Vector Database Expert
Guides design of embedding, vector search, chunking and RAG systems
- Repository
- RightNow-AI/openfang
- Stars
- 18,243
- Licence
- Apache-2.0
- Files
- 1 file
What it does
This skill gives the agent specialist guidance on building search systems that find content by meaning rather than exact keywords. Once installed, the agent can advise on choosing embedding models, picking distance metrics and index types (such as HNSW), splitting documents into chunks, combining vector and keyword search, and wiring up retrieval-augmented generation (RAG) so an AI model answers using your own documents. It is written as expert advice and checklists; it does not itself create or connect to a database.
Great for
- Engineers designing a semantic search or RAG feature for a product
- Data teams deciding how to chunk, embed and index a document collection
- Developers troubleshooting poor search relevance in an existing vector system
Skip it if
- People who only need to run a ready-made chatbot with no building involved, since the skill contains advice rather than tools
- Anyone expecting exact, up-to-date parameter values or vendor pricing, because the file gives general typical ranges and named models without dates or verification
Try asking
- “Help me choose an embedding model and chunk size for a library of support articles”
- “Design a hybrid search pipeline that combines keyword and vector results for our docs site”
- “Why does my RAG chatbot return irrelevant passages, and how should I add reranking?”
- “Set up an evaluation plan with precision@k and recall@k for our retrieval system”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 1 original files, the Apache-2.0 licence and an attribution note.
vector-db.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip vector-db.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip vector-db.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Give the agent your data type, scale and latency needs so its recommendations fit your situation rather than generic defaults.
- Ask it to check suggested parameter values such as HNSW settings against current library documentation, since the skill gives only typical ranges.
- Treat named embedding models as examples; benchmark them on your own queries before committing.
What's inside
- SKILL.md
Redistributed unmodified from RightNow-AI/openfang under the Apache-2.0 licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.