uv-package-manager

Overview of uv-package-manager skill

The uv-package-manager skill helps an agent give accurate, task-ready guidance for using uv in real Python projects. It is best for developers deciding whether to adopt uv, setting up a new project, migrating from pip or Poetry, speeding up CI, or standardizing dependency workflows across local dev, Docker, and GitHub Actions.

What users usually care about first is not “what is uv?” but whether it will fit their workflow. This skill is useful because it focuses on the practical jobs uv solves well: fast installs, reproducible environments, lockfiles, Python version management, workspace support, and cleaner project setup with fewer tools. It also covers migration and advanced patterns, which matters if you are not starting from a blank repo.

The main differentiator of the uv-package-manager skill is that it is install-decision oriented and workflow oriented. Instead of only naming commands, it helps an agent choose between common patterns such as uv sync, uv add, uv run, Python installation, lockfile usage, CI setup, Docker layering, and monorepo workspaces.

Best fit for this skill

Use the uv-package-manager skill if you want help with:

• creating a new Python project around pyproject.toml
• replacing slow pip-based install flows
• adopting lockfiles and reproducible environments
• setting up Python versions with uv python install
• improving CI or Docker dependency setup
• managing multiple packages in a workspace or monorepo

When this skill is less useful

This is not the best choice if you need deep package publishing guidance, Conda-first data science environment design, or generic Python tutoring unrelated to dependency and environment management. It is also less valuable if your team is fully committed to another tool and cannot change install or lock workflows.

What makes uv-package-manager worth installing

For most users, the value of the uv-package-manager skill is faster and more opinionated project setup guidance than a generic prompt can provide. The repository includes a main guide plus an advanced reference with concrete patterns for CI, Docker, and monorepos, which are exactly the areas where adoption decisions usually get blocked.

How to Use uv-package-manager skill

Install context for uv-package-manager

Add the skill to your agent environment, then invoke it when the task involves Python project setup, dependency management, lockfiles, virtual environments, interpreter installs, or migration to uv.

A typical install pattern is:

npx skills add https://github.com/wshobson/agents --skill uv-package-manager

Once available, call it when your prompt includes a clear project goal, current toolchain, and any constraints around CI, Docker, operating system, or repo structure.

Read these files first

If you want to inspect the source before relying on the skill, start here:

1. SKILL.md
2. references/advanced-patterns.md

SKILL.md gives the main operating model. references/advanced-patterns.md is the high-value file for real adoption work because it covers monorepos, CI/CD, Docker, troubleshooting, and migration patterns.

What input the skill needs

The uv-package-manager usage quality depends heavily on the context you provide. Include:

• whether this is a new or existing Python project
• current package workflow: pip, pip-tools, Poetry, or mixed
• target Python versions
• whether you need dev dependencies, lockfiles, or workspaces
• whether the setup must work in CI, Docker, or both
• any existing files such as requirements.txt, pyproject.toml, or poetry.lock

Without that, the agent can still explain uv, but it will be less useful for project setup.

Turn a rough request into a strong prompt

Weak prompt:

“Help me use uv.”

Stronger prompt:

“I have an existing Python service using requirements.txt and GitHub Actions. I want to migrate to uv, keep reproducible installs, support Python 3.11 and 3.12, and avoid breaking our Docker build. Show the recommended file changes, commands, and CI updates.”

Why this works: it gives migration state, deployment context, compatibility needs, and the output format you want.

Ask for the workflow, not just commands

The best way to use the uv-package-manager skill is to request an end-to-end path. For example:

• “Set up a new Python CLI project with uv, dev dependencies, and a lockfile.”
• “Migrate this Poetry project to uv with minimal behavior change.”
• “Rewrite this CI pipeline to use uv sync and cached installs.”
• “Design a uv-package-manager for Project Setup workflow for a monorepo.”

That framing pushes the skill toward a working sequence instead of isolated command snippets.

Core command areas the skill can guide well

The repository evidence suggests the skill is strongest when asked about:

• uv add for dependency changes
• uv sync for environment reproduction
• uv run for command execution inside the managed environment
• uv python install for interpreter management
• lockfile-based workflows
• workspace and monorepo configuration
• CI and Docker integration

These are the highest-signal topics to test during evaluation.

Suggested workflow for a new project

For a new repo, a practical uv-package-manager guide request should usually cover:

1. initialize the project metadata in pyproject.toml
2. install or select the Python version
3. add runtime and dev dependencies
4. generate and use the lockfile
5. run tests or scripts via uv run
6. mirror the same install model in CI
7. freeze Docker installs with lock-aware commands if containers matter

If the first response skips lockfiles, CI parity, or command execution conventions, ask the agent to fill those gaps.

Suggested workflow for migration

For an existing repo, ask the skill to map old artifacts to new ones. Good migration prompts explicitly mention files like:

• requirements.txt
• requirements-dev.txt
• pyproject.toml
• poetry.lock
• .github/workflows/*.yml
• Dockerfile

This makes the output concrete: what to keep, what to replace, and where uv should become the source of truth.

