Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rdsthomas/mission-control --skill mission-controlgit clone --depth 1 https://github.com/rdsthomas/mission-controlWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rdsthomas/mission-control/mission-control)<a href="https://agentmods.dev/skills/rdsthomas/mission-control/mission-control"><img src="https://agentmods.dev/badge/skills/rdsthomas/mission-control/mission-control/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rdsthomas/mission-control/mission-control"><img src="https://agentmods.dev/badge/skills/rdsthomas/mission-control/mission-control.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.01708 |
| Opus 5.5 | $0.00022 | $0.00683 |
| Sonnet 5.5 | $0.00011 | $0.00342 |
| Haiku 4.5 | $0.00006 | $0.00171 |
Grade A, and why
mission-control scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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": []
}
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/.github/workflows/deploy.yml 717 B
- assets/data/tasks.json 7.1 KB
- assets/data/version.json 83 B
- assets/examples/clawdbot-hooks-config.json 148 B
- assets/examples/CONFIG-REFERENCE.md 2.7 KB
- assets/examples/HOOKS-CONFIG.md 1.8 KB
- assets/examples/mission-control.json 673 B
- assets/index.html 284 KB
- assets/scripts/mc-update.sh 9.6 KB runs code
- assets/transforms/github-mission-control.mjs 22 KB runs code
- data/demo-crons.json 460 B
- data/demo-tasks.json 24 KB
- data/skills.json 1.6 KB
- scripts/cors-proxy.js 1.2 KB runs code
- scripts/mc-setup.sh 1.8 KB runs code
- scripts/mc-update.sh 9.6 KB runs code
- scripts/update-version.sh 842 B runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 221 lines · 56 tokens per session scan A 5dfdfad27e0f
mission-control is a skill published in the GitHub repository rdsthomas/mission-control (24 stars, last pushed 8mo ago), licensed MIT. It adds 56 tokens to every session and 1,708 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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