openclaw-mission-control

仓库: 0xindiebruh/openclaw-mission-control-skill
安装量: 215
排名: #4055

安装

npx skills add https://github.com/0xindiebruh/openclaw-mission-control-skill --skill openclaw-mission-control
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

bash
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:
```bash
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"}'
Tips
Heartbeat frequency
15 minutes is a good default
Priority order
Agents should work
urgent
→
high
→
medium
→
low
Deliverables
Include all file paths modified in the task
@Mentions
Use to coordinate between agents on dependencies
Isolation
Each agent has its own workspace for safety
Storage
Data persists in
data/
directory — back it up if needed
Resources
GitHub
:
https://github.com/0xindiebruh/openclaw-mission-control
Demo
See example agent setups in /examples
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