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Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types, including the Agent Script language for the new Agentforce Builder (hybrid-reasoning -> logic vs | prompt instructions, @actions/@subagent/@variables references, subagents). Use when creating agents in source control, writing or debugging Agent Script, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'write or debug Agent Script', 'how do I deploy an Agentforce agent using source control', 'convert an agent topic to a subagent', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship', 'Agent Script logic vs prompt instructions'. NOT for Apex-based agent actions — use agentforce/custom-agent-actions-apex. NOT for clicking the agent together in the Builder UI — use agentforce/agentforce-agent-creation.

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Run work as an explicit event-driven state machine executed by subagents. The orchestrator is the runtime from the canonical loop — `state, effects = init(machine); execute(effects); while state is active: event = nextEvent(); state, effects = transition(machine, state, event); execute(effects); return state.output`. All routing judgment is authored ONCE, at plan time, into a machine file (states, event alphabet, transition table, budgets, final states); at runtime the orchestrator does pure table lookups and dispatches effects — it never decides, never authors, never ingests artifacts. Subagent/sync protocol is the timeboxed-iterating one: role prompts scaffolded once to disk and dispatched BY PATH, an initialiser preamble every subagent reads first, a self-populating cheatsheet, per-unit files, tiny structured returns, independent measurement gate. Use when the user says "workmachine", "run this as a state machine", "state-machine this", or when a goal has a small number of clearly named phases with explicit outcomes (built / passed / failed / blocked) and you want the routing to be auditable rather than judged on the fly. For clock-driven loops use timeboxed-iterating; for wide graphs with judged edge criteria use workgraph.

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Create subagent definitions for Claude Code and OpenCode that delegate to skills. Use when creating new subagents or refactoring existing ones to follow the delegation pattern.

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Execute implementation plan by dispatching fresh subagent for each task, with code review between tasks

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Expert en Claude Code subagents (équipe dev orchestrée, contexte isolé, CLAUDE.md, multi-agent)

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プラグインに新しいサブエージェントを作成する。「サブエージェント作成」「新しいエージェント」「エージェントを作って」「エージェント追加」「subagent 作成」「エージェントを追加したい」「新規エージェント」などで起動。特定の役割を持つサブエージェントを生成。

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Invoke connected Unique spaces/subagents through the unique-cli subagent command. Use when the workspace exposes connected-space tools and you need to delegate a question or task to one of those configured assistants.

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Execute plans via delegate_task subagents (2-stage review).

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Operating discipline distilled from Claude Fable 5 for sessions and subagents on weaker models (Opus 4.x, Sonnet 5/4.x, Haiku 4.5) doing agentic coding. Load it at the START of any non-trivial task when not running on Fable 5: debugging, refactoring, changes across 2+ files, or unfamiliar code; and instruct a subagent on Opus/Sonnet/Haiku to read this skill with a non-trivial assignment. Triggers include "think like Fable", "fable-mode", "turn on discipline", "work carefully", dispatching a subagent on a weaker model, and repeated mistakes or rework in the session. NOT needed for trivial edits (rename, typo, one-liner) or purely conversational answers.

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在同一会话中逐项执行已有的实施计划，确认测试先失败后通过，记录进度与判断，并在全部任务结束后审查整个分支的更改。

• 执行已有的开发计划
• 不另派实施代理完成工作
• 根据进度记录恢复任务

将实施计划中的任务逐一交给不同的代理，检查是否符合规格要求以及代码质量，并将进度记录到文件中，最后审查整个分支的全部变更。

• 执行任务基本独立的实施计划
• 逐项检查任务是否符合规格要求
• 根据进度记录恢复工作

在单次会话中协调承担不同任务的子智能体，推进长期目标。后台工作与用户对话同时进行，并改进共享规范、工作环境和后续任务所需的信息访问条件。

• 协调长期目标中的任务分工
• 后台推进工作并持续对话
• 改进共享规范和工作环境

帮助为Claude Code插件创建独立执行多步骤任务的专用智能体，明确启动条件、职责、可用工具和输出格式，并指导验证配置及实际运行情况。

• 创建专门负责代码审查的智能体
• 完善启动条件和使用示例
• 将工具权限限制在必要范围

将发布工作交给子代理，先使用claude-mem的version-bump，再运行childhood，把两个阶段串联为一项自动推进的发布任务。

• 将发布工作委派给子代理
• 在version-bump后运行childhood
• 将两个发布阶段合并为一次请求

将分阶段实施计划分配给不同职责的子代理执行，每阶段进行验证、问题模式检查和代码审查，确认后再提交并推送，同时准备下一阶段交接。

• 执行make-plan生成的计划
• 按阶段分配实施任务
• 提交前进行验证和代码审查

Execute a written work plan by assigning implementation and verification to subagents. Track progress, isolate work in Git worktrees, record test evidence, and require independent verification before marking tasks complete, with optional pull request delivery.

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• Executing a saved work plan
• Assigning independent tasks to parallel agents
• Resuming work with recorded progress and evidence

Coordinates a named team of Codex workers with concrete responsibilities and shared progress records. It selects available collaboration tools, manages communication and separate working copies when needed, and handles integration, team archival, and cleanup after the work ends.

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• Splitting related implementation work across cooperating agents
• Investigating several aspects of a clearly defined problem
• Inspecting team progress and member responsibilities

Research a topic across multiple sources and turn the findings into a readable report. The skill coordinates separate research agents to compare options, examine markets or trends, and synthesize literature into a coherent narrative.

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• Comparing products or services
• Researching markets and industry trends
• Reviewing studies on a topic

Design how an AI agent trims long conversations and large tool outputs while retaining decisions, constraints, failed approaches, and sources. Compare summarization strategies, choose compression triggers, and consider separate subagents to keep intermediate information out of the main conversation.

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• Managing large search results and tool outputs
• Preserving decisions and constraints during summarization
• Choosing compression triggers and batch processing

Create a specialized Claude Code subagent with Hindsight long-term memory. Start with an empty agent or import prepared documents, then equip it with knowledge pages and memory search to reuse information across sessions.

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• Create a specialized agent that remembers across sessions
• Build an agent from prepared reference documents
• Start an empty agent and add knowledge over time

Create, update, explain, and compare Excalidraw diagrams through separate subagents. Keeps lengthy diagram data out of the main conversation, returning concise summaries of components, connections, structural differences, and completed changes.

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• Explaining components and connections in an existing diagram
• Adding a component and its connections
• Creating an architecture diagram or flowchart

Execute an implementation plan in one session by assigning a fresh subagent, a separate task-focused agent, to each task. Check specification compliance first, then code quality, and repeat fixes and reviews before proceeding.

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• Execute an existing plan with mostly independent tasks
• Check specification compliance before reviewing code quality
• Repeat fixes and reviews for each implementation task

Manage shell commands and AI agent tasks through a shared gbrain job queue. Track progress, steer agents, pause or resume work, and coordinate parallel tasks, with guidance for long operations, completion reporting, and reusable stage checkpoints.

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• Run scripts as monitored background jobs
• Coordinate parallel research by AI agents
• Redirect, pause, or resume an agent task

Build, edit, or debug backend AI agents with eve, a framework that represents agents as directories of files. Work on instructions, tools, connections, schedules, and related components using documentation bundled with the installed version.

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• Create a backend AI agent with eve
• Update agent instructions, tools, and connections
• Debug schedules and subagent configurations