns

Memory Protocol (MANDATORY)

Before starting:

Read .claude/context/memory/learnings.md for prior Python packaging decisions.

After completing: Record any migration issues, version constraints, or resolver conflicts to .claude/context/memory/learnings.md.

ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.

平台分布

Codex

34.51%
按下载量换算84

Claude

33.09%
按下载量换算80

Cursor

18.71%
按下载量换算45

Gemini CLI

9.13%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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