claude-devfleet by affaan-m/everything-claude-code
npx skills add https://github.com/affaan-m/everything-claude-code --skill claude-devfleet当你需要调度多个 Claude Code 智能体并行处理编码任务时,请使用此技能。每个智能体都在一个独立的 git worktree 中运行,并配备完整的工具集。
需要连接一个正在运行的 Claude DevFleet 实例(通过 MCP):
claude mcp add devfleet --transport http http://localhost:18801/mcp
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
| 工具 | 用途 |
|---|---|
plan_project(prompt) | AI 将描述分解为一个包含链式任务的项目 |
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create_project(name, path?, description?)手动创建一个项目,返回 project_id |
create_mission(project_id, title, prompt, depends_on?, auto_dispatch?) | 添加一个任务。depends_on 是一个任务 ID 字符串列表(例如 ["abc-123"])。设置 auto_dispatch=true 可在依赖满足时自动启动。 |
dispatch_mission(mission_id, model?, max_turns?) | 启动智能体执行任务 |
cancel_mission(mission_id) | 停止正在运行的智能体 |
wait_for_mission(mission_id, timeout_seconds?) | 阻塞直到任务完成(见下方说明) |
get_mission_status(mission_id) | 检查任务进度(非阻塞) |
get_report(mission_id) | 读取结构化报告(文件变更、测试情况、错误、后续步骤) |
get_dashboard() | 系统概览:运行中的智能体、统计信息、近期活动 |
list_projects() | 浏览所有项目 |
list_missions(project_id, status?) | 列出项目中的任务 |
关于
wait_for_mission的说明: 此调用会阻塞对话,最长timeout_seconds(默认 600 秒)。对于长时间运行的任务,建议每 30-60 秒使用get_mission_status轮询一次,以便用户能看到进度更新。
plan_project(prompt="...") → 返回 project_id 和带有 depends_on 链且 auto_dispatch=true 的任务列表。depends_on 为空)调用 dispatch_mission(mission_id=<first_mission_id>)。剩余任务将在其依赖项完成后自动调度(因为 plan_project 将它们设置为 auto_dispatch=true)。get_mission_status(mission_id=...) 或 get_dashboard() 来检查进度。get_report(mission_id=...)。与用户分享重点内容。DevFleet 默认最多同时运行 3 个智能体(可通过 DEVFLEET_MAX_AGENTS 配置)。当所有槽位都占满时,设置了 auto_dispatch=true 的任务将在任务监视器中排队,并在槽位空闲时自动调度。通过 get_dashboard() 查看当前槽位使用情况。
plan_project(prompt="...") → 显示包含任务和依赖关系的计划。depends_on 为空的那个)。auto_dispatch=true)。get_mission_status 或 get_dashboard() 轮询,直到所有任务达到终止状态(completed、failed 或 cancelled)。get_report(mission_id=...) —— 总结成功之处,并指出失败任务的错误和后续步骤。create_project(name="My Project") → 返回 project_id。create_mission(project_id=project_id, title="...", prompt="...", auto_dispatch=true) → 记录 root_mission_id。为每个后续任务调用 create_mission(project_id=project_id, title="...", prompt="...", auto_dispatch=true, depends_on=["<root_mission_id>"])。dispatch_mission(mission_id=...) 以启动链。get_report(mission_id=...)。create_project(name="...") → 获取 project_id。create_mission(project_id=project_id, title="Implement feature", prompt="...") → 获取 impl_mission_id。dispatch_mission(mission_id=impl_mission_id),然后使用 get_mission_status 轮询直到完成。get_report(mission_id=impl_mission_id) 以审查结果。create_mission(project_id=project_id, title="Review", prompt="...", depends_on=[impl_mission_id], auto_dispatch=true) —— 由于依赖项已满足,将自动启动。get_dashboard() 以了解智能体槽位可用性。auto_dispatch=true。没有此标志,任务将保持 draft 状态。每周安装量
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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.
Requires a running Claude DevFleet instance connected via MCP:
claude mcp add devfleet --transport http http://localhost:18801/mcp
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
| Tool | Purpose |
|---|---|
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 totimeout_seconds(default 600). For long-running missions, prefer polling withget_mission_statusevery 30–60 seconds instead, so the user sees progress updates.
plan_project(prompt="...") → returns project_id + list of missions with depends_on chains and auto_dispatch=true.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).get_mission_status(mission_id=...) or to check progress.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.
plan_project(prompt="...") → shows plan with missions and dependencies.depends_on).auto_dispatch=true).get_mission_status or get_dashboard() periodically until all missions reach a terminal state (completed, failed, or cancelled).get_report(mission_id=...) for each terminal mission — summarize successes and call out failures with errors and next steps.create_project(name="My Project") → returns project_id.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.dispatch_mission(mission_id=...) on the first mission to start the chain.get_report(mission_id=...) when done.create_project(name="...") → get project_id.create_mission(project_id=project_id, title="Implement feature", prompt="...") → get impl_mission_id.dispatch_mission(mission_id=impl_mission_id), then poll with get_mission_status until complete.get_report(mission_id=impl_mission_id) to review results.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.get_dashboard() for agent slot availability before bulk dispatching.auto_dispatch=true if you want them to trigger automatically when dependencies complete. Without this flag, missions stay in draft status.Weekly Installs
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get_dashboard()get_report(mission_id=...) when missions complete. Share highlights with the user.