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Found 22 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.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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.
Semantic skill discovery and routing using GraphRAG, vector embeddings, and multi-tool search. Automatically matches user intent to the most relevant skills from 144+ available options using ck semantic search, LEANN RAG, and knowledge graph relationships. Triggers on /meta queries, complex multi-domain tasks, explicit skill requests, or when task complexity exceeds threshold (files>20, domains>2, complexity>=0.7).
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
Design and operate an advanced AI agent memory system on HelixDB using hybrid graph + vector + BM25 search. Use when building long-term memory, user profiles, document/chunk RAG, recall/remember features, memory extraction, deduplication, consolidation, versioning, updating, forgetting/deletion, categorisation, or connector-backed ingestion. Covers tenant-safe Helix data modeling, modality decision rules, the full write/maintain lifecycle, and the product layers an agent must implement around Helix. TypeScript-first (@helix-db/helix-db); a Rust DSL variant is in EXAMPLES.rust.md.
Use pkm for personal knowledge management with temporal awareness, quality filtering, hybrid search, and relationship tracking with LSP and MCP server integration.
Answers questions about the wiki knowledge base. Activates when the user asks about concepts, processes, entities, or any wiki content.
Add persistent memory to AI coding agents using agentmemory - remembers context, preferences, and decisions across sessions
Redis vector search guidance covering HNSW vs FLAT algorithm choice, vector index configuration (dims, distance metric, datatype), filtered hybrid search combining vector similarity with TAG or NUMERIC filters, and the RAG retrieval pattern with RedisVL. Use when defining a VECTOR field in FT.CREATE, integrating embeddings (OpenAI, Cohere, sentence-transformers), tuning HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.