Total 51,031 skills, AI & Machine Learning has 8547 skills
Showing 12 of 8547 skills
Convert text to natural speech using Sarvam AI's Bulbul v3 model. Handles audio generation, voiceovers, and voice interfaces for 11 Indian languages with 30+ voices. Supports REST, HTTP streaming, WebSocket, and pronunciation dictionaries. Use when generating spoken audio from text.
Build AI-powered chat applications with TanStack AI and React. Use when working with @tanstack/ai, @tanstack/ai-react, @tanstack/ai-client, or any TanStack AI packages. Covers useChat hook, streaming, tools (server/client/hybrid), tool approval, structured outputs, multimodal content, adapters (OpenAI, Anthropic, Gemini, Ollama, Grok), agentic cycles, devtools, and type safety patterns. Triggers on AI chat UI, function calling, LLM integration, or streaming response tasks using TanStack AI.
Generate images, videos, audio, and 3D models via RunningHub API (170+ endpoints) and run any RunningHub AI Application (custom ComfyUI workflow) by webappId. Covers text-to-image, image-to-video, text-to-speech, music generation, 3D modeling, image upscaling, AI apps, and more.
Manage AI agent memory files (AGENTS.md/CLAUDE.md). Supports update and restructure modes. Use this when users need to sync, update, or restructure agent memory files. Triggered by keywords such as "记忆文件", "memory file", "AGENTS.md", "更新记忆", "重构记忆", "memory sync", "memory restructure".
This skill should be used when the user asks to "build an MCP server", "create an MCP tool", "expose resources with MCP", "write an MCP client", or needs guidance on the Model Context Protocol Python SDK best practices, transports, server primitives, or LLM context integration.
Instrument Python LLM apps, build golden datasets, write eval-based tests, run them, and root-cause failures — covering the full eval-driven development cycle. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM, even if they don't say "evals" explicitly. Use for making sure an AI app works correctly, catching regressions after prompt changes, debugging why an agent started behaving differently, or validating output quality before shipping.
Clarify ambiguous or conflicting requests by researching first, then asking only judgment calls. Use when prompts say "$grill-me"/"grill me", ask hard questions, request relentless interrogation, pressure-test assumptions, clarify scope/requirements, define success criteria, or request system-design/optimization decisions before implementation; stop before implementation.
Evaluate and rank agent results by metric or LLM judge for an AgentHub session.
Use this skill when working with the A2A (Agent-to-Agent) protocol - agent interoperability, multi-agent communication, agent discovery, agent cards, task lifecycle, streaming, and push notifications. Triggers on any A2A-related task including implementing A2A servers/clients, building agent cards, sending messages between agents, managing tasks, and configuring push notification webhooks.
Semantic search, context management, and document indexing via OpenViking. Use when the user asks to: index/import documents or files into a knowledge base, perform semantic search across indexed content, browse or explore indexed resources, get summaries/overviews of indexed documents, manage an OpenViking instance, or integrate structured context retrieval into workflows. Also use when sub-agents need to retrieve relevant context from a large document collection.
Use when analyzing patient records, clinical notes, medical PDFs, FHIR data, or advising on how to present medical data in health-tech products — OCR interpretation, clinical summarization, differential diagnosis support, drug interaction flags
Support skill providing the workflow, templates, and references for AI coding assistant rule authoring. Invoked by fusion-rules gateway agents — not intended for direct use.