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.
npx skills add majiayu000/claude-skill-registry --skill vector-index-tuning-dokhacgiakhoa-antigravity-ide --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.
# 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, ch
- Use this skill when
- Do not use this skill when
- Instructions
- Safety
- Resources
- 🧠 Knowledge Modules (Fractal Skills)
- 1. [implementation-playbook](./sub-skills/implementation-playbook.md)
What does the vector-index-tuning skill do?
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill vector-index-tuning-dokhacgiakhoa-antigravity-ide --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.