A professional framework for AI agents to dispatch, monitor, and verify subagent tasks through proactive intervention and evidence-based reporting.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install supervising-agents
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 supervising-agents 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 Supervising Agents Skill?
The Supervising Agents skill is a robust protocol designed for complex AI workflows where a primary agent must delegate tasks to subagents or parallel workers. It operates on the core principle that the dispatcher owns the outcome, providing a structured methodology to move beyond passive waiting. By implementing this skill, agents gain the ability to proactively manage the lifecycle of delegated tasks, ensuring high-quality output and technical accountability.
Built for high-stakes autonomous environments, this skill provides the necessary checks and balances to prevent common subagent failures like silent stalling, fake completion, or scope drift. It integrates seamlessly into the Openclaw Skills ecosystem to provide a standardized way for agents to act as effective project managers and quality assurance leads.
Supervising Agents Skill Use Cases
- Orchestrating multi-agent development projects where tasks are split across specialized sub-workers.
- Managing long-running autonomous research or coding tasks that require periodic status probes.
- Implementing quality control gates for automated PR reviews or complex documentation generation.
- Handling background processes where immediate intervention is required if progress stalls.
How Supervising Agents Skill Works
- PREPARE: The supervisor defines the goal, requirements, and specific deliverables while planning the verification strategy.
- DISPATCH: Tasks are assigned using a commitment-lock template that forces the subagent to confirm its understanding and plan.
- MONITOR: The supervisor performs active file watching and sends periodic status probes rather than waiting for the subagent to finish.
- INTERVENE: If red flags are detected, the supervisor uses standardized levels of intervention (Probe, Redirect, or Takeover).
- VERIFY: All deliverables are checked against a strict verification gate requiring concrete evidence like file content or test results.
- REPORT: A final accountability report is generated for the human user, detailing the evidence and status of the task.
Supervising Agents Skill Setup
To enable this skill within your environment, use the following command:
/load supervising-agents
To ensure all your agents utilize this protocol by default, add it to your environment configuration:
export CLAW_SKILLS=supervising-agents
Supervising Agents Skill Data Schema & Taxonomy
The skill organizes monitoring data and task tracking using the following structure:
| Data Component | Description | Format |
|---|---|---|
| Task Registry | Tracks agent IDs, budgets, and check intervals | JSON Map |
| Monitoring Log | Records file modification times and size changes | Time-series List |
| Verification Matrix | Compares requirements against actual evidence | Markdown Table |
| Reporting Template | Standardized status output for human review | Markdown |
It utilizes tools like SendMessage for communication and filesystem utilities (ls, wc, grep) for non-invasive progress tracking.
Supervising Agents Skill Advanced Features
- Multi-Level Intervention Logic: Automatically escalates from simple status probes to full task takeover based on subagent responsiveness.
- Automated File Watching: Monitors expected output directories for changes, detecting stalled subagents within minutes of inactivity.
- Evidence-Based Verification: Requires actual file content analysis and test execution rather than accepting verbal confirmation of completion.
- Scope Drift Detection: Analyzes subagent output against original requirements to catch unrelated activity early in the process.
- Integrated Time Budgeting: Manages check intervals dynamically based on the total task budget and complexity within the Openclaw Skills framework.
SKILL.md
Loading
Related Openclaw Skills
SpielerPlus Scraper
A specialized scraper for the SpielerPlus and TeamPlus platforms designed to extract team events, member data, and financial records.
Slack Automation Skill
A specialized browser automation skill designed to interact with Slack workspaces for messaging, data extraction, and workflow automation.
Umnix Platform Skill
A comprehensive educational framework for understanding AI agent governance, identity verification, and constitutional accountability.
Job Autopilot Bundle
A comprehensive automation suite that manages the entire job application lifecycle from initial search to final submission.
Job Autopilot - Resume Tailor
An AI-powered skill that automatically tailors resumes and cover letters to specific job descriptions while maintaining 100% factual accuracy.
Memory Optimization Skill
A sophisticated memory management system that scores, prunes, and archives AI agent records to maintain high-relevance context and optimal token efficiency.
Featured*
