Claude Devfleet

affaan-m/ECC/skills/claude-devfleet

作者 affaan-mef648e01899ba3e8dc6371642deaaf64b4477775無授權條款275K 個星標收錄於 2026年10月9日更新於 2026年10月9日儲存庫4 天前更新

Orchestrate multi-agent coding tasks via Claude DevFleet — plan projects, dispatch parallel agents in isolated worktrees, monitor progress, and read structured reports. Use when dispatching parallel coding agents across isolated worktrees and tracking their reports.

AI 產生的概覽

透過 DevFleet MCP 伺服器協調並行的 Claude Code 代理,規劃任務、在隔離的 git worktree 中派送並讀取報告。

功能
此技能指引代理使用 Claude DevFleet MCP 伺服器,將程式開發需求拆解為由多個相依任務組成的專案,把代理派送到隔離的 git worktree 中執行,監控其狀態,並蒐集結構化報告,內容涵蓋變更的檔案、測試、錯誤與後續步驟。文件中列出可用工具(plan_project、create_project、create_mission、dispatch_mission、cancel_mission、wait_for_mission、get_mission_status、get_report、get_dashboard、list_projects、list_missions),並說明規劃、派送、監控與回報的工作流程,包括相依任務自動派送,以及預設最多三個並行代理。其產出是專案、任務與彙總報告,而非程式碼本身。
適用情境
適用於需要讓多個程式開發任務在隔離的 worktree 中並行執行,並追蹤其進度與結果的情境。也適合規劃多任務專案、啟動根任務,並向使用者回報結果。
執行需求
需要另外安裝並執行的 DevFleet 伺服器,並透過 MCP 存取(例如本機 HTTP 端點),同時需要 git worktree 供代理使用。此技能不含指令碼,僅為說明文件。選用的 DEVFLEET_MAX_AGENTS 設定可控制並行數量。

Claude DevFleet Multi-Agent Orchestration

When to Use

Use this skill when you need to dispatch multiple Claude Code agents to work on coding tasks in parallel. Each agent runs in an isolated git worktree with full tooling.

Setup

The DevFleet server is a separate project, not bundled with ECC. Install and run it from its repository first: https://github.com/LEC-AI/claude-devfleet

Then connect the running instance via MCP:

bash
claude mcp add devfleet --transport http http://localhost:18801/mcp

Before first use, verify the process listening on port 18801 is the DevFleet binary you installed (see SECURITY.md on localhost MCP servers).

How It Works

User → "Build a REST API with auth and tests"  ↓plan_project(prompt) → project_id + mission DAG  ↓Show plan to user → get approval  ↓dispatch_mission(M1) → Agent 1 spawns in worktree  ↓M1 completes → auto-merge → auto-dispatch M2 (depends_on M1)  ↓M2 completes → auto-merge  ↓get_report(M2) → files_changed, what_done, errors, next_steps  ↓Report back to user

Tools

ToolPurpose
plan_project(prompt)AI breaks a description into a project with chained missions
create_project(name, path?, description?)Create a project manually, returns project_id
create_mission(project_id, title, prompt, depends_on?, auto_dispatch?)Add a mission. depends_on is a list of mission ID strings (e.g., ["abc-123"]). Set auto_dispatch=true to auto-start when deps are met.
dispatch_mission(mission_id, model?, max_turns?)Start an agent on a mission
cancel_mission(mission_id)Stop a running agent
wait_for_mission(mission_id, timeout_seconds?)Block until a mission completes (see note below)
get_mission_status(mission_id)Check mission progress without blocking
get_report(mission_id)Read structured report (files changed, tested, errors, next steps)
get_dashboard()System overview: running agents, stats, recent activity
list_projects()Browse all projects
list_missions(project_id, status?)List missions in a project

Note on wait_for_mission: This blocks the conversation for up to timeout_seconds (default 600). For long-running missions, prefer polling with get_mission_status every 30–60 seconds instead, so the user sees progress updates.

Workflow: Plan → Dispatch → Monitor → Report

  1. Plan: Call plan_project(prompt="...") → returns project_id + list of missions with depends_on chains and auto_dispatch=true.
  2. Show plan: Present mission titles, types, and dependency chain to the user.
  3. Dispatch: Call dispatch_mission(mission_id=<first_mission_id>) on the root mission (empty depends_on). Remaining missions auto-dispatch as their dependencies complete (because plan_project sets auto_dispatch=true on them).
  4. Monitor: Call get_mission_status(mission_id=...) or get_dashboard() to check progress.
  5. Report: Call get_report(mission_id=...) when missions complete. Share highlights with the user.

Concurrency

DevFleet runs up to 3 concurrent agents by default (configurable via DEVFLEET_MAX_AGENTS). When all slots are full, missions with auto_dispatch=true queue in the mission watcher and dispatch automatically as slots free up. Check get_dashboard() for current slot usage.

Examples

Full auto: plan and launch

  1. plan_project(prompt="...") → shows plan with missions and dependencies.
  2. Dispatch the first mission (the one with empty depends_on).
  3. Remaining missions auto-dispatch as dependencies resolve (they have auto_dispatch=true).
  4. Report back with project ID and mission count so the user knows what was launched.
  5. Poll with get_mission_status or get_dashboard() periodically until all missions reach a terminal state (completed, failed, or cancelled).
  6. get_report(mission_id=...) for each terminal mission — summarize successes and call out failures with errors and next steps.

Manual: step-by-step control

  1. create_project(name="My Project") → returns project_id.
  2. create_mission(project_id=project_id, title="...", prompt="...", auto_dispatch=true) for the first (root) mission → capture root_mission_id. create_mission(project_id=project_id, title="...", prompt="...", auto_dispatch=true, depends_on=["<root_mission_id>"]) for each subsequent task.
  3. dispatch_mission(mission_id=...) on the first mission to start the chain.
  4. get_report(mission_id=...) when done.

Sequential with review

  1. create_project(name="...") → get project_id.
  2. create_mission(project_id=project_id, title="Implement feature", prompt="...") → get impl_mission_id.
  3. dispatch_mission(mission_id=impl_mission_id), then poll with get_mission_status until complete.
  4. get_report(mission_id=impl_mission_id) to review results.
  5. create_mission(project_id=project_id, title="Review", prompt="...", depends_on=[impl_mission_id], auto_dispatch=true) — auto-starts since the dependency is already met.

Guidelines

  • Always confirm the plan with the user before dispatching, unless they said to go ahead.
  • Include mission titles and IDs when reporting status.
  • If a mission fails, read its report before retrying.
  • Check get_dashboard() for agent slot availability before bulk dispatching.
  • Mission dependencies form a DAG — do not create circular dependencies.
  • Each agent runs in an isolated git worktree and auto-merges on completion. If a merge conflict occurs, the changes remain on the agent's worktree branch for manual resolution.
  • When manually creating missions, always set auto_dispatch=true if you want them to trigger automatically when dependencies complete. Without this flag, missions stay in draft status.

來源與署名

來源:affaan-m/ECC位於skills/claude-devfleet提交ef648e0

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