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skills/vector-index-tuning/SKILL.md

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+---
+name: vector-index-tuning
+description: Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
+---
+
+# 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.