Agent skill · data analytics · rmyndharis
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 domain knowledge on tuning HNSW parameters and optimizing vector index performance that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-02. 1,255 stars on the source repository.
What this skill does
Vector Index Tuning Guide to optimizing vector indexes for production performance. Use this skill when Tuning HNSW parameters Implementing quantization Optimizing memory usage Reducing search latency Balancing recall vs speed Scaling to billions of vectors Do not use this skill when You only need exact search on small datasets (use a flat index) You lack workload metrics or ground truth to validate recall You need end-to-end retrieval system design beyond index tuning Instructions 1. Gather workload targets (latency, recall, QPS), data size, and memory budget. 2. Choose an index type and establish a baseline with default parameters. 3. Benchmark parameter sweeps using real queries and track recall, latency, and memory. 4. Validate changes on a staging dataset before rolling out to production. Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates. Safety Avoid reindexing in production without a rollback plan. Validate changes under realistic load before applying globally. Track recall regressions and revert if quality drops. Resources resources/implementation-playbook.md for detailed patterns, checklists, and templates.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills rmyndharis/vector-index-tuning