agent-orchestration

agent-orchestration is a skill for Claude Code from AstralArtisan/astral-skills. It costs 275 tokens per session (4,227 once invoked), scanned A, original, MIT.

A workflow for coordinating several isolated coding agents on a complex project. One main agent divides the work, passes information through documents, checks each stage, and combines the results.

In plain words
What is it for?
Use it to split substantial development, documentation, or research work into stages that can be checked independently. It is intended for agent environments that can start separate sub-agents.
Why use it?
It keeps a large, multi-stage task from exceeding one agent’s working context. It also reduces the need for the user to repeatedly provide direction after the initial plan.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: reads .claude/ paths; mentions CLAUDE.md; mentions subagents.

Part of the agent-orchestration plugin — 1 skill shipped together

Good fit Use it to split substantial development, documentation, or research work into stages that can be checked independently. It is intended for agent environments that can start separate sub-agents.

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Install with agentmods
npx agentmods add skills/astralartisan/astral-skills/agent-orchestration
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.

Any agent
npx skills add AstralArtisan/astral-skills --skill agent-orchestration
Clone the repo
git clone --depth 1 https://github.com/AstralArtisan/astral-skills

Made for: Claude Code.

Or install agent-orchestration, the plugin that ships this one along with the rest of its 1 skill.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for agent-orchestration

README.md
[![agentmods](https://agentmods.dev/badge/skills/astralartisan/astral-skills/agent-orchestration/github.svg)](https://agentmods.dev/skills/astralartisan/astral-skills/agent-orchestration)
Your own site
<a href="https://agentmods.dev/skills/astralartisan/astral-skills/agent-orchestration"><img src="https://agentmods.dev/badge/skills/astralartisan/astral-skills/agent-orchestration/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for agent-orchestration

Your own site · 80×15
<a href="https://agentmods.dev/skills/astralartisan/astral-skills/agent-orchestration"><img src="https://agentmods.dev/badge/skills/astralartisan/astral-skills/agent-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 275 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,227 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00275 $0.04227
Opus 5 $0.00138 $0.02114
Sonnet 5 $0.00055 $0.00845
Haiku 4.5 $0.00028 $0.00423

Measured 11d ago against content hash 5beefe8788da, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

agent-orchestration 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 11d 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.

plugins/agent-orchestration/skills/agent-orchestration/SKILL.md · 153 lines

How it starts

The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.

中枢编排:像项目 leader 一样调度子代理

你现在是中枢调度者(orchestrator),像项目组 leader 一样工作。用户是 BOSS:他只在开工前给方向、定验收口径并确认方案;具体的拆解、派发、检查、整合、记录、推进、提交由你完成。你的价值不在于亲自把代码 / 文档 / 调研全写完,而在于把一个单一上下文扛不住的大任务,组织成一串由隔离子代理分别完成、并逐个通过真实验收的可交付单元

一句话记住内核:把上下文压力外推到子代理和交接文档上,让你自己的主线只保留协调所需的最小信息。 你读得越克制,越能把项目稳稳带到终点。

适用前提:你运行在一个"主 agent 能拉起隔离子代理"的环境。文档交接是本方法的核心,正因为它是最低共同接口——不依赖任何工具特有的上下文共享,所以这套方法能在 Claude Code、Codex 等不同宿主间移植。

何时用、何时不用

digraph when_to_use {
    "收到任务请求" [shape=box];
    "多阶段 + 需要独立验收?" [shape=diamond];
    "单一上下文扛得住?" [shape=diamond];
    "直接做(无需编排)" [shape=box];
    "用本方法编排" [shape=box];

    "收到任务请求" -> "多阶段 + 需要独立验收?";
    "多阶段 + 需要独立验收?" -> "直接做(无需编排)" [label="否"];
    "多阶段 + 需要独立验收?" -> "单一上下文扛得住?" [label="是"];
    "单一上下文扛得住?" -> "直接做(无需编排)" [label="扛得住"];
    "单一上下文扛得住?" -> "用本方法编排" [label="扛不住"];
}

会触发本方法的,是这一类请求:交付物要多个阶段、每个阶段值得独立验收、整体塞不进一个上下文,而且用户希望只在开工前把关、之后让你协调子代理自主做完。例如:"你当 leader,开工前跟我对一遍范围和验收口径,之后用子代理分阶段做完、自己验收迭代到可交付,别中途反复打断我。"

下面这类直接做就好,别启动整套机器:

信号 该怎么做
单步小改("修一下这个空指针 bug") 直接改
概念解释("讲讲 LR(0) 分析") 直接答
一个小函数 / 一段脚本 直接写

判断标准不是"任务听起来大不大",而是是否真的需要多次独立验收、且单一上下文扛不住。一个上下文就能干净做完并自检的事,编排只会徒增子代理开销。

两段式工作法(本方法的骨架)

本方法最关键的,是把和用户的关系切成界限分明的两段。守住这条边界,用户就得到他要的体验:开工前充分把关,开工后不被打断。

A. 开工前:唯一与用户交互的窗口

针对每个新任务,先做一轮聚焦的立项沟通,问清足以设计流水线的事实。别泛泛地问,围绕这几项:

  • 交付物:最终要交出什么?(可运行的程序 / 一份报告 / 一个 demo / 一套测试……)
  • 完成与验收口径:怎样算"做完"?验收时拿什么判定通过?(具体命令、期望输出、指标)
  • 约束:技术栈、依赖、风格、时限、不能动的东西。
  • 已有产物与前序依赖:有没有现成代码 / 规格 / 上一阶段成果?路径在哪?
  • 运行与测试环境:用什么运行时、怎么跑、怎么测;有没有要先建的虚拟环境或服务。
  • 是否需要 demo 或界面:要不要可视化前端、截图、演示;若要,用什么。
  • 合理的阶段划分:这件事大致分几步、每步产出什么、在哪设验收门。

据此现场设计一条任务专属流水线(怎么设计、阶段如何增删,见 references/pipeline-design.md),把它物化成文档(见下文"物化调度方案"),然后请用户过目确认

这一步同时是成本闸:子代理是完整实例,派发越多用量消耗越快。趁确认方案时,把"大约要拉多少个子代理、分几个阶段"一并摆给用户看,让规模在开工前就定下来。

不要写死任何固定流水线。 阶段数量、各阶段叫什么、要不要前端、用不用某个报告技能、handoff 文件怎么命名——都由你按本次任务现场决定,作为可选项,而不是照搬模板。

B. 开工后:全自主执行循环

一旦用户确认、你开始派发,就进入全自主模式,不再停下来问用户。围绕每个可交付单元,跑这个循环:

Read the full file on GitHub · 153 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. 11d ago First seen · 153 lines · 275 tokens per session scan A 5beefe8788da

Subscribe to this mod's changes

agent-orchestration is a skill published in the GitHub repository AstralArtisan/astral-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 275 tokens to every session and 4,227 once invoked, about $0.0014 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 2026-08-31.

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