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Found 9 Skills
Build document Q&A with Gemini File Search - fully managed RAG with automatic chunking, embeddings, and citations. Upload 100+ file formats, query with natural language. Use when: document Q&A, searchable knowledge bases, semantic search. Troubleshoot: document immutability, storage quota (3x), chunking config, metadata limits (20 max), polling timeouts, displayName dropped (Blob uploads), grounding lost (JSON mode), tool conflicts (googleSearch + fileSearch).
Google Gemini File Search for managed RAG with 100+ file formats. Use for document Q&A, knowledge bases, or encountering immutability errors, quota issues, polling failures. Supports Gemini 3 Pro/Flash (Gemini 2.5 legacy).
Provides patterns to build Retrieval-Augmented Generation (RAG) systems for AI applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
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.
Compile a LaTeX project and run basic QA (missing refs, bib errors, broken citations), producing `latex/main.pdf` and a build report. **Trigger**: latex compile, build PDF, LaTeX errors, missing refs, 编译PDF, 引用错误. **Use when**: 已有 `latex/main.tex`（通常来自 `latex-scaffold`），需要确认可编译并输出失败原因报告。 **Skip if**: 还没有 LaTeX scaffold（先跑 `latex-scaffold`）。 **Network**: none. **Guardrail**: 编译失败也要落盘 `output/LATEX_BUILD_REPORT.md`；不做“内容改写”，只做编译/QA。
Document Q&A with RAG using Supabase pgvector store.
Use when "RAG", "retrieval augmented generation", "LangChain", "LlamaIndex", "sentence transformers", "embeddings", "document QA", "chatbot with documents", "semantic search"
QUERY LENGTH LIMIT EXCEEDED. MAX ALLOWED QUERY : 500 CHARS
Local RAG system management with RLAMA. Create semantic knowledge bases from local documents (PDF, MD, code, etc.), query them using natural language, and manage document lifecycles. This skill should be used when building local knowledge bases, searching personal documents, or performing document Q&A. Runs 100% locally with Ollama - no cloud, no data leaving your machine.