using-vector-databases
using-vector-databases is a RAG & retrieval skill for AI agents, published by ancoleman in the ai-design-components repository on GitHub. Skillspace rates using-vector-databases C (77/100) and it passed the safety scan; the rating is based on static analysis only.
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
- Grade
- C
- Quality
- 77 / 100
- Safety
- Passed
- GitHub stars
- 387
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- Low-risk patterns
- License
- MIT
- Adoption
- Adopted by 3 other repositories, in 2 collections
- Scope
- Vendor-specific: built around one product. Implementation guidance for Qdrant, Pinecone, pgvector and other products
What the scanner flagged
- mentions secrets in text
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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