Orchestrates parallel Claude Code agents in isolated git worktrees: plans projects from natural language, dispatches missions, monitors progress, and reads structured reports.
From muggle-ai-teamsnpx claudepluginhub multiplex-ai/muggle-ai-teams --plugin muggle-ai-teams/devfleetDelegates task description to claude-devfleet skill: plans DAG for approval, executes missions, polls status, reports IDs, changes, failures, and next steps.
/devfleetOrchestrates parallel Claude Code agents in isolated git worktrees: plans projects from natural language, dispatches missions, monitors progress, and reads structured reports.
/devfleetOrchestrates parallel Claude Code agents in isolated git worktrees: plans projects from natural language, dispatches missions, monitors progress, and reads structured reports.
/devfleetDelegates task description to claude-devfleet skill: plans DAG for approval, executes missions, polls status, reports IDs, changes, failures, and next steps.
/devfleetOrchestrates parallel Claude Code agents in isolated git worktrees: plans projects from natural language, dispatches missions, monitors progress, and reads structured reports.
/devfleetOrchestrates parallel Claude Code agents in isolated git worktrees: plans projects from natural language, dispatches missions, monitors progress, and reads structured reports.
Orchestrate parallel Claude Code agents via Claude DevFleet. Each agent runs in an isolated git worktree with full tooling.
Requires the DevFleet MCP server: claude mcp add devfleet --transport http http://localhost:18801/mcp
User describes project
→ plan_project(prompt) → mission DAG with dependencies
→ Show plan, get approval
→ dispatch_mission(M1) → Agent spawns in worktree
→ M1 completes → auto-merge → M2 auto-dispatches (depends_on M1)
→ M2 completes → auto-merge
→ get_report(M2) → files_changed, what_done, errors, next_steps
→ Report summary to user
mcp__devfleet__plan_project(prompt="<user's description>")
This returns a project with chained missions. Show the user:
Wait for user approval before dispatching. Show the plan clearly.
Dispatch the first mission (the one with empty depends_on):
mcp__devfleet__dispatch_mission(mission_id="<first_mission_id>")
The remaining missions auto-dispatch as their dependencies complete (because plan_project creates them with auto_dispatch=true). When manually creating missions with create_mission, you must explicitly set auto_dispatch=true for this behavior.
mcp__devfleet__get_dashboard()
Or check a specific mission:
mcp__devfleet__get_mission_status(mission_id="<id>")
Prefer polling with get_mission_status over wait_for_mission for long-running missions, so the user sees progress updates.
mcp__devfleet__get_report(mission_id="<mission_id>")
Call this for every mission that reached a terminal state. Reports contain: files_changed, what_done, what_open, what_tested, what_untested, next_steps, errors_encountered.
| Tool | Purpose |
|---|---|
plan_project(prompt) | AI breaks description into chained missions with auto_dispatch=true |
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. |
dispatch_mission(mission_id, model?, max_turns?) | Start an agent |
cancel_mission(mission_id) | Stop a running agent |
wait_for_mission(mission_id, timeout_seconds?) | Block until done (prefer polling for long tasks) |
get_mission_status(mission_id) | Check progress without blocking |
get_report(mission_id) | Read structured report |
get_dashboard() | System overview |
list_projects() | Browse projects |
list_missions(project_id, status?) | List missions |
get_dashboard() for slot availability.