Guide · Agent skill
How to use the Vector Index Tuning agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The Vector Index Tuning skill gives an AI agent a structured guide to making vector search faster, more accurate, or lighter on memory. It is aimed at engineers who already run a vector search or retrieval-augmented generation (RAG) system and need to decide how their index should be built. After this guide, you will know how to install the skill, how to ask for help with index decisions, and what the skill can and cannot do for you.
Vector Index Tuningwshobson/agents · MIT Download the skill Quick start
- 1Download the skill. Download the ZIP file for vector-index-tuning from our page.
- 2Unzip the folder. Unzip the file so you have a folder containing SKILL.md and references/details.md.
- 3Place it where the agent can find it. Move the folder to ~/.claude/skills/ for personal use, or to the project's .claude/skills/ folder for one project.
- 4Or upload it in the Claude apps. If you use the Claude apps, go to Settings → Capabilities → Skills and upload the skill there.
- 5Describe your indexing problem. Ask in plain terms, including your number of vectors, dimensions, latency target and memory limit.
- 6Ask for templates when needed. Request concrete examples, and the agent can read the worked templates in references/details.md.
- 7Test with your own data. Run any suggested settings against real queries and measure recall and latency before changing production.
What does the Vector Index Tuning skill make the agent do?
Vector search finds items by meaning, using lists of numbers called embeddings. How fast and how accurately it works depends heavily on how the index is built. This skill teaches the agent the trade-offs involved, so it can give you guidance instead of generic advice.
With the skill installed, the agent works from a guide that covers choosing an index type for your data size, setting HNSW parameters (M, efConstruction and efSearch), and picking quantization methods that shrink memory use. When you ask for concrete examples, it can point to a reference file with worked templates.
- Index type selection by data size: Flat, HNSW, HNSW with quantization, or IVF + PQ or DiskANN
- HNSW parameters M, efConstruction and efSearch, with their default values and what each one trades off
- Quantization options: FP32, FP16, INT8 scalar, product quantization and binary, with approximate storage per vector
- Best-practice checklists on benchmarking, recall monitoring, build time and reindexing
How do you install the Vector Index Tuning skill?
Download the ZIP file for the skill from our page, then unzip it. The skill folder contains SKILL.md and references/details.md. Put that folder in one of two places, depending on how you want to use it.
For a personal install that works across your projects, place it in ~/.claude/skills/. To limit it to one project, place it in that project's .claude/skills/ folder. If you use the Claude apps instead, you can upload the skill under Settings → Capabilities → Skills.
- Personal: ~/.claude/skills/
- Single project: the project's .claude/skills/ folder
- Claude apps: Settings → Capabilities → Skills, then upload the skill
How do you trigger the skill with a natural request?
You do not need a special command. The skill's description tells the agent when to use it: tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure. Describe your problem in those terms and the agent can pick up the skill.
Give the agent as much context as you can, such as your number of vectors, embedding dimensions, latency target, and how much memory you can spend. The skill's guidance depends on data size, so this detail changes the answer.
- Which index type should I use for about 5 million vectors?
- My HNSW search is slow. Should I raise efSearch or lower it?
- How much memory will INT8 quantization save for 768-dimensional embeddings?
- Help me decide between HNSW with quantization and IVF + PQ for 200 million vectors.
What kinds of examples can you expect?
For general questions, the agent answers from the guide in SKILL.md, using the index size rules and the parameter table. For concrete code, it can read references/details.md, which contains a template library and worked examples. One template, for example, benchmarks different combinations of M, efConstruction and efSearch values against a set of ground-truth results.
Templates are starting points. You will need to run them against your own data and queries to learn what they mean for your system.
What does the skill need, and is it safe to use?
The skill lists no requirements. It does not run scripts and does not use the network, according to its listing. It is a set of instructions and reference files, so it cannot measure your real recall or latency on its own.
The example templates in the reference file use Python and the hnswlib library. If you run them yourself, they will need that environment, and you should review any code before running it against production data.
- Requires: nothing listed
- Runs scripts: no
- Uses network: no
- Licence: MIT
What tips help you get good results?
The source advises starting with the default settings and tuning only when you have a measured need. Benchmark with real queries rather than synthetic ones, because synthetic data may not reflect production. Keep measuring recall after launch, since it can degrade as your data changes.
Also plan for build time and reindexing. Index updates have a cost, and cold indexes can be slow until they are warmed.
- Start with defaults (M 16, efConstruction 100, efSearch 50) and tune only when needed
- Profile before you optimize
- Use quantization for significant memory savings
- Consider tiered storage for hot and cold data
- Plan for reindexing and warm up indexes before serving traffic
What people use it for
- Choosing between Flat, HNSW, HNSW with quantization, or IVF + PQ and DiskANN as a dataset grows from thousands to hundreds of millions of vectors
- Deciding how to change M, efConstruction or efSearch when HNSW search is too slow or recall is too low
- Estimating memory savings from FP16, INT8, product quantization or binary quantization
- Getting a benchmarking template to compare HNSW settings on your own vectors
- Reviewing a vector search setup for reindexing, warming and tiered storage practices
Frequently asked questions
How do I install the Vector Index Tuning skill?
Download the ZIP from our page and unzip it. Place the folder in ~/.claude/skills/ for personal use or in a project's .claude/skills/ folder for one project. In the Claude apps, you can instead upload it under Settings → Capabilities → Skills.
Does the vector-index-tuning skill need anything else installed?
The listing states no requirements. The skill does not run scripts or use the network. The reference file's example templates use Python and hnswlib if you want to run them yourself.
Is the Vector Index Tuning skill free?
The skill is licensed under MIT, as stated in its listing. The source does not mention prices, so check the licence text for the terms that apply.
What index should I use for my dataset size?
The skill's rule of thumb is Flat (exact search) below 10K vectors, HNSW from 10K to 1M, HNSW with quantization from 1M to 100M, and IVF + PQ or DiskANN above 100M. These are general guidelines, so test them on your own data.
Can the skill tell me my real recall and latency?
No. The advice is general rules of thumb, and the agent cannot measure your recall or latency from the skill's files alone. Use real queries and benchmarks on your own system.
How is this different from just asking an AI about HNSW?
The skill gives the agent a fixed guide covering index selection, the HNSW parameter effects, quantization sizes and best practices, plus a reference file of templates. Without it, answers depend on what the agent happens to know. The skill does not name or compare other products.
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Our verdict
This skill suits engineers running production vector search or RAG systems who want a quick, structured reference on index types, HNSW settings and quantization trade-offs. It is not a good fit for beginners who do not yet have a vector database or embeddings, or for anyone who needs exact tuning for their own system, since it offers general rules of thumb and cannot measure your results.
Download the skill