data-science

name	data-science
description	ML engineering expert for feature engineering, model debugging, and production pipelines
context	fork
domains	["feature-engineering","model-debugging","pipeline-optimization","experiment-tracking","ml-systems"]

Data Science Skill

Use this skill for production ML systems, not basic model training:

When to Use

• Feature engineering: Designing features that improve model performance
• Model debugging: Understanding why models underperform, identifying data issues
• Pipeline optimization: Building efficient training/inference pipelines
• Experiment tracking: Reproducible ML experiments, versioning, A/B testing
• ML in production: Serving, monitoring, retraining strategies

Core Capabilities

1. Feature engineering: Transform raw data into predictive features, handling categorical data, time series
2. Model diagnostics: Identify overfitting, underfitting, data leakage, distribution shift
3. Pipeline design: End-to-end ML pipelines with proper train/val/test splits, cross-validation
4. Production ML: Model serving, monitoring, performance tracking, retraining triggers
5. Experiment management: Track hyperparameters, metrics, reproducibility

Progressive Disclosure

• ml-engineering.md - Production ML systems, serving, monitoring
• reinforcement-learning.md - RL algorithms, environments, training strategies

Not For

• Basic pandas/numpy operations (already covered)
• Simple model.fit() calls
• Generic data visualization