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Found 85 Skills
Guide for building MCP (Model Context Protocol) servers that integrate external APIs/services with LLMs. Covers Python (FastMCP) and TypeScript (MCP SDK) implementations.
Build production-ready MCP clients in TypeScript or Python. Handles connection lifecycle, transport abstraction, tool orchestration, security, and error handling. Use for integrating LLM applications with MCP servers.
Implementing providers for Beluga AI v2 registries. Use when creating LLM, embedding, vectorstore, voice, or any other provider.
Use when "writing prompts", "prompt optimization", "few-shot learning", "chain of thought", or asking about "RAG systems", "agent workflows", "LLM integration", "prompt templates"
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.
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
Register and implement PydanticAI tools with proper context handling, type annotations, and docstrings. Use when adding tool capabilities to agents, implementing function calling, or creating agent actions.
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems.
Build on-device AI into React Native apps using ExecuTorch. Provides hooks for LLMs, computer vision, OCR, audio processing, and embeddings without cloud dependencies. Use when building AI features into mobile apps - AI chatbots, image recognition, speech processing, or text search.
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"