overthinkos/python-ml
ov-layers/skills/python-ml/SKILL.md
Core ML/AI Python environment with PyTorch, vLLM runtime deps, and CUDA support. Tier 2 environment-owner meta-layer that composes llama-cpp. Use when working with machine learning, PyTorch, HuggingFace, or GPU computing.
development
Updated Apr 24, 2026
$ install --global
skillsauthnpx skillsauth add overthinkos/overthink-plugins python-mlInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- python-ml
- description:
- |
python-ml -- Core ML Python environment (Tier 2 meta-layer)
Layer Properties
| Property | Value |
|----------|-------|
| Dependencies | cuda |
| Sub-layers | llama-cpp |
| Install files | layer.yml, pixi.toml, tasks: |
Architecture: Tier 2 Environment-Owner Meta-Layer
This layer owns the pixi.toml for the core ML Python environment and composes the llama-cpp Tier 1 layer via layers: [llama-cpp]. Build order: pixi environment → llama-cpp (binaries) → python-ml user-phase tasks (vLLM wheel).
Environment Variables
| Variable | Value |
|----------|-------|
| NVIDIA_PYTHON_PROJECT | ~/.pixi |
| LD_LIBRARY_PATH | /usr/lib64:$HOME/llama.cpp |
Plus from llama-cpp sub-layer:
| Variable | Value |
|----------|-------|
| LLAMA_CPP_PATH | ~/llama.cpp |
| PATH (appended) | ~/llama.cpp |
Packages (pixi.toml)
PyPI: PyTorch >= 2.10.0 (CUDA 13.0), xformers, transformers, accelerate, safetensors, numpy, scipy, einops, pillow, kornia, spandrel, torchsde, vLLM runtime deps (blake3, flashinfer, numba, ray, xgrammar, etc.), gguf, pydantic, aiohttp
Post-pixi Installs (tasks:)
- vLLM 0.19 cu130 nightly wheel (
pip install --no-deps)
Used In Images
/ov-images:python-ml/ov-images:immich-ml
Related Layers
/ov-layers:llama-cpp— Sub-layer: llama.cpp binaries (composed vialayers:)/ov-layers:cuda— CUDA toolkit dependency/ov-layers:jupyter-ml— Full ML + Jupyter variant (superset of python-ml's pixi env)/ov-layers:unsloth-studio— Fine-tuning variant (similar pixi env + unsloth)
When to Use This Skill
Use when the user asks about:
- Machine learning Python environment
- PyTorch, transformers, or vLLM setup
- CUDA Python integration
- The
python-mllayer, its packages, or its meta-layer composition - The two-tier layer architecture for ML layers
Related
/ov:layer— layer authoring reference (layer.ymlschema, task verbs, service declarations)/ov:test— declarative testing (tests:block,ov image test,ov test)
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