「crew」の検索結果

Connect this agent to a running Guaardvark (self-hosted AI studio) and check what it can do right now. Use first when the user mentions Guaardvark, asks to generate images/video/music/voice locally, run a Film Crew, train a LoRA, launch a swarm, or when a guaardvark MCP tool is missing or a call fails with "not reachable" / "plugin offline".

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。

Build consistent characters, environments and props in Guaardvark's Cast Library and train LoRAs for them locally (reference photos → vision bible → sample plan → approved samples → training). Use when the user wants the same face or object across images, videos, a music video, or a Film Crew production, or asks to "train a LoRA".

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。

Use this skill to plan, direct, troubleshoot, and hand off provider-independent live or hybrid livestream event productions, including run of show, crew, capture, audio, graphics, switching, guests, encoding, ingest, transport choices, latency, redundancy, accessibility, moderation, monitoring, incidents, recording, and delivery.

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。

E2B is a cloud platform that gives AI agents secure, isolated sandboxes — ephemeral Linux VMs with a filesystem, package managers, and internet access — to run AI-generated code safely. Use when a user wants an agent to execute Python or JavaScript, install packages, read/write files, or run a long process without risking the host machine, or wants to wire code execution into a LangChain, CrewAI, or custom agent loop.

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。

AG-UI (Agent-User Interaction Protocol) is an open, event-based protocol that streams an AI agent's text, tool calls, state and lifecycle events to a frontend over HTTP and Server-Sent Events. Use when the user wants to build an AG-UI server, connect an agent or framework (LangGraph, Mastra, CrewAI, Pydantic AI) to a React or custom UI, stream agent progress and state, or use CopilotKit with AG-UI.

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 workflow skill. Use this skill when the user needs AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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 workflow skill. Use this skill when the user needs AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.

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。

Provides best practices for AI agent orchestration including MCP servers, A2A protocol, multi-agent coordination, and swarm architectures. Use when designing agent systems, configuring MCP servers, setting up agent teams, or when user mentions 'MCP', 'A2A', 'agent orchestration', 'multi-agent', 'swarm', 'agent team', 'LangGraph', 'CrewAI', 'AutoGen'.

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。

Bridge layer connecting AlgebraicJulia/Catlab.jl to plurigrid/asi. Wires ACSets (attributed C-sets), wiring diagrams, decorated cospans, and the AlgebraicJulia ecosystem (AlgebraicDynamics, AlgebraicPetri, AlgebraicRewriting, Decapodes) into the ASI skill graph. ACSets generalize relational databases with categorical semantics; every diagram, network, and model in the ecosystem is an ACSet.

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。

Generate cinematic case study pages for films, documentaries, photography portfolios, and creative projects — film-frame hero images, structured crew credit grids, dark footage galleries, festival award sections, scroll-choreographed reveals via data-attribute animation orchestration. Warm cream palette with analog typography. Single-file HTML, zero build step. Triggers on film case study, documentary showcase, filmmaker portfolio, production company website, film credits page, creative project case study, director portfolio, photography case study.

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。

Build and maintain a library collection through acquisitions, weeding (deaccessioning), collection assessment, reader advisory, and interlibrary loan coordination. Covers selection criteria, collection development policies, the CREW/MUSTIE method for weeding, usage analysis, and responsive collection management. Use when building a new collection with a defined scope and budget, assessing an existing collection for gaps or outdated materials, when shelves are overcrowded and systematic weeding is needed, or when establishing a formal collection development policy.

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。

Use when a task needs the judgment of an Energy Auditor — running a blower-door/duct-leakage diagnostic sequence on a home or light-commercial building, testing combustion appliances for worst-case backdraft risk before recommending air sealing, reconciling utility billing history against walkthrough findings, ranking retrofit measures by savings-to-investment ratio (SIR) for a weatherization or utility rebate program, or writing the prioritized scope of work an install crew executes without the auditor on site. Distinct from an energy-engineer (industrial/commercial capital planning, IPMVP measurement and verification, calibrated simulation) — this role owns the field diagnostics and the residential/light-commercial audit-to-scope handoff.

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。

Use when a task needs the judgment of a First-Line Supervisor of Entertainment and Recreation Workers (camp, pool, ride crew, attractions, or activities floor) — building today's shift roster against certification/ratio requirements when call-outs hit, running a pre-shift briefing, triaging an escalated guest complaint, deciding whether a new hire is ready to sign off independent, or choosing when to consolidate or close a program area for the day.

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。

Coordinate teams of role-based agents — role definitions, task delegation, sequential and hierarchical workflows, and shared memory. Use when a job naturally splits into specialist roles collaborating.

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.

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.

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。

Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).

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。

Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

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。

Coordinate multi-agent work through durable artifacts in git instead of chat. Use this whenever a task involves sending a message to another agent, agent mailboxes or handoffs, claiming work from a shared queue, notifying another agent or crew (OpenClaw, Grok Bot, Claude Code, Codex, Gas Town/Gas City workers) that work is ready, or whenever the user mentions the artifact bus, message bus, agent mail, beads mail, doorbells, or pings between agents — even if they don't say "bus" explicitly.

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。

Turns an ICP or research goal into a plan for finding and scraping suitable directories. Use when the user has a target audience but is unsure where to find them, and wants source categories, queries, and an extraction workflow.

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。

Converts a job description into a structured candidate-sourcing workflow. Use when the user provides a role and wants candidate criteria, search terms, source recommendations, and a screening process. Must avoid sourcing on sensitive personal attributes.

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。

Divides records into geographic or account-ownership territories. Use when the user provides records with location or account data and wants consistent territory assignments with unresolved locations flagged.

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。

Assesses potential suppliers against defined commercial, operational, and compliance requirements. Use when the user provides supplier records plus sourcing requirements and wants a consistent shortlist with strengths and risks.

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。