compose

A skill for designing systems in which multiple AI agents work together on one task. An agent is an AI worker assigned a particular role.

In plain words
What is it for?
Use it when one agent cannot handle a task alone and you need to design a multi-agent workflow with clearly separated roles.
Why use it?
It helps decide whether several agents are genuinely needed and defines their responsibilities, boundaries, tools, and coordination pattern.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/sharpdeveye/maestro/compose
Any agent
npx skills add sharpdeveye/maestro --skill compose
Clone the repo
git clone --depth 1 https://github.com/sharpdeveye/maestro

Made for: Claude Code, Codex.

Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 661 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00020 $0.00661
Opus 5 $0.00010 $0.00331
Sonnet 5 $0.00004 $0.00132
Haiku 4.5 $0.00002 $0.00066

Measured 2d ago against content hash f3b306b99c39, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

compose scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

source/skills/compose/SKILL.md · 87 lines

How it starts

The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 87 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 87 lines · 20 tokens per session scan A f3b306b99c39

Subscribe to this mod's changes

compose is a skill published in the GitHub repository sharpdeveye/maestro (415 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 661 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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