A smart automation tool that intelligently scans Python project dependencies to create, manage, or reuse matching Conda environments.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install auto-conda-env
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install auto-conda-env using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Auto Conda Python Env?
Auto Conda Python Env is a specialized utility designed to streamline the often tedious process of setting up local development environments. By analyzing project files like requirements.txt or environment.yml, it ensures that every Python project runs in a perfectly matched, isolated container. This Openclaw Skills extension eliminates manual version matching and dependency conflicts, providing a seamless bridge between raw code and a functional runtime.
Beyond simple package installation, the tool is robust enough to handle complex deep learning setups. It detects hardware requirements and can automatically configure GPU-enabled libraries like PyTorch with the correct CUDA versions. Whether you are initializing a fresh project or jumping into an existing codebase, this skill automates the discovery of Conda executables and environment validation to get you coding faster.
Auto Conda Python Env Use Cases
- Initializing new Python projects with standardized virtual environments.
- Managing multiple projects that require conflicting Python versions or library sets.
- Setting up Machine Learning and Deep Learning environments with specific CUDA/GPU requirements.
- Automating the onboarding process for team members by standardizing environment creation.
- Resolving dependency isolation issues without manual Conda command intervention.
How Auto Conda Python Env Works
- The skill identifies the project path, defaulting to the current working directory if none is specified.
- It scans for dependency manifests (environment.yml, pyproject.toml, requirements.txt, etc.) in a predefined priority order to extract Python versions and package lists.
- It locates the Conda executable by searching common installation paths like Miniconda or Anaconda directories if not found in the system PATH.
- Existing environments are inspected to see if a compatible match already exists, preventing redundant installations.
- If no match is found, it generates a unique environment name and executes creation commands, followed by package installation via Pip or Conda.
- For AI projects, it performs an additional check for GPU requirements and verifies CUDA availability.
- A final summary is provided, including the activation command and environment details.
Auto Conda Python Env Setup
To use this within the Openclaw Skills framework, ensure you have Python 3 and Pip installed. The skill will automatically attempt to find Conda in standard locations.
# Ensure base requirements are met
python3 --version
pip --version
# The skill handles the following internally once triggered:
# 1. Scans project directory
# 2. Locates Conda executable
# 3. Creates/Updates environment
Auto Conda Python Env Data Schema & Taxonomy
The skill interprets and interacts with the following project structures:
| File Type | Data Extracted |
|---|---|
| environment.yml | Full Conda environment definition including channels and dependencies. |
| pyproject.toml | Python version constraints and project dependencies. |
| requirements.txt | Flat list of pip dependencies; cross-referenced with .python-version. |
| setup.py / cfg | Installation requirements and python_requires metadata. |
| Pipfile | Package lists for Pipenv-based projects. |
All generated environments follow the naming convention: [project_name]_env.
Auto Conda Python Env Advanced Features
- Smart environment reuse: Automatically detects and validates 'ghost' or broken environments before deciding to create a new one.
- CUDA/GPU Auto-Configuration: Specialized hooks for PyTorch and TensorFlow to ensure hardware acceleration is active.
- PEP 668 Workarounds: Automatically applies --break-system-packages if the OS blocks pip installations.
- Intelligent Path Discovery: Deep-scans local user directories for Conda installations, making it highly portable across different system configurations.
- Multi-manifest Support: Handles complex projects that mix different dependency management styles.
SKILL.md
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