data-ai-ml
Build data pipelines, AI systems, and machine learning models with Python. USE THIS for data processing, model training, LLM integration, RAG systems, NLP, vector databases, prompt engineering, knowledge bases, data analysis, and AI/ML workflows. Include when user mentions AI, ML, data science, LLMs, embeddings, retrieval-augmented generation, or intelligent systems.
npx skills add majiayu000/claude-skill-registry --skill data-ai-ml --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, AI & ML Development Skill Build intelligent systems, data pipelines, and machine learning solutions with Python and modern AI tools. ## Your Tech Stack ### Core Libraries - **Python 3.9+** - **LLM Integration**: OpenAI, Anthropic Claude, Ollama, LangChain - **Data Processing**: Pandas, NumPy, Polars - **NLP**: NLTK, spaCy, Transformers - **Vector DB**: Pinecone, Weaviate, Chroma, FAISS, Milvus - **ML Framework**: scikit-learn, PyTorch, TensorFlow - **Data Viz**: Matplotlib, Plotly, Seaborn ### RAG Systems (Knowledge Bases) - Document chunking and embedding - Vector storage and retrieval - Query augmentation - Context ranking and reranking ## Workflow Patterns ### 1. Data Processing Pipeline ```python import pandas as pd import numpy as np # ETL Pattern def extract(): """Load raw data""" return pd.read_csv('data.csv') def transform(raw_data): """Clean and prepare""" data = raw_data.dropna() data['normalized'] = (data['value'] - data['value'].mean()) / data['value'].std() return data def load(processed_data): """Store in database/warehouse""" processed_data.to_sql('processed', engine) # Execute raw = extract() clean = transform(raw) load(clean) ``` ### 2. LLM Integration Patt
- Your Tech Stack
- Core Libraries
- RAG Systems (Knowledge Bases)
- Workflow Patterns
- 1. Data Processing Pipeline
- 2. LLM Integration Pattern
- 3. RAG (Retrieval-Augmented Generation) System
- 4. Prompt Engineering Best Practices
- Data Processing Patterns
- Chunking Strategy (for embeddings)
- Data Validation
- Model Training & Evaluation
- Classification Example
- Deployment Patterns
What does the data-ai-ml skill do?
Build data pipelines, AI systems, and machine learning models with Python. USE THIS for data processing, model training, LLM integration, RAG systems, NLP, vector databases, prompt engineering, knowledge bases, data analysis, and AI/ML workflows. Include when user mentions AI, ML, data science, LLMs, embeddings, retrieval-augmented generation, or intelligent systems.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-ai-ml --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.