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teamharness-team-coordination

Plan work across several AI agents by defining task owners, deliverables, dependencies, and acceptance criteria. Choose a finite workflow or repeated improvement cycles, then review results before allowing dependent tasks to proceed.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Use when a Leader must choose Project Work boundaries, owners, dependency shape, DAG vs Loop, acceptance criteria, or follow-up strategy. Do not use for Quick Task execution after the mode is already selected.

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适用场景

  • Divide a project among several AI agents
  • Choose between a planned workflow and repeated improvement
  • Define acceptance criteria for each deliverable
  • Review results before starting dependent tasks

包含的文件(1)

  • SKILL.md3.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Team Coordination

Use this skill when acting as Leader to coordinate several members.

Break work into bounded tasks, assign one owner per task, keep dependencies visible, and avoid asking multiple workers to silently own the same result.

Report accepted outcomes, not every intermediate worker message.

Leader Strategy

You are the Team Leader. Design context boundaries, task ownership, dependency shape, and acceptance criteria. Do not act as a message router that simply forwards the requester's raw message to Workers.

Before delegation, clarify:

  • what done means
  • which Worker owns each deliverable
  • which task can run independently
  • which result must be accepted before downstream work starts
  • where the requester should receive final updates

If the goal, acceptance standard, ownership, or safe next step is ambiguous, ask the requester before dispatching work.

Choose Execution Mode

TeamHarness Project Work supports DAG and Loop execution modes.

Choose DAG when the work is finite and the dependency graph can be planned now.

DAG fits:

  • known phases
  • known dependencies
  • fan-out and fan-in work
  • one-shot implementation
  • bounded verification

Choose Loop when the work repeats until a stop condition, quality gate, evidence threshold, or maximum iteration count is reached.

Loop fits:

  • many rounds
  • repeated research waves
  • quality improvement until accepted
  • build-test-fix until passing
  • exploration where the next question depends on current results
  • requester language such as "iterate", "repeat", "retry until passing", or "up to N rounds"

Do not pre-expand repeated Loop rounds into a large DAG. Plan the current iteration, evaluate it, then decide whether to continue, replan, ask the requester, stop successfully, or stop blocked.

Good Task Boundaries

A good task has:

  • one owner
  • one inspectable output
  • enough context to work independently
  • no write conflict with sibling tasks
  • clear acceptance criteria
  • deliverables inside shared/tasks/{task-id}/

Do not assign multiple Workers to write the same file, directory, final answer, or decision record.

Result Decisions

A Worker completion is not automatic project progress. After check_task, decide whether to:

  • accept the result and mark the project node completed
  • request revision through a new task
  • add a verifier task
  • ask the requester for clarification
  • report a blocker

Only accepted results should unblock downstream dependencies.

Patch Rules

Do not:

  • delegate tasks that were not returned by ready-node resolution
  • ask Workers to edit project-level state
  • accept a task just because it says SUCCESS
  • hide task assignment in a DM when the Team Room is the shared coordination room
  • report every intermediate Worker message as a requester update

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同一仓库的技能

Find worker agents in Nacos that match a task, recommend candidates, and import the administrator's chosen worker. It also supports importing a specified nacos package URI, with worker name confirmation and direct reporting of installation failures.

中文简介正在准备中。当前显示英语简介。

  • Find a suitable worker for an unassigned task
  • Compare Nacos worker candidates before importing
  • Import a worker from a specified nacos package URI
agentscope-ai/AgentTeams58422026年10月20日 更新

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中文简介正在准备中。当前显示英语简介。

  • Moving standalone OpenClaw into AgentTeams
  • Reviewing required tool dependencies
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agentscope-ai/AgentTeams58422026年10月20日 更新

Manage communication channels by identifying senders, maintaining trusted contacts, and setting the administrator's primary notification destination. It also covers first contact on a new channel and contacting the administrator across channels, with rules for handling unknown senders.

中文简介正在准备中。当前显示英语简介。

  • Identify who sent a message in a room
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  • Configure the administrator's primary notification channel
agentscope-ai/AgentTeams58422026年10月20日 更新

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中文简介正在准备中。当前显示英语简介。

  • Delegating code implementation or bug fixes to an AI CLI
  • Writing coding requests with clear acceptance criteria
  • Reviewing and testing changes returned by a CLI
agentscope-ai/AgentTeams58422026年10月20日 更新

Run AI coding command-line tools on behalf of worker agents using their prepared prompts and workspaces. Detect available tools, configure delegation, return results, and apply supported execution controls such as time budgets and permission settings.

中文简介正在准备中。当前显示英语简介。

  • Running coding tools for worker agents
  • Detecting and selecting available coding CLIs
  • Setting Qwen Code permissions and time budgets
agentscope-ai/AgentTeams58422026年10月20日 更新

Guide agent replies in Matrix chat rooms, including when to mention a coordinator, report completion, raise blockers, or answer questions. The rules limit repetitive acknowledgments and stop exchanges that add no new task, question, or decision.

中文简介正在准备中。当前显示英语简介。

  • Reporting task completion to a coordinator
  • Raising blockers or questions that need action
  • Reducing repetitive acknowledgments
agentscope-ai/AgentTeams58422026年10月20日 更新

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