name: mission-control description: Kanban-style task management dashboard for AI assistants. Manage tasks via CLI or dashboard UI. Use when user mentions tasks, kanban, task board, mission control, or wants to track work items with status columns (backlog, in progress, review, done). homepage: https://github.com/rdsthomas/mission-control metadata: {"clawdbot": {"emoji": "🎛️"}}

Mission Control — Task Management for AI Assistants

A Kanban-style task board that you (the AI assistant) manage. Your human creates and prioritizes tasks via the web dashboard; you execute them automatically when they're moved to "In Progress".

🚀 Quick Start

Just say: "Set up Mission Control for my workspace"

The agent will:

1. Check prerequisites (Tailscale, gh CLI)
2. Copy dashboard files to your workspace
3. Create the config file (~/.clawdbot/mission-control.json)
4. Install the webhook transform
5. Set up GitHub webhook
6. Push to GitHub and enable Pages

That's it. The agent handles everything.

Prerequisites

Before setup, you need:

If any are missing, tell the agent — it will guide you through installation.

How It Works

1. Dashboard — Web UI hosted on GitHub Pages where humans manage tasks
2. Webhook — GitHub sends push events to Clawdbot when tasks change
3. Transform — Compares old vs new tasks.json, detects status changes
4. Auto-Processing — When a task moves to "In Progress", the agent starts working

The Flow

Human moves task → GitHub push → Webhook → Transform → Agent receives work order
      ↓                                                         ↓
   Dashboard                                              Executes task
      ↓                                                         ↓
Agent updates status ← Commits changes ← Marks subtasks done ←─┘

Task Structure

Tasks live in <workspace>/data/tasks.json:

{
  "id": "task_001",
  "title": "Implement feature X",
  "description": "Detailed context for the agent",
  "status": "backlog",
  "subtasks": [
    { "id": "sub_001", "title": "Research approach", "done": false },
    { "id": "sub_002", "title": "Write code", "done": false }
  ],
  "priority": "high",
  "dod": "Definition of Done - what success looks like",
  "comments": []
}

Status Values

CLI Commands

Use <skill>/scripts/mc-update.sh for task updates:

# Status changes
mc-update.sh status <task_id> review
mc-update.sh status <task_id> done

# Comments
mc-update.sh comment <task_id> "Progress update..."

# Subtasks
mc-update.sh subtask <task_id> sub_1 done

# Complete (moves to review + adds summary)
mc-update.sh complete <task_id> "Summary of what was done"

# Push to GitHub
mc-update.sh push "Commit message"

Agent Workflow

When you receive a task (moved to "In Progress"):

1. Read — Check title, description, subtasks, dod
2. Mark started — mc-update.sh start <task_id>
3. Execute — Work through subtasks, mark each done
4. Document — Add progress comments
5. Complete — mc-update.sh complete <task_id> "Summary"

Handling Rework

If a completed task is moved back to "In Progress" with a new comment:

1. Read the feedback comment
2. Address the issues
3. Add a comment explaining your changes
4. Move back to Review

EPICs

EPICs are parent tasks with multiple child tickets. When you receive an EPIC:

1. Child tickets are listed in the subtasks (format: MC-XXX-001: Title)
2. Work through them sequentially (1 → 2 → 3...)
3. After each child: comment result, set to "review", mark EPIC subtask done
4. After last child: set EPIC to "review"

Heartbeat Integration

Add to your HEARTBEAT.md:

## Task Check

1. Check `data/tasks.json` for tasks in "in_progress"
2. Flag tasks with `processingStartedAt` but no recent activity
3. Check "review" tasks for new feedback comments

Configuration

Config lives in ~/.clawdbot/mission-control.json. See assets/examples/CONFIG-REFERENCE.md for all options.

Minimal config (set by agent during setup):

{
  "gateway": { "hookToken": "your-token" },
  "workspace": { "path": "/path/to/workspace" },
  "slack": { "botToken": "xoxb-...", "channel": "C0123456789" }
}

Troubleshooting

See docs/TROUBLESHOOTING.md for common issues:

• Dashboard shows sample data → Connect GitHub token
• Webhook not triggering → Check Tailscale Funnel
• Changes not appearing → GitHub Pages cache (wait 1-2 min)

Security

Mission Control is a task management system for AI agents — its core purpose is to pass human-authored task descriptions to an agent for execution. This is by design, not a vulnerability.

Trust Model

• Single-user / trusted-user setup: Task authors are the same people who control the agent. The trust boundary is identical to typing a message directly to your assistant.
• Multi-user setups: If multiple users can create tasks on the dashboard, treat task content as untrusted input. Use Clawdbot's agent sandbox and permission model to limit what the agent can do.

Mitigations

• Input sanitization: mc-update.sh validates all inputs against injection patterns before passing them to Python or git.
• No credential storage: The dashboard stores no tokens or secrets — all auth is handled by Clawdbot's config.
• Webhook HMAC verification: The transform module validates webhook signatures using timingSafeEqual to prevent tampering.
• Security scan on sync: The sync-to-opensource.sh script scans for leaked credentials before publishing.

Recommendations

• Keep your dashboard repository private if you don't want others to see your task data.
• Review task descriptions before moving them to "In Progress" if the task was created by someone else.
• Use Clawdbot's groupPolicy and allowFrom settings to restrict who can interact with the agent.

Files