据 AlphaMoat 统计,截至 2026-10,Mission Control 累计下载 17,211 次、获 12 个星标,按累计下载量在本次参与排名的 10,914 个 AI 技能中排名第 1,638 位,在「AI 智能体」分类(1,632 个)中排名第 231 位。
列表默认按累计下载量排序,也可按星标排序;详情页排名按累计下载量计算。
| 平台 | 累计下载 | 星标 | 评论 |
|---|---|---|---|
| ClawHub ↗ | 1.5万 | 12 | 1 |
| SkillHub ↗ | 1.7万 | 12 | — |
各平台的计数口径不同,统计时间不一致。
GitHub Pages 仪表板保存任务,推送 webhook 检测状态变化,任务进入进行中时启动处理。CLI 记录子任务和评论,完成后先进入待审核,人工批准后才标记完成。设置需 Tailscale、GitHub CLI 和 webhook;多用户环境应将任务内容视为不可信并限制代理权限。
资料发布于 2026-10-27
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-10 | 1.7万 | — | 12 |
| 2026-09 | 1.5万 | — | 12 |
| 2026-08 | 1.5万 | +9.1% | 12 |
| 2026-07 | 1.3万 | — | 12 |
| 2026-05 | 1.1万 | — | 11 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
---
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:
| Requirement | Check | Install |
|-------------|-------|---------|
| **Tailscale** | `tailscale status` | `brew install tailscale` or [tailscale.com/download](https://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
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`:
```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:
```bash
# 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`:
```markdown
## 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):
```json
{
"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 |
把开发过程中的经验、错误与纠正记录为 Markdown 文件,供后续复用并晋升为工作区记忆。
用自然语言描述需求,在本地与远程技能市场里找技能并给出推荐的技能。
给 AI agent 用的浏览器自动化命令行技能。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
一份为 AI 代理规划主动跟进、工作记忆与复盘流程的工作区配置指南。
指导如何创建与更新有效 Claude 技能的写作指南。
在安装第三方技能之前,按清单审查来源、权限与危险模式的技能。
把盛唐诗人李白的诗学观、意象系统与口吻,蒸馏成 AI 可调用的角色包。
数据月份:2026-10 · 下载/星标数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。