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mission-control

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).

作者: admin | 来源: ClawHub
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ClawHub
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V 2.3.1
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mission-control

# 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 |

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skill ai

通过对话安装

该技能支持在以下平台通过对话安装:

OpenClaw WorkBuddy QClaw Kimi Claude

方式一:安装 SkillHub 和技能

帮我安装 SkillHub 和 mission-control-1776107355 技能

方式二:设置 SkillHub 为优先技能安装源

设置 SkillHub 为我的优先技能安装源,然后帮我安装 mission-control-1776107355 技能

通过命令行安装

skillhub install mission-control-1776107355

下载 Zip 包

⬇ 下载 mission-control v2.3.1

文件大小: 84.42 KB | 发布时间: 2026-4-14 14:10

v2.3.1 最新 2026-4-14 14:10
Fix: Renamed from 'Jeannie Control' to 'Mission Control'. Removed private cron data from demo crons.json. Clean demo data only.

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