Coordinate Worker and Monitor agents to automate long-running task monitoring.
npx skills add https://github.com/buer2233/my_openclaw --skill long-task-monitor
Please help me install this Agent Skill. Skill: long-task-monitor Source: https://github.com/buer2233/my_openclaw/tree/main/skills/long-task-monitor Command: npx skills add https://github.com/buer2233/my_openclaw --skill long-task-monitor
Long-running tasks in OpenClaw often lack visibility into progress and health; this Skill provides automated orchestration by launching a Worker to perform the task and a Monitor that periodically reports status back to the main session, enabling proactive control and recovery.
Start a new long-running task by creating a Worker, then spawn a Monitor and supply their session keys when available.
High-intent search queries and answers about installing and using this skill.
You can monitor long-running tasks in OpenClaw by using a worker-monitor orchestration that launches a dedicated Worker to execute the task and a Monitor to periodically poll and report progress back to your main session.
Automated progress reporting for long-running AI agent sessions works by having a Monitor agent poll the Worker's status on a fixed cadence and send updates via Announce to the main session every 10 minutes or upon task completion.
To set up long-task monitoring, you need a local Long Tasks registry under ~/.openclaw/workspace/long-tasks, a Worker session key, a Monitor session key, and the hook-logger plugin installed for status insights.
Yes, the Monitor reports status to the main session on completion and includes automatic retry logic for transient failures, providing proactive alerts and recovery control for model training or data processing jobs.
After a long-running task finishes, the orchestration supports manual cleanup of the Worker and Monitor session keys to clear the completed job and free up the local long-tasks registry resources.
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