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Add Claude DevFleet multi-agent orchestration skill (#505)
* Add Claude DevFleet multi-agent orchestration skill Adds a skill for Claude DevFleet — a multi-agent coding platform that dispatches Claude Code agents to work on missions in parallel, each in an isolated git worktree. The skill teaches Claude Code how to use DevFleet's 11 MCP tools to plan projects, dispatch agents, monitor progress, and read structured reports. Setup: claude mcp add devfleet --transport sse http://localhost:18801/mcp/sse Repo: https://github.com/LEC-AI/claude-devfleet * Add DevFleet MCP config and /devfleet command - Add devfleet entry to mcp-configs/mcp-servers.json for discovery - Add /devfleet slash command for multi-agent orchestration workflow * Add orchestration flow diagrams to skill and command - Add visual flow to SKILL.md showing plan → dispatch → auto-chain → report - Add flow to /devfleet command showing the trigger sequence * Fix review feedback: frontmatter, workflow docs, HTTP transport - Add YAML description frontmatter to commands/devfleet.md - Fix manual workflow in SKILL.md to capture project_id from create_project - Change mcp-servers.json from deprecated SSE to Streamable HTTP transport * Address all review comments * Add monitoring/reporting steps to full auto pattern Addresses review feedback: the full auto example now includes polling for completion and retrieving reports, matching the other patterns. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Update skills/claude-devfleet/SKILL.md Co-authored-by: cubic-dev-ai[bot] <191113872+cubic-dev-ai[bot]@users.noreply.github.com> * Update skills/claude-devfleet/SKILL.md Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Update commands/devfleet.md Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Fix review feedback Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Avdhesh Singh Chouhan <avdhesh.acro@gmail.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: cubic-dev-ai[bot] <191113872+cubic-dev-ai[bot]@users.noreply.github.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
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Avdhesh Singh Chouhan
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commands/devfleet.md
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commands/devfleet.md
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---
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description: Orchestrate parallel Claude Code agents via Claude DevFleet — plan projects from natural language, dispatch agents in isolated worktrees, monitor progress, and read structured reports.
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---
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# DevFleet — Multi-Agent Orchestration
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Orchestrate parallel Claude Code agents via Claude DevFleet. Each agent runs in an isolated git worktree with full tooling.
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Requires the DevFleet MCP server: `claude mcp add devfleet --transport http http://localhost:18801/mcp`
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## Flow
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```
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User describes project
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→ plan_project(prompt) → mission DAG with dependencies
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→ Show plan, get approval
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→ dispatch_mission(M1) → Agent spawns in worktree
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→ M1 completes → auto-merge → M2 auto-dispatches (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 summary to user
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```
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## Workflow
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1. **Plan the project** from the user's description:
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```
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mcp__devfleet__plan_project(prompt="<user's description>")
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```
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This returns a project with chained missions. Show the user:
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- Project name and ID
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- Each mission: title, type, dependencies
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- The dependency DAG (which missions block which)
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2. **Wait for user approval** before dispatching. Show the plan clearly.
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3. **Dispatch the first mission** (the one with empty `depends_on`):
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```
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mcp__devfleet__dispatch_mission(mission_id="<first_mission_id>")
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```
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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.
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4. **Monitor progress** — check what's running:
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```
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mcp__devfleet__get_dashboard()
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```
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Or check a specific mission:
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```
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mcp__devfleet__get_mission_status(mission_id="<id>")
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```
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Prefer polling with `get_mission_status` over `wait_for_mission` for long-running missions, so the user sees progress updates.
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5. **Read the report** for each completed mission:
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```
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mcp__devfleet__get_report(mission_id="<mission_id>")
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```
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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.
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## All Available Tools
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| Tool | Purpose |
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|------|---------|
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| `plan_project(prompt)` | AI breaks description into chained missions with `auto_dispatch=true` |
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| `create_project(name, path?, description?)` | Create a project manually, returns `project_id` |
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| `create_mission(project_id, title, prompt, depends_on?, auto_dispatch?)` | Add a mission. `depends_on` is a list of mission ID strings. |
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| `dispatch_mission(mission_id, model?, max_turns?)` | Start an agent |
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| `cancel_mission(mission_id)` | Stop a running agent |
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| `wait_for_mission(mission_id, timeout_seconds?)` | Block until done (prefer polling for long tasks) |
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| `get_mission_status(mission_id)` | Check progress without blocking |
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| `get_report(mission_id)` | Read structured report |
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| `get_dashboard()` | System overview |
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| `list_projects()` | Browse projects |
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| `list_missions(project_id, status?)` | List missions |
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## Guidelines
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- Always confirm the plan before dispatching unless the user said "go ahead"
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- Include mission titles and IDs when reporting status
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- If a mission fails, read its report to understand errors before retrying
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- Agent concurrency is configurable (default: 3). Excess missions queue and auto-dispatch as slots free up. Check `get_dashboard()` for slot availability.
