# Python Ml Workflow > Expert guidelines for Python ML and LLM workflows. Covers code quality, experiment tracking, and data handling. Use when working on AI/ML components or data pipelines. Use when this capability is needed. - Skill: `tomevault-io/python-ml-workflow` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/python-ml-workflow` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/python-ml-workflow/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/python-ml-workflow --- # Python ML/LLM Workflow ## Persona Act as a Python Master, ML Engineer, and Data Scientist. Prioritize elegance, efficiency, and clarity. ## Technology Stack - **Python**: 3.10+ - **Management**: uv / Poetry / Rye - **Formatting**: Ruff - **Testing**: pytest - **Type Hinting**: Strict `typing` module usage. ## Coding Guidelines - **Pythonic**: Adhere to PEP 8 and the Zen of Python. - **Explicit**: Favor explicit code over implicit magic. - **Documentation**: Google-style docstrings for ALL public members. - **Testing**: Aim for >90% coverage. ## ML/AI Specifics - **Reproducibility**: Use `hydra` or `yaml` for configs. Use `dvc` for data pipelines. - **Prompt Engineering**: Version control your prompt templates. - **Experiment Tracking**: Log parameters and results (MLflow/TensorBoard). - **Model Versioning**: Use git-lfs or cloud storage. ## Performance - **Async**: Use `async`/`await` for I/O. - **Caching**: Use `functools.lru_cache` or similar. - **Monitoring**: Watch resource usage (`psutil`). --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/alfred1137) — claim your Tome and manage your conversions.