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:
- Check prerequisites (Tailscale, gh CLI)
- Copy dashboard files to your workspace
- Create the config file (
~/.clawdbot/mission-control.json
)
- Install the webhook transform
- Set up GitHub webhook
- Push to GitHub and enable Pages
That's it. The agent handles everything.
Prerequisites
Before setup, you need:
| Requirement | Check | Install |
|---|
| Tailscale | tailscale status
| brew install tailscale
or tailscale.com/download |
| Tailscale Funnel | tailscale funnel status
| tailscale funnel 18789
(one-time) |
| GitHub CLI | gh auth status
| brew install gh && gh auth login
|
If any are missing, tell the agent — it will guide you through installation.
How It Works
- Dashboard — Web UI hosted on GitHub Pages where humans manage tasks
- Webhook — GitHub sends push events to Clawdbot when tasks change
- Transform — Compares old vs new tasks.json, detects status changes
- 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
| Status | Meaning |
|---|
permanent
| Recurring tasks (daily checks, etc.) |
backlog
| Waiting to be worked on |
in_progress
| Agent is working on this |
review
| Done, awaiting human approval |
done
| Completed and approved |
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"):
- Read — Check title, description, subtasks, dod
- Mark started —
mc-update.sh start <task_id>
- Execute — Work through subtasks, mark each done
- Document — Add progress comments
- Complete —
mc-update.sh complete <task_id> "Summary"
Handling Rework
If a completed task is moved back to "In Progress" with a new comment:
- Read the feedback comment
- Address the issues
- Add a comment explaining your changes
- Move back to Review
EPICs
EPICs are parent tasks with multiple child tickets. When you receive an EPIC:
- Child tickets are listed in the subtasks (format:
MC-XXX-001: Title
)
- Work through them sequentially (1 → 2 → 3...)
- After each child: comment result, set to "review", mark EPIC subtask done
- After last child: set EPIC to "review"
Heartbeat Integration
Add to your
HEARTBEAT.md
:
## Task Check
- Check
data/tasks.json
for tasks in "in_progress"
- Flag tasks with
processingStartedAt
but no recent activity
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
| File | Purpose |
|---|
<workspace>/index.html
| Dashboard UI |
<workspace>/data/tasks.json
| Task data |
<skill>/scripts/mc-update.sh
| CLI tool |
~/.clawdbot/mission-control.json
| Config |
~/.clawdbot/hooks-transforms/github-mission-control.mjs
| Webhook transform |
