How to use the Vector Database Expert agent skill

What is an agent skill, and what does vector-db make the agent do?

An agent skill is a folder containing a SKILL.md file with instructions that an AI agent loads when a task matches the skill. This one is a retrieval systems specialist. Its file covers embedding models, vector indexing, chunking, hybrid search, metadata filtering and RAG pipeline design.

Once installed, the agent can advise you on choosing an embedding model for your domain, picking a distance metric (cosine similarity, dot product or L2), selecting an index type such as HNSW, IVF or flat, splitting documents into chunks, combining vector and keyword search, and laying out a RAG pipeline from query embedding through to LLM generation.

Install the vector-db skill

You can install the skill in either of two ways. For Claude Code, download the ZIP from the skill's page on this site and unzip it into ~/.claude/skills/ to make it available everywhere, or into a project's .claude/skills/ folder to make it available only in that project.

In the Claude apps, you can instead upload the ZIP under Settings → Capabilities → Skills.

• Personal install: unzip into ~/.claude/skills/
• Project install: unzip into the project's .claude/skills/ folder
• Claude apps: Settings → Capabilities → Skills, then upload the ZIP

Trigger the skill with natural requests

You do not need a special command. Describe the problem you are solving in ordinary language and mention search, embeddings, chunking, indexing or RAG. The skill's description is about vector databases, embeddings, similarity search, RAG patterns and indexing strategies, so requests on those topics are the ones it is built for.

Give the agent context: your document types, approximate collection size, languages, and what a good answer looks like. Advice is more specific when the agent knows these details.

• Which embedding model should I use for searching our support tickets, which are mostly in German?
• Should I use cosine similarity or dot product with my embeddings?
• How big should my chunks be for 200-page technical manuals?
• My semantic search returns vaguely related results. How do I improve relevance?
• Design a RAG pipeline that cites sources for answers from our policy documents.

What examples of help does the skill give?

The skill's guidance is a set of principles, techniques, common patterns and pitfalls. For chunking it describes fixed-size chunks with overlap (for example 512 tokens with a 50-token overlap), semantic chunking at paragraph or section boundaries, and recursive splitting that respects document structure. For HNSW it names M (typically 16-64) and efConstruction (typically 100-200) and explains that higher values usually improve recall at the cost of memory and build time.

Common patterns include parent-child retrieval, where small chunks are matched but the larger parent section is sent to the LLM; multi-vector representation, where titles, summaries and full text are each embedded; and contextual retrieval, where a document summary is added to each chunk before embedding. For evaluation it recommends precision@k, recall@k and NDCG on a labelled relevance dataset.

• Parent-child retrieval: match small chunks, return the parent section as context
• Multi-vector representation: embed title, summary and full text separately
• Contextual retrieval: prepend a document summary to each chunk before embedding
• Evaluation: measure precision@k, recall@k and NDCG on labelled queries

What do you need before using it?

The skill lists no requirements. It does not run scripts and does not use the network, so it is advice and checklists only. It does not create, configure or connect to a vector database for you. You will need your own embedding provider or model, your own vector store and your own documents to act on the advice.

Safety notes: scripts and network access

According to the skill's listing, it runs no scripts and uses no network access. The only file in the package is SKILL.md. Because it contains guidance, the main risk is acting on general advice without checking it against your own data, so benchmark embedding models and chunk sizes on a labelled sample before committing.

• Runs scripts: no
• Uses network: no
• Files: SKILL.md only

Tips for getting useful answers

The skill itself warns that parameter ranges are general. Treat numbers such as M or chunk size as starting points and test them. Ask the agent how to measure the result, not only what to set.

Keep the original text and metadata alongside vectors, since the skill notes you need them for context assembly, debugging and auditing. Ask about preprocessing too: removing boilerplate, normalising whitespace and handling tables and code blocks as structured content.

• Ask for a way to measure relevance, not just a setting
• Describe your data: languages, document length, update frequency
• Test suggested chunk sizes and HNSW values on your own queries
• Keep source text and metadata with every vector