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compose

Use when a single agent demonstrably cannot handle the task and multi-agent coordination is justified.

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sharpdeveye/maestro星标 591(GitHub 591 + cccskills 0)

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  • SKILL.md2.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Consult the agent-architecture reference in the agent-workflow skill for topology patterns and when multi-agent is justified.


Design a multi-agent system. But first — are you sure you need one?

Step 1: Pre-Composition Check

Answer these before proceeding:

  1. Has a single agent been tried and failed? (If no, try single agent first)
  2. What specific limitation requires multiple agents? (If you can't name it, you don't need multi-agent)
  3. Is the cost/latency increase justified? (Multi-agent = 2-10x cost and latency)

If you can't articulate a specific limitation, use /amplify on the single agent instead.

Step 2: Design the Topology

Choose the right architecture pattern (consult the agent-architecture reference in the agent-workflow skill):

For each agent in the system, define:

## Agent: [Name]
Role: [One sentence]
Responsibilities: [What it does]
Boundaries: [What it does NOT do]
Tools: [List of tools this agent has access to]
Input: [What it receives]
Output: [What it produces]

Step 3: Design Handoffs

For each agent-to-agent connection:

## Handoff: [Agent A] → [Agent B]
Trigger: [When does A hand off to B?]
Payload: [What data is passed?]
Expected response: [What does A expect back?]
Timeout: [How long to wait?]
Failure handling: [What if B fails?]

Step 4: Design the Supervisor

Every multi-agent system needs a supervisor:

  • Monitors agent health and performance
  • Routes tasks to appropriate agents
  • Handles failures and escalation
  • Enforces global constraints (budget, time, quality)

Composition Checklist

  • Each agent has a clear, non-overlapping role
  • Handoff protocols are defined for every connection
  • A supervisor pattern is in place
  • Cost/latency budget accounts for all agents
  • Failure modes are handled at every handoff point
  • The system can be understood by reading the topology diagram

Recommended Next Step

After composition, run /fortify to add error handling at every handoff, then /evaluate to test the multi-agent system end-to-end.

NEVER:

  • Build multi-agent for a problem a single agent can handle
  • Create agents with overlapping responsibilities
  • Skip the supervisor (autonomous swarms are unpredictable)
  • Pass full context between all agents (pass only what's needed)
  • Compose without defining handoff protocols

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同一仓库的技能

Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

amplify

免费

Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

calibrate

免费

Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

capture

免费

Capture a session summary — what was done, what decisions were made, and what to do next.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

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