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Found 18 Skills
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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.
Use pkm for personal knowledge management with temporal awareness, quality filtering, hybrid search, and relationship tracking with LSP and MCP server integration.
FORGE Vector Memory — Diagnostic tool for the vector memory index. Operations: sync, search, status, reset. Usage: /forge-memory sync | /forge-memory search "query" | /forge-memory status | /forge-memory reset
Configure pgvector extension for vector search in Supabase - includes embedding storage, HNSW/IVFFlat indexes, hybrid search setup, and AI-optimized query patterns. Use when setting up vector search, building RAG systems, configuring semantic search, creating embedding storage, or when user mentions pgvector, vector database, embeddings, semantic search, or hybrid search.
Answers questions about the wiki knowledge base. Activates when the user asks about concepts, processes, entities, or any wiki content.