Agent skill · data analytics · wshobson
vector-index-tuning
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
Why this skill is useful
Provides specific tuning parameters and strategies for optimizing vector indexes that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-05. 38,570 stars on the source repository.
What this skill does
Vector Index Tuning Guide to optimizing vector indexes for production performance. When to Use This Skill Tuning HNSW parameters Implementing quantization Optimizing memory usage Reducing search latency Balancing recall vs speed Scaling to billions of vectors Core Concepts 1. Index Type Selection 2. HNSW Parameters Parameter Default Effect ------------------ ------- ---------------------------------------------------- M 16 Connections per node, ↑ = better recall, more memory efConstruction 100 Build quality, ↑ = better index, slower build efSearch 50 Search quality, ↑ = better recall, slower search 3. Quantization Types Templates and detailed worked examples Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates. Best Practices Do's Benchmark with real queries - Synthetic may not represent production Monitor recall continuously - Can degrade with data drift Start with defaults - Tune only when needed Use quantization - Significant memory savings Consider tiered storage - Hot/cold data separation Don'ts Don't over-optimize early - Profile first Don't ignore build time - Index updates have cost Don't forget reindexing - Plan for maintenance Don't skip warming - Cold indexes are slow
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills wshobson/vector-index-tuning