Coordinate AI agent teams via a Kanban task board with local JSON storage. Enables multi-agent workflows with a Team Lead assigning work and Worker Agents executing tasks via heartbeat polling. Perfect for building AI agent command centers.
73
Quality
58%
Does it follow best practices?
Impact
99%
3.41xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./data/skills-md/0xindiebruh/openclaw-mission-control-skill/openclaw-mission-control/SKILL.mdWorker agent heartbeat workflow
Check tasks endpoint
30%
100%
Pick up todo tasks
0%
100%
Log progress step
50%
100%
Valid log action value
0%
100%
Complete task endpoint
0%
100%
Deliverables array
0%
100%
Check @mentions endpoint
0%
100%
Mark mentions as read
0%
100%
Five distinct steps
100%
100%
File placement
100%
100%
Without context: $0.2640 · 1m 21s · 17 turns · 19 in / 4,145 out tokens
With context: $0.4171 · 1m 22s · 20 turns · 25 in / 4,445 out tokens
Team Lead task lifecycle management
Seed call included
0%
100%
Task creation endpoint
100%
100%
createdBy field
0%
100%
assignee field
100%
100%
Valid priority values
0%
100%
Priority reflects urgency
37%
100%
Move to todo step
0%
100%
Approval via done status
0%
100%
Correct lifecycle order
70%
100%
Content-Type header
100%
87%
Multiple tasks created
100%
100%
Without context: $0.5993 · 2m · 30 turns · 29 in / 6,863 out tokens
With context: $0.3230 · 1m 6s · 14 turns · 272 in / 3,668 out tokens
Multi-agent team configuration
AGENT_CONFIG export
0%
100%
brand object present
0%
100%
Agent required fields
40%
100%
sessions.list structure
0%
100%
Worker agentDir field
0%
100%
Heartbeat polling config
0%
100%
IDs match across configs
50%
100%
Agent directory paths
0%
100%
Workspace directories
0%
100%
data/ in gitignore
0%
100%
Lead has no heartbeat
50%
100%
Without context: $0.5797 · 1m 51s · 33 turns · 1,443 in / 6,794 out tokens
With context: $0.4820 · 1m 21s · 25 turns · 30 in / 4,745 out tokens
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Table of Contents
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