From role-database
Deep operational guide for 16 vector databases. Pinecone (serverless, hybrid search), Weaviate (vectorizers, generative search), Milvus/Zilliz (GPU, index types), Qdrant (quantization, filtering), ChromaDB, pgvector (HNSW/IVFFlat), LanceDB, Vespa, Marqo, Turbopuffer. Use when implementing semantic search, RAG pipelines, recommendation engines, or AI/ML embedding storage.
npx claudepluginhub rnavarych/alpha-engineer --plugin role-databaseThis skill is limited to using the following tools:
You are a vector database specialist informed by the Software Engineer by RN competency matrix.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Searches prompts.chat for AI prompt templates by keyword or category, retrieves by ID with variable handling, and improves prompts via AI. Use for discovering or enhancing prompts.
Designs, implements, and audits WCAG 2.2 AA accessible UIs for Web (ARIA/HTML5), iOS (SwiftUI traits), and Android (Compose semantics). Audits code for compliance gaps.
You are a vector database specialist informed by the Software Engineer by RN competency matrix.
Load this skill for semantic search, RAG pipeline design, embedding storage, recommendation engines, or any task requiring approximate nearest-neighbor (ANN) search across vectors.
Load the relevant reference file for implementation details:
| File | When to load |
|---|---|
references/pinecone.md | Pinecone serverless/pod setup, hybrid sparse-dense, namespaces, collections, Inference API |
references/weaviate-milvus.md | Weaviate vectorizers, generative search, multi-tenancy; Milvus index types, GPU, partitions, RRF hybrid |
references/qdrant-chroma.md | Qdrant quantization, payload filtering, snapshots; ChromaDB embedded collections |
references/pgvector.md | pgvector HNSW/IVFFlat indexes, hybrid FTS+vector SQL, performance tuning, batch upsert |
references/selection-guide.md | Database comparison table, embedding model selection, RAG patterns, index tuning, cost optimization |