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Build AI agents with Hugging Face's SmolAgents framework. Use when creating code-executing agents, tool-calling agents, multi-agent systems, agentic RAG, text-to-SQL pipelines, web browsing agents, or any multi-step AI workflows. Covers CodeAgent, ToolCallingAgent, custom tools, MCP integration, memory management, secure code execution (E2B, Docker, Blaxel), and model configuration (HF Inference, LiteLLM, Transformers, Ollama).
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Design and build AI agents for business, research, operations, or creative work. Define essential actions and knowledge, start with a simple execution loop, and add progress tracking or separate child agents as needs emerge.
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Delegate complex work through a tree of Claude Code sub-agents. Each level uses its own working context and returns concise summaries, helping the coordinating agent manage research, verification, and synthesis without retaining every intermediate detail.
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Find prebuilt WASM agents, software components packaged in WebAssembly format, through gallery browsing and keyword search. Install agents from their configurations, or export and publish your own agents to share with the community.
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Create and manage persistent Azure AI agents from Java. Covers conversation threads that retain messages, agent execution, status checks, response retrieval, and cleanup, using preview SDK examples with guidance on authentication and cloud resource handling.
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Coordinate multiple local command-line agents through tmux sessions in an authorized workspace. This guide covers starting and stopping agents, assigning tasks, monitoring logs, and checking results, while requiring a separately installed and reviewed manager runtime.
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Install or refresh oh-my-codex for a user or a specific project. Choose installation settings, preview file changes, manage how existing instructions are updated, and check the resulting files and configuration with diagnostic commands.
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Manage multiple local command-line agents in separate tmux sessions. Start and stop agents, assign tasks, and follow their logs, with support for scheduling recurring work through cron. Requires tmux and python3.
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Investigate and fix agent behavior in TradingAgentsCN by checking active database prompt templates, prompt assembly code, and output processing. Apply targeted changes and verify them with focused tests, template checks, and workflow logs.
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Use cmux on macOS to organize visible teams of AI agents, send prompts, inspect terminal output, and monitor completion. Set up role-based teams or have several agents investigate the same problem; live operation remains unproven in the supplied material.
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Coordinate multiple coding agents through Superset in separate workspaces. Split work into bounded tasks, track dependencies and progress, send follow-ups, and collect structured results covering changed files, verification checks, blockers, and the order for combining work.
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Set up ScenarioRunner, CARLA’s scenario engine, with a branch matched to your simulator version. Install dependencies, configure Python search paths, and verify imports to address setup failures before running scenarios.
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Look up how to configure and organize Omnigent agents. Use examples to choose an executor, the agent's runtime, arrange instructions and skills, add local tools or external services, and define specialized child agents.
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Audit AGENTS.md, rules, skills, and their references for broken paths or commands. Optional independent agent reviews assess redundant instructions and practical usefulness, producing reports that identify what to retain, review, or consider trimming.
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Microsoft 365 Agents SDK for TypeScript/Node.js. Build multichannel agents for Teams/M365/Copilot Studio with AgentApplication routing, Express hosting, streaming responses, and Copilot Studio client integration. Triggers: "Microsoft 365 Agents SDK", "@microsoft/agents-hosting", "AgentApplication", "startServer", "streamingResponse", "Copilot Studio client", "@microsoft/agents-copilotstudio-client".
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Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Triggers: "PersistentAgentsClient", "persistent agents java", "agent threads java", "agent runs java", "streaming agents java".
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Use when preparing your agent for production — IAM scoping, inbound auth (JWT, SigV4), secrets management, cold start optimization, session lifecycle, rate limiting, input validation, and quota guidance. Triggers on: "production checklist", "harden agent", "production ready", "secure agent", "inbound auth", "going live", "cold start optimization", "session lifecycle", "StopRuntimeSession", "quota", "throttling", "maxVms", "rate limit", "security audit of outbound API calls", "gateway target audit for production", "restrict who can call", "lock down endpoint", "only our app can call". Not for Cedar tool-restriction policies — use agents-connect. Not for quality measurement — use agents-optimize. Not for outbound credential storage or API key wiring — use agents-connect. Not for A2A agent-to-agent auth — use agents-build. Cold start observation and diagnosis (not optimization) routes to agents-debug.
