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.
npx skills add uvwt/agentdock-skills --skill multi-agent-orchestrationgit clone --depth 1 https://github.com/uvwt/agentdock-skillsWrote 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.
[](https://agentmods.dev/skills/uvwt/agentdock-skills/multi-agent-orchestration)<a href="https://agentmods.dev/skills/uvwt/agentdock-skills/multi-agent-orchestration"><img src="https://agentmods.dev/badge/skills/uvwt/agentdock-skills/multi-agent-orchestration.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.02772 |
| Opus 5 | $0.00036 | $0.01386 |
| Sonnet 5 | $0.00014 | $0.00554 |
| Haiku 4.5 | $0.00007 | $0.00277 |
Grade A, and why
multi-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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Orchestration
本 Skill 是 AgentDock Orchestrator 的轻量客户端。它不再在本地维护第二套 Git/Record/HTML 控制面；Project、Work Item、Assignment、Execution、Gate、Record 与 Outcome 的唯一事实源都在 Orchestrator 服务端。
适合的运行方式是：1~5 号各自有固定定时任务 → 定时唤醒 AgentDock → 调用本 Skill → 按 slot 原子领取一个服务端 Assignment → 执行 → 回报。
1. 固定 Slot
| Slot | 协议身份 | 主要职责 |
|---|---|---|
| 1 | Orchestrator | plan / rework / integrate / close |
| 2 | Independent Gatekeeper | 对最终候选做独立 Gate |
| 3 | Worker | 动态角色，由 Assignment focus_role 决定 |
| 4 | Worker | 动态角色，由 Assignment focus_role 决定 |
| 5 | Worker | 动态角色，由 Assignment focus_role 决定 |
Slot 只是定时任务的固定入口，不是权限声明。 真正的协议角色、任务归属、Work Item 状态和可执行动作都由服务端当前 Assignment/Execution 决定。不要在 prompt、payload 或 Record 中伪造 from / role。
2. 每次定时唤醒
定时任务只需要告诉模型自己的编号，例如：
使用 multi-agent-orchestration Skill。你是 3 号，领取并推进当前正式任务；如果 idle 或 busy 就结束本轮。
模型调用：
printf '%s' '{"skill_action":"claim","slot":3}' | python3 run.py
脚本从 Skill 根目录以相对路径执行；URL、Token、节点标识等环境由运行宿主注入，不读取 AgentDock 私有目录或环境文件。
claim 是推荐入口。它在服务端一次完成“选择下一项 + 创建 Execution + 标记 running”，模型不需要再手动 start，也不需要保存 execution_id。
返回状态：
claimed：本轮拿到新的正式 Assignment，Execution 已经是running，直接开始实际工作。resume：上一轮 Execution 已成功结束，但仍有一个协议后续动作；读取next_action并继续，不要领取别的 Work Item。idle：当前没有属于该 Slot 的待办，正常结束。busy：该 Slot 已有 queued/running Execution，通常说明另一个定时触发仍在工作；新的定时触发不要重复执行。
同一 Slot 一次只运行一个 Execution。服务端会优先恢复 resume 上下文，再按 FIFO 从所有活动 Project/Work Item 中选择 READY Assignment，避免在 dispatch / Gate / close 尚未完成时跳去其他任务。
3. claimed / resume 后的统一流程
收到 claimed 后先阅读返回的：
projectwork_itemassignmentexecutionrecent_records
只执行 assignment 明确要求的工作，不自行改变正式任务归属。claim 已经自动写入 started；不要再调用 start。
后续所有写动作都只传自己的 slot，Skill 会从 Orchestrator 当前上下文解析 Execution；模型不要抄写、缓存或传递 execution_id。
执行时间较长时可以按 slot 报告关键进展；不要高频刷 progress：
{"skill_action":"progress","slot":3,"summary":"已筛出 12 个候选，正在核对真实使用量"}
有真实成果时及时上报 Artifact：
{
"skill_action": "artifact",
"slot": 3,
"artifact": {
"title": "实现结果",
"type": "text",
"value": "真实路径、URL、commit、报告或可验证结果",
"description": "这份成果如何核验"
}
}
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago Changed · -195 lines · -6 tokens per session a680140c3348
- 8d ago First seen · 433 lines · 77 tokens per session scan A 91d9f07646a0
multi-agent-orchestration is a skill published in the GitHub repository uvwt/agentdock-skills (3 stars, last pushed 7d ago), licensed Apache-2.0. It adds 71 tokens to every session and 2,772 once invoked, about $0.0004 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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