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Found 106 Skills
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Build semantic search with Cloudflare Vectorize V2. Covers async mutations, 5M vectors/index, 31ms latency, returnMetadata enum changes, and V1 deprecation. Prevents 14 errors including dimension mismatches, TypeScript types, testing setup. Use when: building RAG or semantic search, troubleshooting returnMetadata, V2 timing, metadata index, dimension errors, vitest setup, or wrangler --json output.
Tips and best practices for effective GrepAI searches. Use this skill to improve search result quality.
Search ClinicalTrials.gov with natural language queries. Find clinical trials, enrollment, and outcomes using Valyu semantic search.
Universal ChromaDB integration patterns for semantic search, persistent storage, and pattern matching across all agent types. Use when agents need to store/search large datasets, build knowledge bases, perform semantic analysis, or maintain persistent memory across sessions.
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
Persistent knowledge storage using basic-memory CLI. Use to save notes, search memories semantically, and build context for topics across sessions.
Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.
CLI for Limitless.ai Pendant with lifelog management, FalkorDBLite semantic graph, vector embeddings, and DAG pipelines. Use for personal memory queries, semantic search across lifelogs/chats/persons/topics, entity extraction, and knowledge graph operations. Triggers include "lifelog", "pendant", "limitless", "personal memory", "semantic search", "graph query", "extraction".
Use this at session start to discover what CodeCompass can do. Read .ai/capabilities.json for module map (5 domains, 21+ modules) instead of manual Grep/Glob. Apply when: (1) planning tasks, (2) user asks 'What can CodeCompass do?', (3) before implementing features
MemPalace — mine projects and conversations into a searchable memory palace. Use when asked about mempalace, memory palace, mining memories, searching memories, or palace setup.
Relevance AI integration. Manage Organizations, Users. Use when the user wants to interact with Relevance AI data.