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Found 125 Skills
Model Context Protocol (MCP) server development and AI/ML integration patterns. Covers MCP server implementation, tool design, resource handling, and LLM integration best practices. Use when developing MCP servers, creating AI tools, integrating with LLMs, or when asking about MCP protocol, prompt engineering, or AI system architecture.
How to access SuprSend documentation and get support. Includes docs site, LLM-friendly doc endpoints, in-app chat, AI copilot, Slack community, and email support.
TensorLake SDK for building agentic workflows, sandboxed code execution, and document parsing/extraction. Use when the user mentions tensorlake, or asks about TensorLake APIs/docs/capabilities. Also use when the user is building AI agents or agentic applications that need serverless workflow orchestration (parallel map/reduce DAGs), sandboxed execution of LLM-generated code, or document parsing, structured extraction, and OCR from PDFs/images. Works with any LLM provider (OpenAI, Anthropic), agent framework (LangChain, CrewAI, LlamaIndex), database, or API as the infrastructure layer.
Vercel AI SDK expert guidance. Use when building AI-powered features — chat interfaces, text generation, structured output, tool calling, agents, MCP integration, streaming, embeddings, reranking, image generation, or working with any LLM provider.
Step-by-step guide to building AI agents from simple chat loops to autonomous multi-agent systems with tools, memory, and event-driven architecture
Teach AI agents how to query data warehouses accurately using ktx - an executable context layer with skills, memory, and a semantic layer
A meta-skill that establishes a 'One Brain' portable memory folder (.agent/). It persists context, user preferences, identity rules, and execution history across different AI harnesses (Claude Code, Cursor, Windsurf, OpenClaw).
Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.
Building modular, debuggable AI behaviors using behavior trees for game NPCs and agentsUse when "behavior tree, bt, npc ai, ai behavior, game ai, decision tree, blackboard, ai, behavior-trees, npc, game-ai, decision-making, agents" mentioned.
Implementing providers for Beluga AI v2 registries. Use when creating LLM, embedding, vectorstore, voice, or any other provider.
Use when "deploying ML models", "MLOps", "model serving", "feature stores", "model monitoring", or asking about "PyTorch deployment", "TensorFlow production", "RAG systems", "LLM integration", "ML infrastructure"
Create narrative lore entries that transform technical work into mythological stories. Use when generating agent memory, documenting changes as narrative, or building persistent knowledge through storytelling.