Menu

Individual skill

vector-db-pro

Vector database guidance — embeddings, indexing (HNSW/IVF), hybrid search, metadata filtering, and production ops.

skills/vector-db-pro/SKILL.md

Read pinned source

Source commit faf37c0859ebf627bfcb5225e8057d088691e0ae

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Instructions only
Last checked
Nov 8, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
53
Repository last updated
Oct 10, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

## Overview

Vector databases store embeddings and answer "what's most similar?" at scale — the retrieval backbone of RAG, semantic search, and recommendation. The core is approximate nearest neighbor (ANN) search: trading a little recall for orders-of-magnitude speed over brute force.

This skill covers choosing and operating vector stores: embedding strategy, index types (HNSW, IVF), hybrid search, metadata filtering, and the production concerns (updates, scaling, evaluation) that matter beyond the demo.

## When to use

- Choosing a vector database (Pinecone, Qdrant, Weaviate, pgvector, Milvus).
- Tuning ANN indexes (recall vs latency vs memory).
- Adding hybrid search (dense + keyword).
- Filtering by metadata alongside similarity.
- Operating vector DBs in production (updates, scaling, backups).
- Deciding pgvector vs a dedicated vector DB.