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- Dependencies form a DAG — never create circular dependencies
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- Each agent auto-merges its worktree on completion. If a merge conflict occurs, the changes remain on the worktree branch for manual resolution.
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@@ -123,6 +123,11 @@
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},
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"description": "AI browser agent for web tasks"
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},
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"devfleet": {
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"type": "http",
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"url": "http://localhost:18801/mcp",
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"description": "Multi-agent orchestration — dispatch parallel Claude Code agents in isolated worktrees. Plan projects, auto-chain missions, read structured reports. Repo: https://github.com/LEC-AI/claude-devfleet"
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},
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"token-optimizer": {
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"command": "npx",
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"args": ["-y", "token-optimizer-mcp"],
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103
skills/claude-devfleet/SKILL.md
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skills/claude-devfleet/SKILL.md
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---
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name: claude-devfleet
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description: Orchestrate multi-agent coding tasks via Claude DevFleet — plan projects, dispatch parallel agents in isolated worktrees, monitor progress, and read structured reports.
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origin: community
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---
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# Claude DevFleet Multi-Agent Orchestration
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## When to Use
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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.
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Requires a running Claude DevFleet instance connected via MCP:
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```bash
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claude mcp add devfleet --transport http http://localhost:18801/mcp
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```
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## How It Works
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```
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User → "Build a REST API with auth and tests"
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↓
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plan_project(prompt) → project_id + mission DAG
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↓
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Show plan to user → get approval
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↓
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dispatch_mission(M1) → Agent 1 spawns in worktree
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↓
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M1 completes → auto-merge → auto-dispatch M2 (depends_on M1)
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↓
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M2 completes → auto-merge
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↓
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get_report(M2) → files_changed, what_done, errors, next_steps
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↓
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Report back to user
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```
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### Tools
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| Tool | Purpose |
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|------|---------|
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| `plan_project(prompt)` | AI breaks a description into a project with chained missions |
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| `create_project(name, path?, description?)` | Create a project manually, returns `project_id` |
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| `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. |
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| `dispatch_mission(mission_id, model?, max_turns?)` | Start an agent on a mission |
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| `cancel_mission(mission_id)` | Stop a running agent |
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| `wait_for_mission(mission_id, timeout_seconds?)` | Block until a mission completes (see note below) |
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| `get_mission_status(mission_id)` | Check mission progress without blocking |
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| `get_report(mission_id)` | Read structured report (files changed, tested, errors, next steps) |
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| `get_dashboard()` | System overview: running agents, stats, recent activity |
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| `list_projects()` | Browse all projects |
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| `list_missions(project_id, status?)` | List missions in a project |
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> **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.
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### Workflow: Plan → Dispatch → Monitor → Report
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1. **Plan**: Call `plan_project(prompt="...")` → returns `project_id` + list of missions with `depends_on` chains and `auto_dispatch=true`.
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2. **Show plan**: Present mission titles, types, and dependency chain to the user.
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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).
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4. **Monitor**: Call `get_mission_status(mission_id=...)` or `get_dashboard()` to check progress.
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5. **Report**: Call `get_report(mission_id=...)` when missions complete. Share highlights with the user.
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### Concurrency
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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.
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## Examples
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### Full auto: plan and launch
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1. `plan_project(prompt="...")` → shows plan with missions and dependencies.
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2. Dispatch the first mission (the one with empty `depends_on`).
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3. Remaining missions auto-dispatch as dependencies resolve (they have `auto_dispatch=true`).
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4. Report back with project ID and mission count so the user knows what was launched.
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5. Poll with `get_mission_status` or `get_dashboard()` periodically until all missions reach a terminal state (`completed`, `failed`, or `cancelled`).
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6. `get_report(mission_id=...)` for each terminal mission — summarize successes and call out failures with errors and next steps.
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### Manual: step-by-step control
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1. `create_project(name="My Project")` → returns `project_id`.
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2. `create_mission(project_id=project_id, title="...", prompt="...", auto_dispatch=true)` for the first (root) mission → capture `root_mission_id`.
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`create_mission(project_id=project_id, title="...", prompt="...", auto_dispatch=true, depends_on=["<root_mission_id>"])` for each subsequent task.
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3. `dispatch_mission(mission_id=...)` on the first mission to start the chain.
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4. `get_report(mission_id=...)` when done.
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### Sequential with review
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1. `create_project(name="...")` → get `project_id`.
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2. `create_mission(project_id=project_id, title="Implement feature", prompt="...")` → get `impl_mission_id`.
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3. `dispatch_mission(mission_id=impl_mission_id)`, then poll with `get_mission_status` until complete.
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4. `get_report(mission_id=impl_mission_id)` to review results.
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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.
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## Guidelines
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- Always confirm the plan with the user before dispatching, unless they said to go ahead.
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- Include mission titles and IDs when reporting status.
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- If a mission fails, read its report before retrying.
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- Check `get_dashboard()` for agent slot availability before bulk dispatching.
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- Mission dependencies form a DAG — do not create circular dependencies.
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- 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.
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- 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.
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