python-ml-workflow — community python-ml-workflow, ScreenBanter, community, ide skills

v1.0.0

About this Skill

Perfect for AI Agents needing advanced machine learning workflow capabilities with Python 3.10+ 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.

alfred1137 alfred1137
[0]
[0]
Updated: 3/12/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Locale and body language aligned
Review Score
7/11
Quality Score
26
Canonical Locale
en
Detected Body Locale
en

Perfect for AI Agents needing advanced machine learning workflow capabilities with Python 3.10+ 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.

Core Value

Empowers agents to create elegant and efficient machine learning workflows utilizing uv, Poetry, and Ruff for optimized development, with strict adherence to PEP 8 and the Zen of Python, and leveraging pytest for testing and the typing module for type hinting

Ideal Agent Persona

Perfect for AI Agents needing advanced machine learning workflow capabilities with Python 3.10+

Capabilities Granted for python-ml-workflow

Automating machine learning model deployment with Poetry and uv
Generating optimized data pipelines using Python 3.10+ and Ruff
Debugging machine learning workflows with pytest and strict type hinting

! Prerequisites & Limits

  • Requires Python 3.10+
  • Limited to uv, Poetry, and Ruff for management and formatting
  • Strict adherence to PEP 8 and the Zen of Python may limit flexibility

Why this page is reference-only

  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

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Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

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FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is python-ml-workflow?

Perfect for AI Agents needing advanced machine learning workflow capabilities with Python 3.10+ 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.

How do I install python-ml-workflow?

Run the command: npx killer-skills add alfred1137/ScreenBanter. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for python-ml-workflow?

Key use cases include: Automating machine learning model deployment with Poetry and uv, Generating optimized data pipelines using Python 3.10+ and Ruff, Debugging machine learning workflows with pytest and strict type hinting.

Which IDEs are compatible with python-ml-workflow?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for python-ml-workflow?

Requires Python 3.10+. Limited to uv, Poetry, and Ruff for management and formatting. Strict adherence to PEP 8 and the Zen of Python may limit flexibility.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add alfred1137/ScreenBanter. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use python-ml-workflow immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

python-ml-workflow

Install python-ml-workflow, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

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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