「crew」の検索結果

Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

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CrewAI multi-agent orchestration setup for collaborative AI systems

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Case studies of American screenwriting (美国电影剧作案例) — Mei Feng's readings of the Hollywood Western (value pairs, four oppositions), screwball comedy (strong heroine, buddy vs adversary two-hander), Billy Wilder (disguise, Lubitsch touch, meet-cute), Hitchcock and his writers, Adaptation, Mildred Pierce (eight drafts, woman's film + noir), Thelma & Louise ("but" method, mirror pair), Fargo (Coen method), Good Will Hunting (five-part structure), American Beauty (POV strategy, genre mix), Eternal Sunshine (1:5:1, image motifs); Walter's American Graffiti; the Hollywood masterclass films (Bourne, Guardians, Zootopia, Thor); Hoxter's Winter's Bone and Jojo Rabbit; Snyder's Miss Congeniality; Field's Chinatown. Use when you want a worked example of how a specific American film's script is built, when analysing a film for structure, character or theme, or when looking for a model to imitate for a Western, screwball, noir, buddy, coming-of-age, black comedy or non-linear romance.

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Enhancement overlay for multi-agent / tool-using coder agents. Encodes the per-agent tool- scoping discipline that role-based frameworks (CrewAI, LangChain) document only as a passing best-practice: which agent gets which tool, and why blanket-sharing every tool to every agent is a correctness and blast-radius risk. Activates when an agent system has tools AND there is more than one agent (or one agent holding many tools). Treat a tool as a capability grant; scope by least-privilege. ENHANCE overlay — read alongside [[crewai]], [[agentsop-http-tool-wrapping]], [[agentsop-llm-tool-idempotency]]. Search keywords: which tools per agent, least-privilege agent, agent tool access, tool permissions, limit agent tools, scope tools to roles.overlay_type: enhancement

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SOP for building multi-agent systems with CrewAI — role-based collaboration, sequential/hierarchical processes, Flows, memory, delegation. Use when modeling agent teams with clear roles and task pipelines.

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Schedule labor crews based on CWICR norms and project timeline. Calculate crew sizes, shifts, and labor loading curves.

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Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

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Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when "build agent, AI agent, autonomous agent, tool use, function calling, multi-agent, agent memory, agent planning, langchain agent, crewai, autogen, claude agent sdk, ai-agents, langchain, autogen, crewai, tool-use, function-calling, autonomous, llm, orchestration" mentioned.

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Makers 开发专家团 AI Agent 工程师 Skill — Claude Agent SDK、 OpenAI Agents SDK、LangGraph、DeepAgents、CrewAI 框架集成， SSE 流式响应、conversation store、EdgeOne Makers Agent Runtime。 触发词：AI Agent、聊天机器人、LLM 应用、多 Agent 系统、 Claude SDK、OpenAI Agents、LangGraph、CrewAI、SSE 流式、 智能助手、AI 推理端点。

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Framework de orquestração multi-agent para colaboração autônoma de IA. Use ao construir equipes de agentes especializados trabalhando juntos em tarefas complexas, quando você precisa de colaboração de agentes baseada em papéis com memória, ou para workflows em produção exigindo execução sequencial/hierárquica. Construído sem dependências de LangChain para execução enxuta e rápida.

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AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

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Expert en orchestration d'agents autonomes (CrewAI, AutoGen, LangGraph, swarm patterns, planning)

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Autonomous Agents Specialist IA — Expert en agents autonomes (AutoGPT, CrewAI, LangGraph, planification multi-étapes, tool use)

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Agent Orchestrator IA — Expert en orchestration multi-agent (CrewAI, AutoGen, LangGraph, task decomposition, coordination)

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Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

日本語の概要は準備中です。原文の説明を表示しています。

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

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Mistake-proof code so misuse cannot be expressed, rather than warning against it. Use when designing an interface, schema, or state machine and the user wants it hard to get wrong ("make invalid states unrepresentable", "so callers cannot screw it up", "type-safe API", "pit of success"); when auditing existing code for footguns ("what could bite us here", "what is easy to misuse", "poka-yoke this repo", "review this diff for ways to get it wrong"); or when a bug has recurred and the fix must close the class rather than the case ("make sure this never happens again", "this is the third time"). Especially for money, auth, permissions, deletion, migrations, and pipelines where failure is silent. Classifies every finding by what happens when the mistake occurs and how the device notices, which is what keeps it from collapsing into generic code review.

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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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Use for any CopilotKit question — adding it to an app, chat UI, frontend or server tools, generative UI, shared state, human-in-the-loop, agent frameworks (LangGraph, CrewAI, Mastra, ADK, PydanticAI, and others), the runtime, Intelligence, threads, voice, or diagnosing something that is not working. Do not answer from memory: this skill exists to point you at the current documentation and source, both of which you can read.

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Runs the payroll-confidence chain end to end — forecasts cash across the payroll window with cash-flow-snapshot, ranks and drafts overdue-invoice reminders with invoice-chase to close any gap, then assembles timesheets, flags every anomaly, and stages the run with payroll-prep. On Gusto the run is staged for the owner to submit; on QuickBooks Payroll, or with no payroll system, the last step is a validated run sheet the owner keys in. This chain never presses the payroll button. Builds on the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), with Gmail or M365, PayPal, Square, Stripe, and either Gusto or QuickBooks Payroll adding detail, and falls back to CSV and timesheet uploads. Use it whenever payroll is the worry, including "can I make payroll," "payroll is due Friday," "plan payroll," "will I have enough to pay the crew," or "I need to run payroll but money is tight." Accepts optional horizon and payroll-date arguments.

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Gets payroll ready to run without anyone getting shorted: pulls timesheets from Gusto, QuickBooks Payroll, or an uploaded spreadsheet, totals regular, overtime, and PTO hours by person, flags every anomaly it finds — missed punches, overtime spikes, rate changes, mid-period hires and terminations, hours off the schedule — and stages the run for the owner to approve line by line before a dollar moves. Anomalies are always raised, never quietly corrected. Once the owner runs it, the journal posts to the ledger or goes to the bookkeeper. Reach for this whenever payroll comes up at all — "run payroll," "payroll is due," "check the timecards," "did everyone clock out," "how many hours did the crew put in," "why is payroll so high this week," "I need to pay the guys Friday" — and use it after cash-flow-snapshot when the owner is worried about whether payroll clears.

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Find, evaluate, and download common purchasable CAD parts from step.parts, including named off-the-shelf actuators, servos, motors, electronics boards, connectors, screws, bolts, nuts, washers, bearings, standoffs, and other catalog components. Use when Codex needs to search the hosted step.parts catalog before creating simplified placeholder geometry, resolve fuzzy part names, standards, aliases, or dimensions, choose a matching part, fetch a canonical .step file, verify checksums, or use the step.parts API/OpenAPI/catalog endpoints for standard part discovery.

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Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents, LlamaIndex, Pydantic AI, CrewAI, and others); choose between a native integration and manual instrumentation; set span types, tags, or metadata; or send DeepEval-SDK traces to Confident AI's Observatory. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill), or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill). This skill is purely DeepEval-SDK instrumentation — producing well-formed traces, not running evals.

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