---

name: uv-python-execution description: Enforces using uv to run all Python scripts and ty for type checking. Includes inline script metadata (PEP 723) for one-time scripts with dependencies.

UV Python Execution & Type Checking

Activation

When this skill is triggered, ALWAYS display this banner first:

╭─────────────────────────────────────────────────────────────╮
│  🐍 SKILL ACTIVATED: uv-python-execution                    │
├─────────────────────────────────────────────────────────────┤
│  Mode: [Python Execution | Type Checking | Inline Script]   │
│  Action: Using uv/ty toolchain instead of direct invocation │
╰─────────────────────────────────────────────────────────────╯

Replace [Mode] with the detected trigger type.

When to Use

This skill activates automatically whenever:

Python Execution - Pattern matching on:

• python script.py
• python3 script.py
• python -m module
• python3 -m module
• Any direct Python interpreter invocation

One-Time Scripts - Pattern matching on:

• "write a quick script"
• "make a standalone script"
• "one-off script"
• "throwaway script"
• Scripts needing packages not in the project

Type Checking - Pattern matching on:

• pyright
• mypy
• Any type checking operation

Rules

1. ALWAYS use uv run to execute Python scripts and modules.

2. ALWAYS use ty for type checking (not pyright or mypy directly).

Correct Usage - Python Execution

# Running a script
uv run python script.py

# Running a module
uv run python -m pytest tests/

# Running with arguments
uv run python train.py --epochs 100 --lr 1e-4

# Running inline Python
uv run python -c "import torch; print(torch.cuda.is_available())"

Incorrect Usage - Python Execution (NEVER DO)

# Direct python invocation - WRONG
python script.py
python3 script.py
python -m pytest
./script.py  # if shebang points to python

Correct Usage - Type Checking

# Check entire project
ty check

# Check specific file
ty check src/models/unet.py

# Check specific directory
ty check src/

# Check with specific options
ty check --warn-unreachable src/

Incorrect Usage - Type Checking (NEVER DO)

# Using pyright directly - WRONG
pyright src/
uv run pyright src/

# Using mypy directly - WRONG
mypy src/
uv run mypy src/

Why

Python Execution with uv run

1. Dependency isolation: uv run ensures the script runs with the project's virtual environment and dependencies
2. Reproducibility: Uses the exact versions locked in uv.lock
3. Consistency: All team members run scripts the same way
4. No activation needed: Works without manually activating the venv

Type Checking with ty

1. Speed: ty is extremely fast (written in Rust by Astral, same team as uv/ruff)
2. Unified toolchain: Part of the Astral ecosystem (uv, ruff, ty) for consistent tooling
3. Project-aware: Reads pyproject.toml configuration automatically
4. Better defaults: Sensible defaults for modern Python projects

Exceptions

Python Execution

1. System Python checks: python3 --version to check system Python (but prefer uv run python --version)
2. Inside Docker/CI: When explicitly running inside a container with pre-configured environment
3. User explicitly requests: If user says "use python directly" or "don't use uv"

Type Checking

1. CI/pre-commit already configured: If CI uses pyright/mypy explicitly
2. User explicitly requests: If user says "use pyright" or "use mypy"
3. Compatibility checks: When verifying behavior matches pyright/mypy specifically

Implementation

Python Execution

When writing Bash commands that execute Python:

1. Prefix with uv run: Always add uv run before python or python3
2. Check existing scripts: If referencing a script in scripts/, verify it uses uv run in justfile tasks
3. For new scripts: Add corresponding justfile tasks that use uv run

Type Checking

When checking types or fixing type errors:

1. Use ty check: Run ty check instead of pyright/mypy
2. Target specific files: Use ty check path/to/file.py for focused checking
3. Iterative fixing: Run ty check, fix errors, repeat until clean

Examples

Training a Model

# Correct
uv run python scripts/train.py --config config.yaml

# Also correct - using just task
just train

Running Tests

# Correct
uv run pytest tests/ -v

# Also correct
just test

Quick Python Check

# Correct
uv run python -c "import torch; print(torch.__version__)"

Type Checking Before Fixing Code

# Check entire project for type errors
ty check

# Check specific module you're working on
ty check src/models/

# Check a specific file after making changes
ty check src/trainer/lightning_module.py

One-Time Scripts (Inline Dependencies)

When the user asks for a quick standalone script with external dependencies, use uv's inline script metadata (PEP 723). This runs scripts with their own dependencies without polluting the project environment.

When to Use

• "write a quick script to fetch data from an API"
• "make a one-off script to process this CSV"
• "create a standalone script that uses requests/pandas/etc."
• Any throwaway or utility script needing packages not in the project

Inline Script Format

Add a special comment block at the top of the script:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.11"
# dependencies = [
#     "requests",
#     "rich",
# ]
# ///

import requests
from rich import print

response = requests.get("https://api.github.com")
print(response.json())

Running Inline Scripts

# Run directly (uv auto-detects inline metadata)
uv run script.py

# Or make executable and run
chmod +x script.py
./script.py

Examples

Fetch and display JSON data:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.11"
# dependencies = ["httpx", "rich"]
# ///

import httpx
from rich.console import Console
from rich.json import JSON

console = Console()
data = httpx.get("https://api.github.com/repos/astral-sh/uv").json()
console.print(JSON.from_data(data))

Quick CSV processing:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.11"
# dependencies = ["pandas", "tabulate"]
# ///

import pandas as pd

df = pd.read_csv("data.csv")
print(df.describe().to_markdown())

Web scraping one-liner:

#!/usr/bin/env -S uv run --script
# /// script
# dependencies = ["beautifulsoup4", "requests"]
# ///

import requests
from bs4 import BeautifulSoup

soup = BeautifulSoup(requests.get("https://example.com").text, "html.parser")
print(soup.title.string)

Key Points

1. No project setup needed: Dependencies are installed in an isolated cache
2. Reproducible: Specify exact versions if needed (requests>=2.28,<3)
3. Self-contained: Script documents its own requirements
4. Fast: uv caches dependencies, subsequent runs are instant

When NOT to Use Inline Scripts

• Script will be used repeatedly by the team → Add to project dependencies
• Script is part of the main codebase → Use uv run with project deps
• Complex multi-file scripts → Create proper package structure

Red Flags

❌ Using python or python3 directly without uv run ❌ Activating venv manually before running (.venv/bin/activate) ❌ Using pip install instead of uv add or uv pip install ❌ Using pyright or mypy instead of ty check ❌ Running type checking with uv run pyright or uv run mypy ❌ Installing packages globally for one-time scripts (use inline metadata instead)

Success Criteria

✅ All Python script executions use uv run prefix ✅ New scripts have corresponding justfile tasks ✅ Consistent execution environment across all runs ✅ All type checking uses ty check ✅ Type errors are checked before and after code changes ✅ One-time scripts use inline metadata (PEP 723) for dependencies