notebook-ml-architect
CommunityTurn ML notebooks into production-ready pipelines
System Documentation
What problem does it solve?
Expert guidance for auditing, refactoring, and designing machine learning Jupyter notebooks with production-quality patterns. Use when: (1) Analyzing notebook structure and identifying anti-patterns, (2) Detecting data leakage and reproducibility issues, (3) Refactoring messy notebooks into modular pipelines, (4) Generating templates for ML workflows (EDA, classification, experiments), (5) Adding reproducibility instrumentation (seeding, logging, env capture), (6) Converting notebooks to Python scripts, (7) Generating experiment summary reports. Triggers on: ML notebook, Jupyter audit, notebook refactor, data leakage, experiment template, ipynb best practices, notebook to script, reproducibility.
Core Features & Use Cases
- Audit notebooks for anti-patterns, leakage, modularization, and reproducibility gaps.
- Refactor notebooks into modular Python pipelines and generate template notebooks for common ML workflows (EDA, preprocessing, training, evaluation).
- Generate reproducibility instrumentation and templates (seeding, environment capture, reporting) and convert notebooks to Python scripts.
Quick Start
Provide an ML notebook and choose an operation (audit, refactor, template, report, or convert) to begin producing a production-ready artifact.
Dependency Matrix
Required Modules
Components
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: notebook-ml-architect Download link: https://github.com/BjornMelin/dev-skills/archive/main.zip#notebook-ml-architect Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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