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General coding280k · #152

claude-devfleet

Lets your agent manage multiple coding agents at once. It can plan projects, distribute tasks, and monitor each agent's progress.

What it does

This tool helps organize AI programmers (agents) to work together on a coding project. It plans the tasks, distributes the work to different agents working in separate environments, and shows progress.

When to use it

  • ✓When you want multiple AI programmers to work on different parts of a project simultaneously.
  • ✓When you need AI programmers to work in isolated environments to avoid conflicts.
  • ✓When you want to track the progress of each AI programmer and see final reports.

Example of what to ask your AI

“Plan and launch a project to build a REST API with authentication and automated tests.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/affaan-m/ECC/blob/main/skills/claude-devfleet/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/affaan-m/ECC/blob/main/skills/claude-devfleet/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

You describe the project, and the tool creates a plan with tasks. It then sends these tasks to individual AI programmers. They work on their tasks, and when they finish, the tool collects the reports.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add affaan-m/ECC --skill claude-devfleet

Or download just the file:

curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/skills/claude-devfleet/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

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:

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

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 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.

…

FAQ

What are 'isolated worktrees'?

These are like separate boxes where each AI programmer works, ensuring one's work doesn't interfere with another's.

How do I know what the AI programmers are doing?

You can request a report (`get_report`) to see what was done, which files changed, and if there were any errors.

Can I stop an AI programmer mid-task?

Yes, you can use the `cancel_mission` command to stop a specific task.

Details

Original description by the author

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.

repo: affaan-m/ECC

Popularity

GitHub stars
280k
Ranking position
#152
License
MIT
Updated
10/15/2026

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