Name: vector-index-tuning
Author: wshobson

Imported from wshobson/agents (plugins/llm-application-dev/skills/vector-index-tuning/SKILL.md) via skills.sh. Install upstream with npx skills add wshobson/agents --skill vector-index-tuning. Copyright stays with the author.

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
efConstruction	100	Build quality, ↑ = better index, slower build
efSearch	50	Search quality, ↑ = better recall, slower search

3. Quantization Types

Full Precision (FP32): 4 bytes × dimensions
Half Precision (FP16): 2 bytes × dimensions
INT8 Scalar:           1 byte × dimensions
Product Quantization:  ~32-64 bytes total
Binary:                dimensions/8 bytes

Templates and detailed worked examples

Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.

Best Practices

Do's

Benchmark with real queries - Synthetic may not represent production
Monitor recall continuously - Can degrade with data drift
Start with defaults - Tune only when needed
Use quantization - Significant memory savings
Consider tiered storage - Hot/cold data separation

Don'ts

Don't over-optimize early - Profile first
Don't ignore build time - Index updates have cost
Don't forget reindexing - Plan for maintenance
Don't skip warming - Cold indexes are slow

Install vector-index-tuning and it becomes one more candidate the router can pick - when it fits.

A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.

{ "ocm": "1", "id": "wshobson-agents-vector-index-tuning", "kind": "skill", "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.", "publisher": "wshobson", "version": "1.0.0", "capabilities": { "domains": [ "general" ], "tags": [ "skill-md", "skills-sh" ], "languages": [ "en" ] }, "quality_prior": 0.6, "examples": [ "Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure." ], "primary": false, "metadata": { "source": { "provider": "skills.sh", "repository": "https://github.com/wshobson/agents", "path": "plugins/llm-application-dev/skills/vector-index-tuning/SKILL.md", "ref": "HEAD", "url": "https://www.skills.sh/wshobson/agents/vector-index-tuning", "key": "wshobson/agents/plugins/llm-application-dev/skills/vector-index-tuning/SKILL.md" } }, "instructions": "# Vector Index Tuning\n\nGuide to optimizing vector indexes for production performance.\n\n## When to Use This Skill\n\n- Tuning HNSW parameters\n- Implementing quantization\n- Optimizing memory usage\n- Reducing search latency\n- Balancing recall vs speed\n- Scaling to billions of vectors\n\n## Core Concepts\n\n### 1. Index Type Selection\n\n```\nData Size Recommended Index\n────────────────────────────────────────\n< 10K vectors → Flat (exact search)\n10K - 1M → HNSW\n1M - 100M → HNSW + Quantization\n> 100M → IVF + PQ or DiskANN\n```\n\n### 2. HNSW Parameters\n\n| Parameter ", "cost": { "context_tokens": 500 } }

vector-index-tuning

About