Vector Index Tuning Agent Skill

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

4k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

38452

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/wshobson/agents --skill vector-index-tuning

The instruction itself

10 sections, as written by 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

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17k tokens

How to use it

Copy the folder

Take wshobson/vector-index-tuning from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.