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Builds voice and chat AI agents with LiveKit Agents on LiveKit Cloud or a self-hosted server. Use when the user asks to "build a voice agent", "create a LiveKit agent", "add voice AI to my app", "implement handoffs", "structure an agent workflow", "my agent is slow / too chatty", "it says it booked but nothing was saved", "make it confirm before committing", "it keeps re-asking things the caller already said", or is writing code against the LiveKit Agents SDK. Covers architecture: designing for latency, keeping context small, splitting a monolithic agent into handoffs and tasks, and designing for voice. Also covers keeping the model in charge of meaning while code owns state, approvals, and effects. For API specifics use reading-livekit-docs. To check behavior use debugging-livekit-agents and testing-livekit-agents.
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Build AI applications with OpenAI Agents SDK - text agents, voice agents, multi-agent handoffs, tools with Zod schemas, guardrails, and streaming. Prevents 11 documented errors. Use when: building agents with tools, voice agents with WebRTC, multi-agent workflows, or troubleshooting MaxTurnsExceededError, tool call failures, reasoning defaults, JSON output leaks.
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Use when you need to generate an AGENTS.md file for a Java repository — covering project conventions, tech stack, file structure, commands, Git workflow, and contributor boundaries — through a modular, step-based interactive process that adapts to your specific project needs. This should trigger for requests such as Create AGENTS.md; Update AGENTS.md file; Add agent instructions; Generate contributor instructions for Java agents; Document repository conventions in AGENTS.md. Part of Plinth Toolkit
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Use when you need to generate a checklist document with embedded agents inventory, following the embedded template exactly and producing INVENTORY-AGENTS-JAVA.md in the project root. This should trigger for requests such as Create embedded agents inventory checklist; Generate INVENTORY-AGENTS-JAVA.md; Use @002-agents-inventory; Inventory embedded Java agent definitions; List generated agent roles for Java development. Part of Plinth Toolkit
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Agent 基建：把任意 skill 或 skill 集合目录桥接到 Claude Code（~/.claude/skills）、Codex（~/.codex/skills）、通用 Agents（~/.agents/skills，豆包 Mac App / Trae Solo / Codex 可读取）和 Grok（~/.grok/skills）。支持 dbskill 仓库内 skill，也支持外部 skill 绝对路径。Claude / Codex / 通用 Agents 使用软链，Grok 生成带 user_invocable: true 的薄 bridge。支持按 skill 名称、相对路径、绝对路径、skills 目录批量桥接，也支持拆桥和查看桥接状态。 触发方式：/dbs-bridge、/bridge、「桥接这个 skill」「桥接整个 skills 目录」「把这个 skill 接到 Claude Code、Codex、豆包和 Grok」「让多个 Agent 都能调用这个 skill」「取消桥接」「查看桥接状态」 Bridge any skill folder or directory of skill folders to Claude Code, Codex, generic Agents (~/.agents/skills), and Grok using symlinks plus Grok bridge files. Supports dbskill skills and external skill directories. Use when the user wants one or many skills callable from multiple agents, or wants to unlink/check bridge status. Trigger: /dbs-bridge, /bridge, "bridge this skill", "bridge this skills directory", "make this skill available to Claude Code, Codex, Doubao, and Grok", "unlink this skill", "check bridge status"
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Build and trace AI agents in Python with AgentScope, an open-source agent framework with a ReAct loop, tools, middleware, team pipelines and OpenTelemetry tracing. Use when building production agents that need observability, debugging an agent's model and tool calls, capping token spend per reply, or coordinating a leader with specialist agents.
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Azure.AI.Agents.Persistent (.NET) workflow skill. Use this skill when the user needs Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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