Search

Refresh Pinecone article embeddings after MDX article changes or RAG chunking and metadata changes. Use when content updates should be reflected in chat retrieval or when the vectorization pipeline changes.

AgentDB memory system with HNSW vector search. Use when: need to store patterns, search for solutions, semantic lookup. Skip when: no learning needed, ephemeral tasks.

Tenant-specific RAG service for company knowledge in the voice assistant platform.

Use this skill when the user asks to add a reference repo or index a GitHub repository for search. Clones, auto-detects file types, indexes with QMD, and embeds.

Creates an Ampi-ready SQLite vault from a folder of documents (md, markdown, txt, docx, doc) or an existing SQLite source table. Builds keyword search (FTS5), optional sparse semantic search, a search_schema contract, and an amplify_search_manifest so Ampi tools (search_vault_keyword, search_vault_semantic, search_vault_deep, lookup_vault_records) work immediately. Use when the user wants to build a new Ampi vault, ingest documents into a searchable SQLite database, check whether an existing vault passes the Ampi search contract, or re-build a vault from updated documents. Don't use for general SQLite database creation, non-Ampi document stores, CSV imports, querying an existing vault, or adding documents to an already-built vault without re-ingesting.

LLMs, prompt engineering, RAG systems, LangChain, and AI application development

This skill should be used when the user asks about "Docling chunking", "HybridChunker", "HierarchicalChunker", "structure-aware chunking", "Docling metadata extraction", "export modes", "DOC_CHUNKS vs MARKDOWN", "chunking strategies", or mentions preparing documents for RAG with Docling.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

RAG Architect - POWERFUL

World-class expert in prompt engineering, LLM fine-tuning, RAG systems, and AI/ML workflows. Use when crafting prompts, designing AI agents, building knowledge bases, implementing retrieval systems, or optimizing LLM performance at production scale.

Evaluate LLM systems using automated metrics, LLM-as-judge, and benchmarks. Use when testing prompt quality, validating RAG pipelines, measuring safety (hallucinations, bias), or comparing models for production deployment.

Process and transform FAQ documents (xlsx, word, pdf, txt) into RAG-optimized format. Use when working with FAQ files, knowledge base documents, or when the user needs to analyze and restructure FAQ content for RAG systems.