embedding-optimization
Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
npx skills add majiayu000/claude-skill-registry --skill embedding-optimization --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.
# Embedding Optimization Optimize embedding generation for cost, performance, and quality in RAG and semantic search systems. ## When to Use This Skill Trigger this skill when: - Building RAG (Retrieval Augmented Generation) systems - Implementing semantic search or similarity detection - Optimizing embedding API costs (reducing by 70-90%) - Improving document retrieval quality through better chunking - Processing large document corpora (thousands to millions of documents) - Selecting between API-based vs. local embedding models ## Model Selection Framework Choose the optimal embedding model based on requirements: **Quick Recommendations:** - **Startup/MVP:** `all-MiniLM-L6-v2` (local, 384 dims, zero API costs) - **Production:** `text-embedding-3-small` (API, 1,536 dims, balanced quality/cost) - **High Quality:** `text-embedding-3-large` (API, 3,072 dims, premium) - **Multilingual:** `multilingual-e5-base` (local, 768 dims) or Cohere `embed-multilingual-v3.0` For detailed decision frameworks including cost comparisons, quality benchmarks, and data privacy considerations, see `references/model-selection-guide.md`. **Model Comparison Summary:** | Model | Type | Dimensions | Cost per
- When to Use This Skill
- Model Selection Framework
- Chunking Strategies
- Caching Implementation
- Dimensionality Trade-offs
- Batch Processing Optimization
- Performance Monitoring
- Working Examples
- Integration Points
- Common Patterns
- Quick Reference Checklist
python scripts/chunk_document.py \ Embed documents with caching enabled python scripts/cached_embedder.py \
What does the embedding-optimization skill do?
Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill embedding-optimization --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.