Orchestrates multi-agent coding tasks via Claude DevFleet: plans projects into mission DAGs, dispatches parallel agents to isolated git worktrees, monitors progress, and retrieves structured reports.
From muggle-ai-teamsnpx claudepluginhub multiplex-ai/muggle-ai-teams --plugin muggle-ai-teamsThis skill uses the workspace's default tool permissions.
Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.
Executes pre-written implementation plans: critically reviews, follows bite-sized steps exactly, runs verifications, tracks progress with checkpoints, uses git worktrees, stops on blockers.
Guides idea refinement into designs: explores context, asks questions one-by-one, proposes approaches, presents sections for approval, writes/review specs before coding.
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"
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plan_project(prompt) → project_id + mission DAG
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Show plan to user → get approval
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dispatch_mission(M1) → Agent 1 spawns in worktree
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M1 completes → auto-merge → auto-dispatch M2 (depends_on M1)
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M2 completes → auto-merge
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get_report(M2) → files_changed, what_done, errors, next_steps
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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 get_dashboard() to check progress.get_report(mission_id=...) when missions complete. Share highlights with the user.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.