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Uv Workflow

Community
Aedelon
uv-workflow

Master uv package manager for Python: project setup, dependency management, virtual environments, lockfiles, CI/CD integration, Docker builds, and migration from pip/poetry. MUST BE USED when user mentions: "uv", "uv add", "uv run", "uv sync", "uv init", "uv lock", "uv venv", "uv pip", "pyproject.toml", "python project setup", "python dependencies", "virtual environment", "venv", "pip install", "poetry to uv", "migrate from pip", "lockfile python", "requirements.txt", "setup.py", "pip freeze", "uv tool", "install package", "add dependency", "python environment", "new python project", "package manager python", "create project", "uv export", "uv cache", "uv python". 10-100x faster than pip. Covers init, add, sync, lock, run, Docker, CI/CD. NOT for npm/pnpm/yarn (JS toolchain), Rust cargo, or deployment (use deployment-assistant).

Overview

PublisherAedelon
Repositoryclaude-code-blueprint
Skill nameuv-workflow
Stars
119
Forks
34
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Aedelon on GitHub. Read the source before you install it.

Installation

Install the Uv Workflow AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Aedelon/claude-code-blueprint/tree/main/skills/uv-workflow
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Aedelon/claude-code-blueprint.git /tmp/claude-code-blueprint
mkdir -p .claude/skills
cp -r /tmp/claude-code-blueprint/skills/uv-workflow .claude/skills/uv-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Uv Workflow in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Uv Workflow on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Uv Workflow is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

UV Package Manager

Ultra-fast Python package installer and resolver written in Rust. 10-100x faster than pip.

Quick Reference

bash
# Project lifecycle
uv init my-project          # Create project
uv add requests pandas      # Add deps
uv add --dev pytest ruff    # Add dev deps
uv remove package           # Remove
uv sync                     # Install all from pyproject.toml
uv lock                     # Generate/update lockfile
uv run pytest               # Run in venv (no activation needed)

# Virtual environments
uv venv                     # Create .venv
uv venv --python 3.12       # With specific Python
uv python install 3.12      # Install Python version
uv python pin 3.12          # Pin for project

# pip-compatible
uv pip install -r requirements.txt
uv pip freeze > requirements.txt

Project Setup Pattern

bash
uv init my-project && cd my-project
uv python pin 3.12
uv add fastapi uvicorn pydantic
uv add --dev pytest ruff mypy black
mkdir -p src/my_project tests
uv run pytest

Creates: pyproject.toml, .python-version, uv.lock, .venv/

pyproject.toml Standard

toml
[project]
name = "my-project"
version = "0.1.0"
requires-python = ">=3.10"
dependencies = [
    "requests>=2.31.0",
    "pydantic>=2.0.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.4.0",
    "ruff>=0.1.0",
    "mypy>=1.5.0",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.uv]
dev-dependencies = []

[tool.uv.workspace]
members = ["packages/*"]  # For monorepos

Lockfile Workflow

bash
uv lock                     # Create/update uv.lock
uv sync --frozen            # Install exact versions (CI)
uv lock --upgrade           # Upgrade all
uv lock --upgrade-package requests  # Upgrade one
uv lock --check             # Verify lockfile is current
uv export --format requirements-txt > requirements.txt  # Export

Rule: Always commit uv.lock to version control.

Docker Integration

dockerfile
FROM python:3.12-slim AS builder
COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
WORKDIR /app
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --no-dev --no-editable

FROM python:3.12-slim
WORKDIR /app
COPY --from=builder /app/.venv .venv
COPY . .
ENV PATH="/app/.venv/bin:$PATH"
CMD ["python", "app.py"]

CI/CD (GitHub Actions)

yaml
name: Tests
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: astral-sh/setup-uv@v2
        with:
          enable-cache: true
      - run: uv python install 3.12
      - run: uv sync --all-extras --dev
      - run: uv run pytest
      - run: uv run ruff check .

Migration Guides

From pip

bash
# Before: pip install -r requirements.txt
# After:
uv init
uv add -r requirements.txt

From poetry

bash
# Before: poetry install
# After: uv already reads pyproject.toml
uv sync

From pip-tools

bash
# Before: pip-compile && pip-sync
# After:
uv lock && uv sync --frozen

Key Commands

CommandPurpose
uv initInitialize project
uv add PKGAdd dependency
uv add --dev PKGAdd dev dependency
uv remove PKGRemove dependency
uv syncInstall all deps
uv sync --frozenInstall exact (CI)
uv lockCreate/update lockfile
uv run CMDRun in venv
uv venvCreate venv
uv python installInstall Python
uv python pinPin Python version
uv cache cleanClear cache

Best Practices

  1. Always use uv run instead of activating venv
  2. Commit uv.lock to git
  3. Use --frozen in CI for reproducible builds
  4. Pin Python version with .python-version
  5. Separate dev deps from production deps
  6. Use workspaces for monorepos
  7. Export requirements.txt for compatibility when needed

Frequently asked questions

What does the Uv Workflow AI skill do?

Master uv package manager for Python: project setup, dependency management, virtual environments, lockfiles, CI/CD integration, Docker builds, and migration from pip/poetry. MUST BE USED when user mentions: "uv", "uv add", "uv run", "uv sync", "uv init", "uv lock", "uv venv", "uv pip", "pyproject.toml", "python project setup", "python dependencies", "virtual environment", "venv", "pip install", "poetry to uv", "migrate from pip", "lockfile python", "requirements.txt", "setup.py", "pip freeze", "uv tool", "install package", "add dependency", "python environment", "new python project", "packa...

Why use Uv Workflow on TypingMind?

Because you install it once and use it with any model. Uv Workflow is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Uv Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Aedelon/claude-code-blueprint/tree/main/skills/uv-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Uv Workflow?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Uv Workflow?

As many as you like. As long as a model supports skills, you can use Uv Workflow with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Uv Workflow AI skill free?

It is published on GitHub by Aedelon. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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