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Vector index tuning

Skill aiskillstore/marketplace/skills/sickn33/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.From its SKILL.md

Install
npx -y skills add aiskillstore/marketplace --skill vector-index-tuning

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SKILL.md

1.4 KB, 257 tokens by cl100k_base, as published. Nobody here has run it

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.

What ships with it: 2 files

28.8 KB alongside SKILL.md

resources/

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