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

Agent Skill

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。

总安装

759

周安装

31

GitHub Stars

25

下载量

243
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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