2032 个 ・ 集成工具: OpenAI · 按人气排序
Build AI phone agents with AgentPhone API. Use when the user wants to make phone calls, send/receive SMS, manage phone numbers, create voice agents, set up webhooks, or check usage — anything related to telephony, phone numbers, or voice AI.
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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
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Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.
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Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.
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Lists the hardware, software, and container runtime requirements for running NemoClaw. Use when verifying prerequisites before installation. Trigger keywords - nemoclaw prerequisites, nemoclaw supported platforms, nemoclaw hardware software, nemoclaw quickstart, install nemoclaw openclaw sandbox, nemoclaw windows wsl2 setup, nemoclaw install windows docker desktop.
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Lists all inference providers offered during NemoClaw onboarding. Use when explaining which providers are available, what the onboard wizard presents, or how inference routing works. Trigger keywords - nemoclaw inference options, nemoclaw onboarding providers, nemoclaw inference routing, switch nemoclaw inference model, change inference runtime, nemoclaw local inference, ollama nemoclaw, vllm nemoclaw, local model server, openai compatible endpoint.
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Zed Editorの特徴、ACP（Agent Client Protocol）の仕様、AI統合、パフォーマンス特性、設定方法について包括的な知識を提供します。Zed Editorに関する質問、ACP対応エージェントの統合、設定のカスタマイズが必要な場合に使用してください。
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AI image generation with OpenAI, Google, DashScope and Replicate APIs. Supports text-to-image, reference images, aspect ratios. Sequential by default; parallel generation available on request. Use when user asks to generate, create, or draw images.
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Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
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Build with OpenAI's stateless APIs - Chat Completions (GPT-5, GPT-4o), Embeddings, Images (DALL-E 3), Audio (Whisper + TTS), and Moderation. Includes Node.js SDK and fetch-based approaches for Cloudflare Workers. Use when: implementing chat completions with GPT-5/GPT-4o, streaming responses with SSE, using function calling/tools, creating structured outputs with JSON schemas, generating embeddings for RAG (text-embedding-3-small/large), generating images with DALL-E 3, editing images with GPT-Image-1, transcribing audio with Whisper, synthesizing speech with TTS (11 voices), moderating content (11 safety categories), or troubleshooting rate limits (429), invalid API keys (401), function calling failures, streaming parse errors, embeddings dimension mismatches, or token limit exceeded.
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End-to-end AI video generation - create videos from text prompts using image generation, video synthesis, voice-over, and editing. Supports OpenAI DALL-E, Replicate models, LumaAI, Runway, and FFmpeg editing.
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添加和配置第三方 API 中转站供应商到 OpenClaw。当用户需要添加新的 API 供应商、配置中转站、设置自定义模型端点时使用此技能。支持 Anthropic 兼容和 OpenAI 兼容的 API 格式。
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Codex CLI深度集成——接入OpenAI最新子Agent/并行编码能力。覆盖：Codex CLI安装配置、subagent工作流、parallel agent模式、worktree集成、OpenClaw+Codex混合调度。触发词：Codex、Codex CLI、OpenAI Codex、subagent、并行编码、云端开发、OpenAI编码代理、agentic coding
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轩辕AI编码舰队——OpenClaw作为大脑，指挥Claude Code/Cursor/Windsurf/Trae/Codex CLI集群并行开发。覆盖：ACP配置、IDE接入、权限管理、任务分发、windsurf+cursor双IDE调度、worktree隔离、舰队看板。触发词：IDE舰队、编码集群、AI IDE、多工具编排、ACP配置、Cursor舰队、Windsurf集群、Trae接入、IDE调度、并行编码、AI编码引擎、IDE编排、coding fleet
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AI基础设施实战——从GPU集群调度到模型推理部署的全栈实操指南。覆盖：昇腾/GPU统一调度、PyTorch分布式训练、vLLM推理部署、模型量化实战、LLMOps、GPU故障诊断、HuggingFace生态、MLOps流水线。触发词：AI基础设施、GPU、昇腾、训练、推理、vLLM、PyTorch分布式、模型部署、LLMOps、MLOps、集群调度、HuggingFace
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Design irresistible offer packages with real salary benchmarks, negotiation playbooks, and competitive counter-strategies. Co-designed with Siku (司库). Includes total compensation calculator, negotiation scripts, and BATNA analysis.
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Analyze chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency
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Create, update, refactor, explain, or review Microsoft Agent Framework solutions using shared guidance plus language-specific references for .NET and Python.
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Go-to-market strategy for AI products. Use when positioning AI products, handling "who is responsible when it breaks" objections, pricing variable-cost AI, choosing between copilot/agent/teammate framing, or selling autonomous tools into enterprises.
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Build agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent.
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Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
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INVOKE THIS SKILL when downloading or exporting Arize traces and spans. Covers exporting traces by ID, sessions by ID, and debugging LLM application issues using the ax CLI.
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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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INVOKE THIS SKILL for LLM-as-judge evaluation workflows on Arize: creating/updating evaluators, running evaluations on spans or experiments, tasks, trigger-run, column mapping, and continuous monitoring. Use when the user says: create an evaluator, LLM judge, hallucination/faithfulness/correctness/relevance, run eval, score my spans or experiment, ax tasks, trigger-run, trigger eval, column mapping, continuous monitoring, query filter for evals, evaluator version, or improve an evaluator prompt.
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