Total 44,222 skills, AI & Machine Learning has 7033 skills
Showing 12 of 7033 skills
CopilotKit integration patterns for providers, runtime wiring, `useCoAgent`, `useCopilotAction`, `useLangGraphInterrupt`, shared state, and HITL with LangGraph. Use when building agent-native product UX.
List all Langfuse models with their pricing. Use when checking model costs, verifying pricing configuration, or getting an overview of model definitions.
Create an AI Evals Pack (eval PRD, test set, rubric, judge plan, results + iteration loop). Use for LLM evaluation, benchmarks, rubrics, error analysis/open coding, and ship/no-ship quality gates for AI features.
调用扣子(Coze)智能体 API 进行对话、工作流执行等操作。当用户需要集成 Coze 智能体、调用 Coze API、或开发 Coze 相关应用时使用。支持流式和非流式对话、工作流调用等功能。
Master Anthropic's prompt engineering techniques to generate new prompts or improve existing ones using best practices for Claude AI models.
Share and distribute skill knowledge and documentation. Publishes capabilities with examples, documentation, and integration guides.
Use this skill when building AI applications with OpenAI Agents SDK for JavaScript/TypeScript. The skill covers both text-based agents and realtime voice agents, including multi-agent workflows (handoffs), tools with Zod schemas, input/output guardrails, structured outputs, streaming, human-in-the-loop patterns, and framework integrations for Cloudflare Workers, Next.js, and React. It prevents 9+ common errors including Zod schema type errors, MCP tracing failures, infinite loops, tool call failures, and schema mismatches. The skill includes comprehensive templates for all agent types, error handling patterns, and debugging strategies. Keywords: OpenAI Agents SDK, @openai/agents, @openai/agents-realtime, openai agents javascript, openai agents typescript, text agents, voice agents, realtime agents, multi-agent workflows, agent handoffs, agent tools, zod schemas agents, structured outputs agents, agent streaming, agent guardrails, input guardrails, output guardrails, human-in-the-loop, cloudflare workers agents, nextjs openai agents, react openai agents, hono agents, agent debugging, Zod schema type error, MCP tracing failure, agent infinite loop, tool call failures, schema mismatch agents
The industry standard library for machine learning in Python. Provides simple and efficient tools for predictive data analysis, covering classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
Expert in primary school mathematics test question imitation, capable of analyzing the knowledge domains, core concepts, mathematical thinking methods, difficulty levels and cognitive requirements of primary school mathematics questions, and generating equivalent alternative test questions and various variant questions
Create personalized workout plans and provide exercise guidance
LangChain workflows for `create_agent`, LCEL chains, `bind_tools`, middleware, and structured output with production-safe orchestration. Use when implementing or refactoring LangChain application logic in Python or TypeScript.
Store a learning, pattern, or decision in the memory system for future recall