chunking-strategy
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
npx skills add majiayu000/claude-skill-registry --skill chunking-strategy --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.
# Chunking Strategy for RAG Systems ## Overview Provides chunking strategies for RAG systems, vector databases, and document processing. Recommends chunk sizes, overlap percentages, and boundary detection methods; validates semantic coherence; evaluates retrieval metrics. ## When to Use Use when building or optimizing RAG systems, vector search pipelines, document chunking workflows, or performance-tuning existing systems with poor retrieval quality. ## Instructions ### Choose Chunking Strategy Select based on document type and use case: 1. **Fixed-Size Chunking** (Level 1) - Use for simple documents without clear structure - Start with 512 tokens and 10-20% overlap - Adjust: 256 for factoid queries, 1024 for analytical 2. **Recursive Character Chunking** (Level 2) - Use for documents with structural boundaries - Hierarchical separators: paragraphs → sentences → words - Customize for document types (HTML, Markdown, JSON) 3. **Structure-Aware Chunking** (Level 3) - Use for structured content (Markdown, code, tables, PDFs) - Preserve semantic units: functions, sections, table blocks - Validate structure preservation post-split 4. **Semantic Chunking** (Level 4) - Use for complex docu
- Overview
- When to Use
- Instructions
- Choose Chunking Strategy
- Implement Chunking Pipeline
- Validate Chunk Quality
- Examples
- Fixed-Size Chunking
- Structure-Aware Code Chunking
- Semantic Chunking
- Best Practices
- Core Principles
- Implementation
- Pitfalls to Avoid
Check semantic coherence (requires sentence-transformers)
python -c "
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
chunks = [...] # your chunks
embeddings = model.encode(chunks)
similarity = (embeddings @ embeddings.T).mean()
Measure retrieval precision
relevant = sum(1 for c in retrieved if c in relevant_chunks)
precision = relevant / len(retrieved)What does the chunking-strategy skill do?
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill chunking-strategy --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.