Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
A 5-phase framework for AI-to-AI task delegation based on Google DeepMind's Intelligent AI Delegation paper (arXiv 2602.11865). Covers task tracking, sub-agent performance logging, automated output verification, fallback chains, and systematic agent selection via multi-axis scoring.
Replaces ad-hoc delegation with a structured, self-correcting system that logs outcomes, enforces contracts, and routes failures automatically rather than silently dropping them.
When to use it
- Tracking background sub-agent tasks so none go unmonitored
- Choosing which sub-agent to assign a task based on past performance
- Automatically verifying sub-agent output before accepting results
- Recovering from delegation failures without manual intervention
- Scoring task complexity and reversibility to decide autonomy level
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: delegate a data extraction task to a sub-agent and ensure results are valid
- 1Score the task on 7 axes using score_task.py to determine autonomy level and agent choice
- 2Write a task contract specifying expected output format, success criteria, and fallback steps
- 3Log the task to TASKS.md and schedule a cron job to check completion
- 4On completion, run verify_task.py to confirm file exists and passes structural checks
- 5If verification fails, apply the fallback chain: retry with adjusted prompt, then try a different agent, then escalate
Verified output file at expected path, task marked COMPLETED in TASKS.md, outcome logged to agent-performance.md