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Found 278 Skills
Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Claude, Gemini, Llama), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.
A skill that equips you with real-time, source-grounded web search and content retrieval using the Exa API—optimized for balanced relevance and speed (type="auto") and full-text extraction for downstream reasoning, RAG, and code assistance. Powering agents with fast, high-quality web search by Exa.AI.
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Evaluates RAG (Retrieval-Augmented Generation) pipeline quality across retrieval and generation stages. Measures precision, recall, MRR for retrieval; groundedness, completeness, and hallucination rate for generation. Diagnoses failure root causes and recommends chunk, retrieval, and prompt improvements. Triggers on: "audit RAG", "RAG quality", "evaluate retrieval", "hallucination detection", "retrieval precision", "why is RAG failing", "RAG diagnosis", "retrieval quality", "RAG evaluation", "chunk quality", "RAG pipeline review", "grounding check". Use this skill when diagnosing or evaluating a RAG pipeline's quality.
Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.
Enables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in notebooklm".
Use when adding multi-format RAG ingest, chunk, embed, and retrieval pipelines; pair with architect-python-uv-batch or architect-python-uv-fastapi-sqlalchemy.
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.
Expert patterns for Godot 3D physics (Jolt/PhysX), including Ragdolls, PhysicalBones, Joint3D constraints, RayCasting optimizations, and collision layers. Use for rigid body simulations, character physics, or complex interactions. Trigger keywords: RigidBody3D, PhysicalBone3D, Jolt, Ragdoll, Skeleton3D, Joint3D, PinJoint3D, HingeJoint3D, Generic6DOFJoint3D, RayCast3D, PhysicsDirectSpaceState3D.
Extract structured data from Office documents (DOCX, PPTX, XLSX, HWP, HWPX) using the Polaris AI DataInsight Doc Extract API. Use when the user wants to parse, analyze, or extract text, tables, charts, images, or shapes from document files. Invoke this skill whenever the user mentions extracting content from Word, PowerPoint, Excel, HWP, or HWPX files, wants to parse document structure, needs to convert document data for RAG pipelines, or asks about reading tables, charts, or text from Office-format documents — even if they don't explicitly mention "DataInsight" or "Polaris".
Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.
Edge-optimized RAG memory system for OpenClaw with semantic search. Automatically loads memory files, provides intelligent recall, and enhances conversations with relevant context. Perfect for Jetson and edge devices (<10MB memory).