SKILL.md

Mission Control

Coordinate a team of AI agents using a Kanban-style task board with HTTP API.

Overview

Mission Control lets you run multiple AI agents that collaborate on tasks:

Team Lead: Creates and assigns tasks, reviews completed work

Worker Agents: Poll for tasks via heartbeat, execute work, log progress

Kanban Board: Visual task management at http://localhost:8080

HTTP API: Agents interact via REST endpoints

Local Storage: All data stored in JSON files — no external database needed

Quick Start

1. Install the Kanban Board

# Clone the Mission Control app

git clone https://github.com/0xindiebruh/openclaw-mission-control.git

cd mission-control

# Install dependencies

npm install

# Start the server

npm run dev

The board runs at http://localhost:8080.

2. Configure Your Agents

Edit lib/config.ts to define your agent team:

export const AGENT_CONFIG = {

  brand: {

    name: "Mission Control",

    subtitle: "AI Agent Command Center",

  },

  agents: [

    {

      id: "lead",

      name: "Lead",

      emoji: "🎯",

      role: "Team Lead",

      focus: "Strategy, task assignment",

    },

    {

      id: "writer",

      name: "Writer",

      emoji: "✍️",

      role: "Content",

      focus: "Blog posts, documentation",

    },

    {

      id: "growth",

      name: "Growth",

      emoji: "🚀",

      role: "Marketing",

      focus: "SEO, campaigns",

    },

    {

      id: "dev",

      name: "Dev",

      emoji: "💻",

      role: "Engineering",

      focus: "Features, bugs, code",

    },

    {

      id: "ux",

      name: "UX",

      emoji: "🎨",

      role: "Product",

      focus: "Design, activation",

    },

    {

      id: "data",

      name: "Data",

      emoji: "📊",

      role: "Analytics",

      focus: "Metrics, reporting",

    },

  ] as const,

};

3. Seed the Database (First Run)

Initialize the agents in the database:

curl -X POST http://localhost:8080/api/seed

This creates agent records from your lib/config.ts configuration. Safe to run multiple times — it only adds missing agents.

4. Configure OpenClaw Multi-Agent Mode

Add each agent to your ~/.openclaw/config.json:

{

  "sessions": {

    "list": [

      {

        "id": "main",

        "default": true,

        "name": "Lead",

        "workspace": "~/.openclaw/workspace"

      },

      {

        "id": "writer",

        "name": "Writer",

        "workspace": "~/.openclaw/workspace-writer",

        "agentDir": "~/.openclaw/agents/writer/agent",

        "heartbeat": {

          "every": "15m"

        }

      },

      {

        "id": "growth",

        "name": "Growth",

        "workspace": "~/.openclaw/workspace-growth",

        "agentDir": "~/.openclaw/agents/growth/agent",

        "heartbeat": {

          "every": "15m"

        }

      },

      {

        "id": "dev",

        "name": "Dev",

        "workspace": "~/.openclaw/workspace-dev",

        "agentDir": "~/.openclaw/agents/dev/agent",

        "heartbeat": {

          "every": "15m"

        }

      }

    ]

  }

}

Key fields:

id: Unique agent identifier (must match an agent ID in lib/config.ts)

workspace: Agent's working directory for files

agentDir: Contains SOUL.md, HEARTBEAT.md, and agent personality

heartbeat.every: Polling frequency (e.g., 5m, 15m, 1h)

5. Set up Agent Heartbeats

Each worker agent needs a HEARTBEAT.md in their agentDir:

# Agent Heartbeat

## Step 1: Check for Tasks

curl "http://localhost:8080/api/tasks/mine?agent=writer"

Step 2: Pick up todo tasks

curl -X POST "http://localhost:8080/api/tasks/{TASK_ID}/pick" \

  -H "Content-Type: application/json" \

  -d '{"agent": "writer"}'

Step 3: Log Progress

curl -X POST "http://localhost:8080/api/tasks/{TASK_ID}/log" \

  -H "Content-Type: application/json" \

  -d '{"agent": "writer", "action": "progress", "note": "Working on..."}'

Step 4: Complete Tasks

curl -X POST "http://localhost:8080/api/tasks/{TASK_ID}/complete" \

  -H "Content-Type: application/json" \

  -d '{

    "agent": "writer",

    "note": "Completed! Summary...",

    "deliverables": ["path/to/output.md"]

  }'

Step 5: Check for @Mentions

curl "http://localhost:8080/api/mentions?agent=writer"

Mark as read when done.

Create the agent directories:

mkdir -p ~/.openclaw/agents/{writer,growth,dev,ux,data}/agent

mkdir -p ~/.openclaw/workspace-{writer,growth,dev,ux,data}


## Task Lifecycle

backlog → todo → in_progress → review → done

│ │ │ │

│ │ │ └─ Team Lead approves

│ │ └─ Agent completes (→ review)

│ └─ Agent picks up (→ in_progress)

└─ Team Lead prioritizes (→ todo)


## Team Lead Operations

### Creating a Task

curl -X POST http://localhost:8080/api/tasks \

-H "Content-Type: application/json" \

-d '{

"title": "Task title",

"description": "Detailed description",

"priority": "high",

"assignee": "writer",

"tags": ["tag1", "tag2"],

"createdBy": "lead"


