/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.
One skill from wshobson-agents.
shell
$ npx -y skills add wshobson/agents --skill vector-index-tuning --agent claude-codeInstalls just this skill. Get the whole plugin for auto-invocation.
How it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/vector-index-tuning
Context preview
The summary Claude sees to decide when to auto-load this skill.
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
Stats
Stars38,171
Forks4,092
LanguagePython
LicenseMIT
Ships with wshobson-agents
SKILL.md
vector-index-tuning.SKILL.md
--- 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. ## 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 ``` Data Size Recommended Index ──────────────────────────────────────── < 10K vectors → Flat (exact search) 10K - 1M → HNSW 1M - 100M → HNSW + Quantization > 100M → IVF + PQ or DiskANN ``` ### 2. HNSW Parameters | Parameter | Default | Effect | | ------------------ | ------- | ---------------------------------------------------- | | **M** | 16 | Connections per node, ↑ = better recall, more memory |
