A 5-phase framework for reliable AI-to-AI task delegation and coordination inspired by Google DeepMind research.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install intelligent-delegation
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install intelligent-delegation using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Intelligent Delegation Framework?
This skill provides a practical implementation of the Intelligent AI Delegation framework for Openclaw Skills. It solves common multi-agent system failures such as lost background tasks, blind trust in sub-agent outputs, and the lack of systematic learning from previous errors. By establishing a rigorous protocol, this framework ensures that every delegation is tracked, verified, and optimized for performance.
Utilizing these Openclaw Skills allows developers to build more resilient agentic workflows. It shifts the paradigm from simple task decomposition to a robust ecosystem of accountability, where agents use task contracts, automated verification scripts, and adaptive fallback chains to handle complex, high-stakes environments with confidence.
Intelligent Delegation Framework Use Cases
- Managing long-running background tasks across multiple sub-agents without losing progress.
- Automating the verification of complex outputs from specialized LLM agents using programmatic checks.
- Implementing adaptive recovery protocols when primary agents encounter context overflows or timeouts.
- Building a performance-based routing system that learns which agents are best for specific technical tasks.
- Calibrating human-in-the-loop escalation for high-criticality or irreversible actions.
How Intelligent Delegation Framework Works
- Task Tracking: The agent logs all background work into a TASKS.md file and schedules a one-shot cron job to ensure completion is checked.
- Performance Logging: Every delegation outcome is recorded in a performance log to track success rates, costs, and failure modes for future routing decisions.
- Contract Specification: Before delegating, the agent defines formal contracts including machine-checkable success criteria and output locations.
- Automated Verification: The verify_task.py tool runs post-task checks (e.g., JSON validation, database row counts) to confirm the delegatee met the contract requirements.
- Adaptive Re-routing: If a task fails, the framework diagnoses the root cause and triggers a fallback chain, which might include retries, switching to a script, or escalating to a human.
Intelligent Delegation Framework Setup
To install the framework into your environment, use the following command:
clawhub install intelligent-delegation
After installation, you should integrate the delegation protocols into your AGENTS.md and add the Active Task Monitor as the first check in your HEARTBEAT.md to ensure persistent tracking. Openclaw Skills work best when these templates are consistently maintained in your workspace.
Intelligent Delegation Framework Data Schema & Taxonomy
The framework organizes its data and metadata through a set of Markdown templates and Python utilities:
| File/Component | Type | Description |
|---|---|---|
| TASKS.md | Template | Logs TASK-ID, status (RUNNING/COMPLETED/FAILED), and cron job IDs. |
| agent-performance.md | Template | Records success rates, quality scores (1-5), and failure mode heuristics. |
| verify_task.py | Tool | CLI utility for validating file existence, JSON structure, and service ports. |
| score_task.py | Tool | Calculates task complexity and criticality across 7 axes for routing. |
| fallback-chains.md | Schema | Defines the logical flow for recovery and human escalation protocols. |
Intelligent Delegation Framework Advanced Features
- Seven-axis task scoring system to determine autonomy levels and monitoring frequency.
- Automated cron-based heartbeat integration to prevent silent task failures.
- Machine-checkable verification manifests for complex multi-file output validation.
- Root-cause diagnosis table to automatically pivot between LLM retries and script-based execution.
- Integrated performance memory to prevent repeating the same delegation mistakes across sessions.
SKILL.md
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