**Priority:** `urgent`, `high`, `medium`, `low`

### Moving to Todo

curl -X PATCH "http://localhost:8080/api/tasks/{id}" \

-H "Content-Type: application/json" \

-d '{"status": "todo"}'


### Approving Completed Work

curl -X PATCH "http://localhost:8080/api/tasks/{id}" \

-H "Content-Type: application/json" \

-d '{"status": "done"}'


### Adding Deliverable Path

curl -X PATCH "http://localhost:8080/api/tasks/{id}" \

-H "Content-Type: application/json" \

-d '{"deliverable": "path/to/file.md"}'


## Worker Agent Operations

### Picking Up Tasks

curl -X POST "http://localhost:8080/api/tasks/{id}/pick" \

-H "Content-Type: application/json" \

-d '{"agent": "{AGENT_ID}"}'


### Logging Progress

curl -X POST "http://localhost:8080/api/tasks/{id}/log" \

-H "Content-Type: application/json" \

-d '{

"agent": "{AGENT_ID}",

"action": "progress",

"note": "Updated the widget component"


**Actions:** `picked`, `progress`, `blocked`, `completed`

### Completing a Task

curl -X POST "http://localhost:8080/api/tasks/{id}/complete" \

-H "Content-Type: application/json" \

-d '{

"agent": "{AGENT_ID}",

"note": "Completed! Summary of changes...",

"deliverables": ["docs/api.md", "src/feature.js"]


Deliverables render as markdown in the task view.

## Comments & @Mentions

### Adding a Comment

curl -X POST "http://localhost:8080/api/tasks/{id}/comments" \

-H "Content-Type: application/json" \

-d '{

"author": "agent-id",

"content": "Hey @other-agent, need your input here"


### Checking for @Mentions

curl "http://localhost:8080/api/mentions?agent={AGENT_ID}"


### Marking Mentions as Read

curl -X POST "http://localhost:8080/api/mentions/read" \

-H "Content-Type: application/json" \

-d '{"agent": "{AGENT_ID}", "all": true}'


## API Reference

### Tasks

| Endpoint | Method | Description |
| --- | --- | --- |
| `/api/tasks` | GET | List all tasks |
| `/api/tasks` | POST | Create new task |
| `/api/tasks/{id}` | GET | Get task detail |
| `/api/tasks/{id}` | PATCH | Update task fields |
| `/api/tasks/{id}` | DELETE | Delete task |
| `/api/tasks/mine?agent={id}` | GET | Agent's assigned tasks |
| `/api/tasks/{id}/pick` | POST | Agent picks up task |
| `/api/tasks/{id}/log` | POST | Log work action |
| `/api/tasks/{id}/complete` | POST | Complete task (→ review) |
| `/api/tasks/{id}/comments` | POST | Add comment |

### Agents & System

| Endpoint | Method | Description |
| --- | --- | --- |
| `/api/agents` | GET | List all agents |
| `/api/seed` | POST | Initialize agents (first run) |
| `/api/mentions?agent={id}` | GET | Get unread @mentions |
| `/api/mentions/read` | POST | Mark mentions as read |

### Files

| Endpoint | Method | Description |
| --- | --- | --- |
| `/api/files/{path}` | GET | Read deliverable content |

## Recommended Agent Team Structure

| Agent | Role | Responsibilities |
| --- | --- | --- |
| **Lead** | Team Lead | Strategy, task creation, approvals |
| **Writer** | Content | Blog posts, documentation, copy |
| **Growth** | Marketing | SEO, campaigns, outreach |
| **Dev** | Engineering | Features, bugs, code |
| **UX** | Product | Design, activation, user flows |
| **Data** | Analytics | Metrics, reports, insights |

## Configuration

### Environment Variables

Create `.env` in your Mission Control app directory (optional):

PORT=8080


### Data Storage

All data is stored locally in the `data/` directory:

| File | Contents |
| --- | --- |
| `data/tasks.json` | All tasks, comments, work logs |
| `data/agents.json` | Agent status and metadata |
| `data/mentions.json` | @mention notifications |

Add `data/` to your `.gitignore` — user data shouldn't be committed.

## Example: Running a Multi-Agent Workflow

-

**Lead creates task:**

curl -X POST http://localhost:8080/api/tasks \

-H "Content-Type: application/json" \

-d '{"title": "Write Q1 Report", "assignee": "writer", "priority": "high"}'


-

**Lead moves to todo:**

curl -X PATCH http://localhost:8080/api/tasks/123 \

-d '{"status": "todo"}'


-

**Writer picks up via heartbeat:**

curl -X POST http://localhost:8080/api/tasks/123/pick \

-d '{"agent": "writer"}'


-

**Writer completes:**

curl -X POST http://localhost:8080/api/tasks/123/complete \

-d '{"agent": "writer", "deliverables": ["reports/q1.md"]}'


-

**Lead reviews and approves:**

curl -X PATCH http://localhost:8080/api/tasks/123 \

-d '{"status": "done"}'

openclaw-mission-control

SKILL.md

Mission Control

Overview

Quick Start

1. Install the Kanban Board

2. Configure Your Agents

3. Seed the Database (First Run)

4. Configure OpenClaw Multi-Agent Mode

5. Set up Agent Heartbeats

Step 2: Pick up todo tasks

Step 3: Log Progress

Step 4: Complete Tasks

Step 5: Check for @Mentions

Let your agent run on any real-world website

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