ml-pipeline-orchestrator
Orchestrates complete machine learning pipelines within SpecWeave increments. Activates when users request "ML pipeline", "train model", "build ML system", "end-to-end ML", "ML workflow", "model training pipeline", or similar. Guides users through data preprocessing, feature engineering, model training, evaluation, and deployment using SpecWeave's spec-driven approach. Integrates with increment lifecycle for reproducible ML development.
npx skills add majiayu000/claude-skill-registry --skill ml-pipeline-orchestrator-anton-abyzov-specweave --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ML Pipeline Orchestrator ## Overview This skill transforms ML development into a SpecWeave increment-based workflow, ensuring every ML project follows the same disciplined approach: spec → plan → tasks → implement → validate. It orchestrates the complete ML lifecycle from data exploration to model deployment, with full traceability and living documentation. ## Core Philosophy **SpecWeave + ML = Disciplined Data Science** Traditional ML development often lacks structure: - ❌ Jupyter notebooks with no version control - ❌ Experiments without documentation - ❌ Models deployed with no reproducibility - ❌ Team knowledge trapped in individual notebooks SpecWeave brings discipline: - ✅ Every ML feature is an increment (with spec, plan, tasks) - ✅ Experiments tracked and documented automatically - ✅ Model versions tied to increments - ✅ Living docs capture learnings and decisions ## How It Works ### Phase 1: ML Increment Planning When you request "build a recommendation model", the skill: 1. **Creates ML increment structure**: ``` .specweave/increments/0042-recommendation-model/ ├── spec.md # ML requirements, success metrics ├── plan.md # Pipeline architecture ├── tasks.md # Implementatio
- Overview
- Core Philosophy
- How It Works
- Phase 1: ML Increment Planning
- Phase 2: Pipeline Execution
- Phase 3: Increment Completion
- When to Use This Skill
- ML Pipeline Stages
- 1. Data Stage
- 2. Feature Stage
- 3. Training Stage
- 4. Evaluation Stage
- 5. Explainability Stage
- 6. Deployment Stage
Start ML increment Automatically integrates experiment tracking All MLflow/W&B logs saved to increment folder After training best model Create GitHub issue for model retraining Linked to increment 0042 Issue tracks model performance over time Skill creates increment 0051-fraud-detection with: Guides through: Skill creates increment 0063-ltv-prediction with:
What does the ml-pipeline-orchestrator skill do?
Orchestrates complete machine learning pipelines within SpecWeave increments. Activates when users request "ML pipeline", "train model", "build ML system", "end-to-end ML", "ML workflow", "model training pipeline", or similar. Guides users through data preprocessing, feature engineering, model training, evaluation, and deployment using SpecWeave's spec-driven approach. Integrates with increment lifecycle for reproducible ML development.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-pipeline-orchestrator-anton-abyzov-specweave --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.