Use for ML model training, inference serving (vLLM, TorchServe, ONNX), data pipelines, RAG/LLM systems, and MLOps. Covers classification, regression, neural networks, ETL, feature engineering, vector databases, model deployment, experiment tracking. PyTorch, scikit-learn, HuggingFace, MLflow, W&B, FAISS, pgvector, Chroma, LoRA, QLoRA, PEFT, DVC, Delta Lake, Spark, Feast, Evidently, data drift, A/B testing.
From george-setupnpx claudepluginhub george11642/george-plugins --plugin george-setupThis skill uses the workspace's default tool permissions.
references/advanced-rag.mdreferences/classical-ml.mdreferences/data-engineering.mdreferences/data-versioning.mdreferences/deep-learning.mdreferences/llm-patterns.mdreferences/ml-experimentation.mdreferences/mlops.mdreferences/model-serving.mdreferences/monitoring-drift.mdreferences/online-evaluation.mdreferences/peft-fine-tuning.mdreferences/vector-databases.mdDesigns and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Enables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
Master skill for machine learning, data science, and data engineering. Routes to specialized references by task type.
| Task | Reference | When to use |
|---|---|---|
| Data pipelines, pandas, polars, ETL, validation | references/data-engineering.md | Data loading, cleaning, transformation, schema validation |
| Classification, regression, clustering | references/classical-ml.md | scikit-learn, feature engineering, model selection |
| Neural networks, GPU training, CNNs, transformers | references/deep-learning.md | PyTorch, transfer learning, training loops |
| RAG, embeddings, prompt engineering, LLM eval | references/llm-patterns.md | LLM integration, chain-of-thought, few-shot |
| Model serving, experiment tracking, CI/CD for ML | references/mlops.md | MLflow, W&B, deployment pipelines |
| pgvector, Pinecone, Chroma, FAISS | references/vector-databases.md | Embedding storage and retrieval |
| Model comparison, statistical significance | references/ml-experimentation.md | Hypothesis testing, nested CV, reproducibility |
| Hybrid retrieval, reranking, HyDE, RAPTOR | references/advanced-rag.md | Multi-vector RAG, agentic RAG |
| TorchServe, BentoML, vLLM, ONNX Runtime | references/model-serving.md | Production inference, batching, GPU serving |
| Data drift, model monitoring, Evidently | references/monitoring-drift.md | PSI, concept drift, prediction distribution |
| LoRA, QLoRA, adapter tuning, HuggingFace PEFT | references/peft-fine-tuning.md | Parameter-efficient fine-tuning |
| DVC, Delta Lake, dataset lineage | references/data-versioning.md | Reproducible data pipelines |
| A/B testing, canary rollout, shadow mode | references/online-evaluation.md | Traffic splitting, sequential testing |
Classical ML vs Deep Learning: Tabular <100k rows → classical (gradient boosting). Images/audio/video → deep learning. Text <10k examples → fine-tuned transformer or TF-IDF + classical. Text generation → LLM.
Fine-tune vs Prompt: Try prompting first. Fine-tune when: consistent format needed, domain knowledge, latency matters, cost at scale. RAG before fine-tuning for knowledge tasks.
Framework: Tabular → scikit-learn + XGBoost/LightGBM. Deep learning → PyTorch. LLM fine-tuning → HuggingFace + PEFT. LLM inference → vLLM or API.
| Anti-pattern | Fix |
|---|---|
| Training on test data | Strict train/val/test split before preprocessing |
| Data leakage | Fit scalers on train only |
| No baseline | Start with simple model |
| Ignoring class imbalance | Use F1/AUROC, stratified splits |
| Notebook spaghetti | Extract to .py modules |
| Deploying without monitoring | Track prediction distributions, drift |
| Skill | Use when |
|---|---|
data-engineering | ETL pipelines, data validation, pandas/polars |
classical-ml | scikit-learn, feature engineering, model selection |
deep-learning | PyTorch, neural networks, GPU training |
llm-patterns | RAG, embeddings, prompt engineering |
mlops | Experiment tracking, model deployment, CI/CD |
vector-databases | Embedding storage, similarity search |
advanced-rag | Hybrid retrieval, reranking, agentic RAG |
model-serving | Production inference, TorchServe, vLLM |
monitoring-drift | Data drift detection, model monitoring |
peft-fine-tuning | LoRA, QLoRA, adapter tuning |
data-versioning | DVC, Delta Lake, dataset lineage |
online-evaluation | A/B testing, canary rollout, shadow mode |