Practical tips that change output quality

Ask the agent to produce:

• exact commands in order
• expected file edits
• a rollback-safe migration plan
• CI or Docker diffs if relevant
• warnings about common breakpoints such as lockfile mismatch or mixed tool usage

This matters because uv-package-manager install decisions often fail at integration boundaries, not at the first local command.

Repository paths worth using in prompts

If your agent can inspect repository files, direct it to the advanced reference when you need non-trivial output:

• references/advanced-patterns.md

This is especially useful for:

• workspace setup
• GitHub Actions with astral-sh/setup-uv
• Docker image layering
• troubleshooting and optimization

A high-quality example prompt

“Use the uv-package-manager skill to design a project setup for a Python API repo. We need Python 3.12, locked dependencies, pytest and ruff as dev tools, GitHub Actions caching, and a Docker build that installs dependencies reproducibly. Show pyproject.toml structure, uv commands, CI YAML changes, and any cautions for teams migrating from pip.”

That prompt is strong because it names the environment, tools, deployment path, and desired deliverables.

uv-package-manager skill FAQ

Is uv-package-manager good for beginners?

Yes, if the beginner already knows basic Python project structure. The skill can shorten the path to a modern setup, but it is most valuable when you have a real project to configure rather than when you are only learning Python fundamentals.

Is this better than a normal prompt about uv?

Usually yes for setup-heavy tasks. A generic prompt may explain uv, but the uv-package-manager skill is more likely to cover the practical workflow edges people miss: lockfiles, uv run, interpreter installation, CI caching, Docker patterns, and migration tradeoffs.

Does the skill help with migration from pip or Poetry?

Yes. That is one of the clearest use cases. The source material explicitly positions uv as compatible with common Python dependency workflows and includes migration-oriented advanced patterns.

Can I use uv-package-manager for CI and Docker?

Yes. This is one of the stronger reasons to install it. The advanced reference includes GitHub Actions setup with astral-sh/setup-uv and Docker examples using uv sync --frozen --no-dev.

Does it cover monorepos or workspaces?

Yes. The repository includes a workspace example using [tool.uv.workspace] and package members, so the skill is a good fit if you need uv-package-manager for Project Setup across multiple Python packages.

When should I not use uv-package-manager?

Skip it if your task is mainly about publishing to PyPI, Conda environment management, or broad Python architecture unrelated to dependency tooling. Also skip it if your environment forbids adopting uv, since the skill is optimized for using uv, not arguing around that constraint.

How to Improve uv-package-manager skill

Give the current state, not just the goal

The fastest way to improve uv-package-manager usage is to tell the agent what exists today. “Set up uv” is much weaker than “we have requirements.txt, a Dockerfile, and GitHub Actions, and we want a lockfile-based uv workflow.”

Current-state context helps the agent choose migration steps instead of inventing a greenfield plan.

Ask for outputs in repo-edit form

To get better results from the uv-package-manager skill, request specific deliverables:

• shell commands
• pyproject.toml edits
• CI YAML updates
• Dockerfile changes
• migration checklist
• troubleshooting notes

This reduces vague explanation and increases implementation value.

Surface your non-negotiable constraints

Important constraints include:

• Python version support
• cross-platform requirements
• offline or cached CI behavior
• whether developers must avoid global Python installs
• whether lockfiles are mandatory
• whether monorepo packages need shared management

These constraints often determine whether the skill should recommend uv sync, workspace config, or a more conservative migration plan.

Common failure mode: mixed-tool ambiguity

A common problem is asking for uv help while leaving ownership between pip, Poetry, and uv unclear. The output gets much better if you ask the agent to define:

• the source of truth for dependencies
• whether old files stay temporarily or are removed
• which command developers should run day to day
• how CI should install dependencies after migration

Common failure mode: no environment target

If you do not mention local dev, CI, Docker, or monorepo needs, the answer may be technically correct but operationally incomplete. The strongest uv-package-manager guide requests state where the workflow must run and remain reproducible.

Iterate after the first draft

After the first answer, improve it with follow-ups like:

• “Now optimize this for GitHub Actions cache efficiency.”
• “Rewrite the Docker steps to maximize layer reuse.”
• “Show the minimal migration path with lowest team disruption.”
• “Add a workspace layout for two internal packages.”

This skill gets more useful as the task becomes more concrete.

Validate against the advanced reference

When the answer touches CI, Docker, or workspaces, compare it against references/advanced-patterns.md. That file is the best place to sanity-check whether the generated approach matches the repository’s strongest examples.

Ask the agent to explain tradeoffs explicitly

A good improvement prompt is:

“Use the uv-package-manager skill and explain not only the commands, but why you chose this workflow over pip or Poetry for this repo.”

This forces decision-quality output: adoption rationale, migration cost, and operational tradeoffs, not just syntax.

Best way to evaluate before adoption

If you are deciding whether to install or trust this skill, test it on one realistic scenario:

• a new service setup
• a pip to uv migration
• a GitHub Actions rewrite
• a Docker dependency install redesign

If the output gives ordered commands, file-level edits, and reproducibility guidance with minimal follow-up, the uv-package-manager skill is a strong fit.