ai-ml-data-science
End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).
npx skills add majiayu000/claude-skill-registry --skill ai-ml-data-science --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.
# Data Science Engineering Suite - Quick Reference This skill turns **raw data and questions** into **validated, documented models** ready for production: - **EDA workflows**: Structured exploration with drift detection - **Feature engineering**: Reproducible feature pipelines with leakage prevention and train/serve parity - **Model selection**: Baselines first; strong tabular defaults; escalate complexity only when justified - **Evaluation & reporting**: Slice analysis, uncertainty, model cards, production metrics - **SQL transformation**: SQLMesh for staging/intermediate/marts layers - **MLOps**: CI/CD, CT (continuous training), CM (continuous monitoring) - **Production patterns**: Data contracts, lineage, feedback loops, streaming features **Modern emphasis (2026):** Feature stores, automated retraining, drift monitoring (Evidently), train-serve parity, and agentic ML loops (plan -> execute -> evaluate -> improve). Tools: LightGBM, CatBoost, scikit-learn, PyTorch, Polars (lazy eval for larger-than-RAM datasets), lakeFS for data versioning. --- ## Quick Reference | Task | Tool/Framework | Command | When to Use | |------|----------------|---------|-------------| | EDA & Profiling
- Quick Reference
- Data Lake & Lakehouse
- Related Skills
- Decision Tree: Choosing Data Science Approach
- Core Concepts (Vendor-Agnostic)
- Implementation Practices (Tooling Examples)
- Do / Avoid
- Pattern 1: End-to-End DS Project Lifecycle
- Pattern 2: Feature Engineering
- Pattern 3: Data Contracts & Lineage
- Pattern 4: Model Selection & Training
- Pattern 5: Evaluation & Reporting
- Pattern 6: Reproducibility & MLOps
- Pattern 7: Feature Freshness & Streaming
What does the ai-ml-data-science skill do?
End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-ml-data-science --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.