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多Agent团队编排-运营版

作者 admirobot · GitHub ↗ · v1.0.0 · MIT-0
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在 OpenClaw 中安装
/install moa-team-orchestration
功能描述
Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with...
使用说明 (SKILL.md)

Agent Team Orchestration

Production playbook for running multi-agent teams with clear roles, structured task flow, and quality gates.

Quick Start: Minimal 2-Agent Team

A builder and a reviewer. The simplest useful team.

1. Define Roles

Orchestrator (you) — Route tasks, track state, report results
Builder agent     — Execute work, produce artifacts

2. Spawn a Task

1. Create task record (file, DB, or task board)
2. Spawn builder with:
   - Task ID and description
   - Output path for artifacts
   - Handoff instructions (what to produce, where to put it)
3. On completion: review artifacts, mark done, report

3. Add a Reviewer

Builder produces artifact → Reviewer checks it → Orchestrator ships or returns

That's the core loop. Everything below scales this pattern.

Core Concepts

Roles

Every agent has one primary role. Overlap causes confusion.

Role Purpose Model guidance
Orchestrator Route work, track state, make priority calls High-reasoning model (handles judgment)
Builder Produce artifacts — code, docs, configs Can use cost-effective models for mechanical work
Reviewer Verify quality, push back on gaps High-reasoning model (catches what builders miss)
Ops Cron jobs, standups, health checks, dispatching Cheapest model that's reliable

Read references/team-setup.md when defining a new team or adding agents.

Task States

Every task moves through a defined lifecycle:

Inbox → Assigned → In Progress → Review → Done | Failed

Rules:

  • Orchestrator owns state transitions — don't rely on agents to update their own status
  • Every transition gets a comment (who, what, why)
  • Failed is a valid end state — capture why and move on

Read references/task-lifecycle.md when designing task flows or debugging stuck tasks.

Handoffs

When work passes between agents, the handoff message includes:

  1. What was done — summary of changes/output
  2. Where artifacts are — exact file paths
  3. How to verify — test commands or acceptance criteria
  4. Known issues — anything incomplete or risky
  5. What's next — clear next action for the receiving agent

Bad handoff: "Done, check the files." Good handoff: "Built auth module at /shared/artifacts/auth/. Run npm test auth to verify. Known issue: rate limiting not implemented yet. Next: reviewer checks error handling edge cases."

Reviews

Cross-role reviews prevent quality drift:

  • Builders review specs — "Is this feasible? What's missing?"
  • Reviewers check builds — "Does this match the spec? Edge cases?"
  • Orchestrator reviews priorities — "Is this the right work right now?"

Skip the review step and quality degrades within 3-5 tasks. Every time.

Read references/communication.md when setting up agent communication channels.Read references/patterns.md for proven multi-step workflows.

Reference Files

File Read when...
team-setup.md Defining agents, roles, models, workspaces
task-lifecycle.md Designing task states, transitions, comments
communication.md Setting up async/sync communication, artifact paths
patterns.md Implementing specific workflows (spec→build→test, parallel research, escalation)

Common Pitfalls

Spawning without clear artifact output paths

Agent produces great work, but you can't find it. Always specify the exact output path in the spawn prompt. Use a shared artifacts directory with predictable structure.

No review step = quality drift

"It's a small change, skip review." Do this three times and you have compounding errors. Every artifact gets at least one set of eyes that didn't produce it.

Agents not commenting on task progress

Silent agents create coordination blind spots. Require comments at: start, blocker, handoff, completion. If an agent goes silent, assume it's stuck.

Not verifying agent capabilities before assigning

Assigning browser-based testing to an agent without browser access. Assigning image work to a text-only model. Check capabilities before routing.

Orchestrator doing execution work

The orchestrator routes and tracks — it doesn't build. The moment you start "just quickly doing this one thing," you've lost oversight of the rest of the team.

When NOT to Use This Skill

  • Single-agent setups — Just follow standard AGENTS.md conventions. Team orchestration adds overhead that solo agents don't need.
  • One-off task delegation — Use sessions_spawn directly. This skill is for sustained workflows with multiple handoffs.
  • Simple question routing — If you're just forwarding a question to a specialist, that's a message, not a workflow.

This skill is for sustained team workflows — recurring collaboration patterns where agents depend on each other's output over multiple tasks.

安全使用建议
This is a documentation-only playbook and appears coherent with its stated purpose. Before installing/using it, confirm that your agent platform provides proper isolation and access controls for the shared directories it recommends (e.g., /shared/, /workspace/). Specifically: (1) ensure agents that write to /shared/ cannot access secrets or other sensitive system areas unintentionally; (2) verify spawn/send APIs (sessions_spawn/sessions_send) are restricted so only trusted agents can be started or interrupted; and (3) if you plan to automate escalation that supplies credentials, implement human gating or secure secret management rather than placing secrets in shared files. If those platform controls are in place, this skill is low-risk and appropriate for orchestrating multi-agent workflows.
能力评估
Purpose & Capability
The name/description (multi-agent orchestration) matches the SKILL.md and reference files: role definitions, task lifecycles, handoffs, and review workflows. There are no unexpected required binaries, env vars, or config paths that would be inappropriate for the described purpose.
Instruction Scope
The instructions assume standard agent platform capabilities: spawning/sending sessions, reading/writing shared directories (e.g., /shared/, /workspace/), and commenting on task state. These behaviors are coherent with orchestration, but they do assume the skill is allowed write/read access to shared artifact and workspace locations—verify your platform isolation and permissions before use.
Install Mechanism
No install spec or code is present (instruction-only). Nothing is downloaded or written to disk by the skill itself, which minimizes install-time risk.
Credentials
The skill requires no environment variables, credentials, or external config paths. References to credentials appear only as part of procedural guidance (e.g., escalate when an agent requests credentials), not as required inputs to the skill.
Persistence & Privilege
always is false and model invocation is not disabled (normal defaults). The skill does not request permanent presence or modify other skills' configs; it is a documentation/playbook resource for orchestrators.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install moa-team-orchestration
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /moa-team-orchestration 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
初始版本,多Agent协作编排模板
元数据
Slug moa-team-orchestration
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

多Agent团队编排-运营版 是什么?

Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 93 次。

如何安装 多Agent团队编排-运营版?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install moa-team-orchestration」即可一键安装,无需额外配置。

多Agent团队编排-运营版 是免费的吗?

是的,多Agent团队编排-运营版 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

多Agent团队编排-运营版 支持哪些平台?

多Agent团队编排-运营版 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 多Agent团队编排-运营版?

由 admirobot(@admirobot)开发并维护,当前版本 v1.0.0。

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