rag-pipeline-builder
Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".
npx skills add majiayu000/claude-skill-registry --skill rag-pipeline-builder --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.
# RAG Pipeline Builder Design end-to-end RAG pipelines for accurate document retrieval and generation. ## Pipeline Architecture ``` Documents → Chunking → Embedding → Vector Store → Retrieval → Reranking → Generation ``` ## Chunking Strategy ```python # Semantic chunking (recommended) from langchain.text_splitter import RecursiveCharacterTextSplitter splitter = RecursiveCharacterTextSplitter( chunk_size=1000, # Characters per chunk chunk_overlap=200, # Overlap between chunks separators=["\n\n", "\n", ". ", " ", ""], length_function=len, ) chunks = splitter.split_text(document.text) # Add metadata to each chunk for i, chunk in enumerate(chunks): chunks[i] = { "text": chunk, "metadata": { "source": document.filename, "page": calculate_page(i), "chunk_id": f"{document.id}_chunk_{i}", } } ``` ## Metadata Schema ```typescript interface ChunkMetadata { // Source information document_id: string; source: string; url?: string; // Location page?: number; section?: string; chunk_index: number; // Content classification content_type: "text" | "code" | "table" | "list"; language?: string; // Timestamps created_at: Date; updated_at: Date; // Retrieval optimization keywords: string[]; summary?: s
- Pipeline Architecture
- Chunking Strategy
- Metadata Schema
- Vector Store Setup
- Retrieval Strategies
- Reranking
- Query Enhancement
- Evaluation Plan
- Context Window Management
- Best Practices
- Output Checklist
What does the rag-pipeline-builder skill do?
Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-pipeline-builder --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.