本文へ移動
cccskills
無料GitHub で公開

orchestrate

Pipeline orchestration: dispatch the highest-priority ready tasks/work units to agents, manage capacity, and coordinate the Todo to Done flow. Invoked as /agiflow:orchestrate. Uses list_tasks, list_active_tasks_by_org, list_members, update_task, get_work_unit_progress.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md4.1 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Invoked as /agiflow:orchestrate. In hosts without slash-prompts, this skill is triggered by matching intent and drives AgiFlow via its MCP tools.

Usage:

  • /agiflow:orchestrate - Check pipeline state and dispatch the next highest-priority task

Guardrails

  • This is a read-assess-dispatch loop, not an implementation prompt.
  • Do NOT implement tasks here — use /agiflow:run-task for that.
  • Keep capacity checks honest: do not dispatch if at capacity.
  • Report pipeline state even when no dispatch is needed.

AgiFlow Project Management Guidelines

Follow the shared AgiFlow project-management guidelines in references/agiflow-agents.md — agent assignment, the task status workflow and transitions, work-unit best practices, and the tags strategy apply to this workflow.


Steps Track these steps as TODOs and complete them one by one.

1. Assess Current Pipeline State

  1. Use list_tasks to count tasks in each active status column:

    • status: "In Progress" — tasks currently being coded by agents
    • status: "Testing" — tasks running test suites
    • status: "Review" — tasks awaiting human review
    • status: "Blocked" — tasks requiring human intervention
    • status: "Todo" — tasks ready for pickup (sorted by priority automatically)
  2. Report the pipeline state in a concise table:

    Pipeline State:
    ┌──────────────┬───────┐
    │ Status       │ Count │
    ├──────────────┼───────┤
    │ Todo         │   N   │
    │ In Progress  │   N   │
    │ Testing      │   N   │
    │ Review       │   N   │
    │ Blocked      │   N   │
    └──────────────┴───────┘
    

2. Check Capacity

  1. Determine active task count: In Progress + Testing combined.
  2. Check capacity limit (default: 3 concurrent active tasks unless specified).
  3. If at or above capacity:
    • Report: "At capacity (N active tasks). No dispatch needed."
    • List any Blocked tasks that need human attention.
    • Stop here.

3. Prioritize the Todo Queue

  1. Use list_tasks with status: "Todo" to retrieve the ready queue.

    • Tasks are automatically sorted by priority (high → medium → low).
    • Within the same priority, tasks are ordered by position then creation date.
  2. Review the top candidates:

    • Show the top 3-5 Todo tasks with: slug, title, priority, assignee, retryCount (from devInfo)
    • Skip any task where devInfo.retryCount >= devInfo.maxRetries (should be Blocked — flag it)

4. Dispatch

  1. Pick the highest-priority eligible Todo task.
  2. Report the dispatch decision:
    Dispatching: [SLUG] Task title (priority: high)
    Reason: Highest priority task in Todo queue
    
  3. Instruct the agent to run the task:
    • Use /agiflow:run-task <slug> to execute it
    • Or if already in an agent session, invoke the run-task prompt directly

5. Surface Blocked Tasks

  1. If there are any Blocked tasks, list them with their blockedReason from devInfo:
    Blocked Tasks Requiring Human Attention:
    - [SLUG] Task title: <blockedReason>
    
  2. Suggest actions for each blocked task (e.g., resolve dependency, provide credentials, clarify spec).

Common Mistakes to Avoid

  • ❌ Dispatching when already at capacity
  • ❌ Picking a lower-priority task when a higher-priority Todo exists
  • ❌ Dispatching a task that has retryCount >= maxRetries (it should be Blocked)
  • ❌ Skipping the pipeline state report
  • ❌ Attempting to implement the task inside this prompt

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use AtomLane to compile and execute safe atomic parallel plans on macOS and native Windows Preview for worthwhile independent argv tasks, dependency DAGs, supported platform entrypoints, or Apple-silicon operators. Use at task start or an execution boundary when structured local work may contain two or more worthwhile units; skip plain answers, one quick command, and work whose effects cannot be safely bounded.

日本語の概要は準備中です。原文の説明を表示しています。

hashgraph-online/awesome-codex-plugins1,2732026年10月10日 更新

add

無料

Register a deferred decision in the debt registry. Trigger by judgment, not a marker scan, whenever a future reader would ask "why this way?": an unmade decision, stub, loosened type, bypassed check, swallowed error, a default picked "for now", or a TODO/FIXME/HACK/XXX marker. Trigger immediately whenever you defer work, or when the user invokes $add. Over-register freely; the developer drops with "drop A", "drop A,C", or "drop all".

日本語の概要は準備中です。原文の説明を表示しています。

hashgraph-online/awesome-codex-plugins1,2732026年10月10日 更新

ADK 框架适配层。为 LangChain / EINO / AutoGen / AgentScope / CrewAI 提供框架特定的 代码模板、惯用模式、API 映射和项目结构,供 agent-dev-workshop Phase 5 代码生成使用。 每个框架 reference 文件标注 verified_date 用于版本锁定。

日本語の概要は準備中です。原文の説明を表示しています。

hashgraph-online/awesome-codex-plugins1,2732026年10月10日 更新

中文调试修复技能。用于报错、测试失败、页面异常、功能不符合预期、需要定位根因并做最小修复时。触发语包括"进入调试模式""帮我修问题""报错了""测试失败""页面坏了""找根因"。

日本語の概要は準備中です。原文の説明を表示しています。

hashgraph-online/awesome-codex-plugins1,2732026年10月10日 更新

交互式 AI Agent 开发工作坊:通过 6 阶段深度协作对话,引导用户完成 Agent 需求分析、架构设计、 工具定义、Prompt 与编排设计、代码生成、验证迭代,产出可直接运行的 Agent 项目。 框架无关设计优先,支持 LangChain / EINO / AutoGen / AgentScope / CrewAI 等 ADK 框架。

日本語の概要は準備中です。原文の説明を表示しています。

hashgraph-online/awesome-codex-plugins1,2732026年10月10日 更新

中文漂移审计技能。用于项目或学习过程变乱、上下文漂移、任务分叉、多个方案冲突、命名不一致、Codex 可能顺手改多了时。触发语包括"漂移检查""感觉跑偏了""项目变乱了""检查是否失控""分叉太多""上下文漂移"。

日本語の概要は準備中です。原文の説明を表示しています。

hashgraph-online/awesome-codex-plugins1,2732026年10月10日 更新

hashgraph-online のスキルをすべて見る

このスキルの問題を報告する