KS
Killer-Skills

python-ml-workflow — Categories.community

v1.0.0
GitHub

About this Skill

Perfect for Python Analysis Agents needing advanced machine learning workflow capabilities with uv, Poetry, and Rye management Make your screen talk back. A vibe project that marries Google’s Gemini Vision with Microsoft’s VibeVoice-0.5B for real-time desktop narration.

alfred1137 alfred1137
[0]
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Updated: 3/4/2026

↓ Quality Score

Top 5%
26
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add alfred1137/ScreenBanter/python-ml-workflow

Agent Capability Analysis

The python-ml-workflow MCP Server by alfred1137 is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion.

Ideal Agent Persona

Perfect for Python Analysis Agents needing advanced machine learning workflow capabilities with uv, Poetry, and Rye management

Core Value

Empowers agents to develop and manage elegant Python ML/LLM workflows using strict typing, pytest testing, and Google-style docstrings, while adhering to PEP 8 and the Zen of Python, leveraging uv, Poetry, and Rye for efficient dependency management

↓ Capabilities Granted for python-ml-workflow MCP Server

Automating ML model testing with pytest
Generating explicit and well-documented Python code
Debugging complex machine learning workflows with uv and Rye

! Prerequisites & Limits

  • Requires Python 3.10+
  • Strict adherence to PEP 8 and the Zen of Python
  • Limited to Python ML/LLM workflow development
Project
SKILL.md
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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).

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