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Found 108 Skills
AI session compression techniques for managing multi-turn conversations efficiently through summarization, embedding-based retrieval, and intelligent context management.
Build Retrieval-Augmented Generation (RAG) applications that combine LLM capabilities with external knowledge sources. Covers vector databases, embeddings, retrieval strategies, and response generation. Use when building document Q&A systems, knowledge base applications, enterprise search, or combining LLMs with custom data.
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
Vercel AI SDK 5 patterns. Trigger: When building AI features with AI SDK v5 (chat, streaming, tools/function calling, UIMessage parts), including migration from v4.
Vercel AI SDK 5 patterns. Trigger: When building AI chat features - breaking changes from v4.
Use when adding multi-format RAG ingest, chunk, embed, and retrieval pipelines; pair with architect-python-uv-batch or architect-python-uv-fastapi-sqlalchemy.
Build chat interfaces for querying documents using natural language. Extract information from PDFs, GitHub repositories, emails, and other sources. Use when creating interactive document Q&A systems, knowledge base chatbots, email search interfaces, or document exploration tools.
AI/ML APIs, LLM integration, and intelligent application patterns
Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads.
Principal AI Architect and Machine Learning Engineer.
Ollama local LLM deployment and management. Use for running LLMs locally.
Comprehensive quality audit for Claude Code agents, skills, and commands with comparative analysis