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Python Env

Organization
aiskillstore
python-env

Fast Python environment management with uv (10-100x faster than pip). Triggers on: uv, venv, pip, pyproject, python environment, install package, dependencies.

Overview

Publisheraiskillstore
Repositorymarketplace
Skill namepython-env
Stars
427
Forks
45
Bundled files
4
LicenseMIT
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Python Env 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.
  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/aiskillstore/marketplace.git /tmp/marketplace
mkdir -p .claude/skills
cp -r /tmp/marketplace/skills/0xdarkmatter/python-env .claude/skills/python-env
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Python Env 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 Python Env 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 Python Env 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.

Python Environment

Fast Python environment management with uv. Prefer the uv project workflow (uv add / uv sync / uv run) over the uv pip compatibility layer — it manages pyproject.toml + a lockfile for you and is reproducible.

Quick Commands

TaskCommand
Start a projectuv init <name> (app) · uv init --package <name> (installable, src/ layout)
Add dependencyuv add httpx
Add dev dependencyuv add --dev pytest ruff
Remove dependencyuv remove httpx
Sync env from lockfileuv sync
Run in project envuv run pytest
Update lockfileuv lock
Install a CLI tooluv tool install ruff · one-shot: uvx ruff
Install a Pythonuv python install 3.12

Start a Project

bash
# Application (flat layout, no package build)
uv init myapp

# Installable package (src/ layout — separate tests/ that import by name)
uv init --package wordtools
# → src/wordtools/__init__.py, pyproject.toml with build-system

uv init creates pyproject.toml, pins a Python version, and prepares the project for uv add / uv sync. The --package (src) layout is preferred for anything with a test suite or that you intend to ship.

Manage Dependencies

bash
# Add runtime deps (writes to [project.dependencies] + updates the lockfile)
uv add "httpx>=0.25" pydantic

# Add dev-only deps (writes to the dev dependency-group)
uv add --dev pytest ruff mypy

# Add with extras
uv add "fastapi[standard]"

# Remove
uv remove httpx

# Install everything from pyproject + uv.lock into .venv (reproducible)
uv sync

# Refresh the lockfile (e.g. after manual pyproject edits)
uv lock

uv creates and manages .venv automatically — you rarely activate it; just prefix commands with uv run.

Run Code

bash
uv run python script.py     # run a script in the project env
uv run pytest               # run a tool from the dev group
uv run -- ruff check .      # `--` ends uv flag parsing

Never call bare python / pytest / ruff in a uv project — they may resolve to a different interpreter. Always uv run.

CLI Tools (global, not project deps)

bash
uv tool install ruff        # persistent, isolated, on PATH
uv tool upgrade ruff
uvx ruff check .            # ephemeral one-shot run, nothing installed

Use uv tool / uvx for developer CLIs (ruff, pre-commit, httpie). Use uv add only for things your code imports.

Python Versions

bash
uv python install 3.12      # download a managed interpreter
uv python list              # show available + installed
uv init --python 3.12 app   # pin a project to a version

Check python.org for the current stable (3.14 as of 2026-07; recent releases add opt-in free-threading and a JIT). 3.11+ is a sensible floor for new projects (TaskGroup, Self, faster interpreter).

Minimal pyproject.toml

toml
[project]
name = "my-project"
version = "0.1.0"
requires-python = ">=3.11"
dependencies = [
    "httpx>=0.25",
    "pydantic>=2.0",
]

# Dev deps live here; `uv add --dev <pkg>` manages this group.
[dependency-groups]
dev = [
    "pytest>=8.0",
    "ruff>=0.4",
    "mypy>=1.10",
]

Compatibility Layer (uv pip) — last resort

uv pip mirrors pip's interface for environments uv doesn't manage (a hand-made venv, a legacy requirements.txt, CI that isn't uv-native). It does not update pyproject.toml or the lockfile — prefer uv add / uv sync whenever you control the project.

bash
uv venv                              # bare venv (no project)
uv pip install -r requirements.txt   # legacy requirements file
uv pip install -e .                  # editable install into an unmanaged venv
uv pip compile requirements.in -o requirements.txt   # pin a requirements.txt

Troubleshooting

IssueSolution
"No Python found"uv python install 3.12
Pin project Pythonuv init --python 3.12 or edit requires-python
Lock/resolve conflictuv lock --resolution=lowest-direct to probe, then loosen bounds
Stale env after pulluv sync
Cache issuesuv cache clean

When to Use

  • Always use uv over pip — 10-100x faster
  • uv add / uv remove / uv sync for project dependencies (not uv pip install)
  • uv run to execute anything inside the project env
  • uv tool install / uvx for standalone developer CLIs
  • uv pip only for environments uv doesn't manage

Additional Resources

For detailed patterns, load:

  • ./references/pyproject-patterns.md - Full pyproject.toml examples, tool configs
  • ./references/dependency-management.md - Lock files, workspaces, private packages
  • ./references/publishing.md - PyPI publishing, versioning, CI/CD

See Also

This is a foundation skill with no prerequisites.

Build on this skill:

  • python-typing-ops - Type hints for projects
  • python-pytest-ops - Testing infrastructure
  • python-fastapi-ops - Web API development

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Python Env AI skill do?

Fast Python environment management with uv (10-100x faster than pip). Triggers on: uv, venv, pip, pyproject, python environment, install package, dependencies.

Why use Python Env on TypingMind?

Because you install it once and use it with any model. Python Env 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 Python Env in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aiskillstore/marketplace/tree/main/skills/0xdarkmatter/python-env. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Python Env?

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 Python Env?

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

Is the Python Env AI skill free?

Yes. It is published on GitHub by aiskillstore under the MIT license. 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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