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Found 334 Skills
Periodic cross-repo reflection analyzing 30 days of git history, extracting patterns via RAGS loop, and auto-creating skills
Подробная русскоязычная справка по Open WebUI: архитектура, авторизация, функции, пайплайны, API, RAG, масштабирование, отладка и скрытые возможности. Используй этот скилл при любых вопросах об Open WebUI — как он устроен, как развернуть, настроить авторизацию (OAuth, LDAP, JWT), написать функцию или пайплайн, подключить модель (Ollama, OpenAI), настроить RAG/knowledge base, масштабировать на production, отладить проблему. Также используй при написании кода для Open WebUI: функции (filter, pipe, action), пайплайны, конфигурации, docker-compose.
Cluely platform help — real-time AI meeting assistant with live coaching overlay, pre-call briefs, meeting notes, conversation analytics, and knowledge base RAG. Use when setting up Cluely for live AI prompts during sales calls, configuring the knowledge base with company docs for real-time RAG retrieval, connecting Cluely to HubSpot or Salesforce via Merge.dev, troubleshooting transcription accuracy or speaker attribution errors, comparing Cluely Pro vs Pro + Undetectability plans, or setting up team coaching scorecards and missed opportunity tracking. Do NOT use for choosing between AI note-takers across vendors (use /sales-note-taker) or reviewing a call for coaching (use /sales-call-review).
Google Gemini embeddings API (gemini-embedding-001) for RAG and semantic search. Use for vector search, Vectorize integration, or encountering dimension mismatches, rate limits, text truncation.
Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eval) or for deploying / repairing services (use rag-blueprint).
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents 13 documented errors. Use when: vector search, RAG systems, semantic search, document clustering. Troubleshoot: dimension mismatch, normalization required, batch ordering bug, memory limits, wrong task type, rate limits (100 RPM).
Extract text from PDFs for LLM consumption. Use when processing PDFs for RAG, document analysis, or text extraction. Supports API services (Mistral OCR) and local tools (PyMuPDF, pdfplumber). Handles text-based PDFs, tables, and scanned documents with OCR.
Complete RAG and search engineering skill. Covers chunking strategies, hybrid retrieval (BM25 + vector), cross-encoder reranking, query rewriting, ranking pipelines, nDCG/MRR evaluation, and production search systems. Modern patterns for retrieval-augmented generation and semantic search.
Expert prompt engineering for LLM applications including prompt design, optimization, RAG systems, agent architectures, and AI product development.
Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
Expert guidance for LlamaIndex development including RAG applications, vector stores, document processing, query engines, and building production AI applications.
Build AI agents with Cloudflare Agents SDK on Workers + Durable Objects. Includes critical guidance on choosing between Agents SDK (infrastructure/state) vs AI SDK (simpler flows). Use when: deciding SDK choice, building WebSocket agents with state, RAG with Vectorize, MCP servers, multi-agent orchestration, or troubleshooting "Agent class must extend", "new_sqlite_classes", binding errors.