uv-project-runner

Name: uv-project-runner
Availability: InStock
Author: delay-n-days

Manage a unified Python virtual environment for workspace skills using uv.

Updated Mar 11, 2026

Search Tags:#environment management #dependency management #python #uv #workspace #virtual environment

Ready to run

npx skills add https://github.com/Delay-n-days/dbcjson5 --skill uv-project-runner

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: uv-project-runner
Source: https://github.com/Delay-n-days/dbcjson5/tree/main/.github/skills/uv-project-runner
Command: npx skills add https://github.com/Delay-n-days/dbcjson5 --skill uv-project-runner

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill centralizes Python environment management for all skills, ensuring consistency and reducing disk space by using a single virtual environment for the entire workspace.

Core Features & Use Cases

Unified Virtual Environment: All Python scripts across different skills run from a single .venv located at the workspace root.
Centralized Dependency Management: Dependencies are managed via the workspace's pyproject.toml and uv.lock files.
Simplified Script Execution: Use uv run to execute Python scripts within the shared environment.
Use Case: Running PDF extraction, CSV conversion, or DBC generation scripts that rely on Python packages, ensuring all dependencies are managed efficiently and consistently.

Quick Start

Navigate to your workspace root and run any Python script using uv run python path/to/your/script.py.

Frequently Asked Questions about uv-project-runner

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I manage Python dependencies for multiple scripts in a single workspace?▼

You can manage Python dependencies for multiple scripts by centralizing them in a workspace-level `pyproject.toml` file. This ensures all scripts run from a single virtual environment using the `uv` package manager for consistency.

Why use a unified virtual environment for Python scripts across different skills?▼

A unified virtual environment reduces disk space and ensures consistent dependency versions across all Python scripts. By using a single `.venv` at the workspace root, you avoid conflicting package installations.

How do I run a Python script using uv in a shared workspace?▼

To run a Python script in a shared workspace, navigate to the workspace root and execute the command `uv run python path/to/your/script.py`. This runs the script within the unified virtual environment.

Does uv work with pyproject.toml for workspace dependency management?▼

Yes, `uv` works directly with `pyproject.toml` and generates a `uv.lock` file to manage dependencies. This centralizes Python package management for the entire workspace within the shared virtual environment.

Can I use uv to run PDF extraction or CSV conversion scripts with shared dependencies?▼

Yes, you can run Python scripts for tasks like PDF extraction or CSV conversion using `uv run`. This executes them within the shared workspace environment, ensuring all required packages are managed efficiently.

What are the limitations of using a single virtual environment for a Python workspace?▼

Using a single virtual environment for a Python workspace means all skills share the exact same dependency versions. This limits running scripts that require conflicting package versions simultaneously within the same workspace.