Why is polling for agent status discouraged?

Polling manually with commands like 'sleep' or 'tail' wastes tokens on repeated checks and can clutter your current session context with unnecessary transcripts.

How does this skill save tokens?

By relying on free system reminders instead of active 'TaskOutput' calls, you avoid sending the entire agent transcript back into your main context repeatedly.

What are Background Agent Pings?

They are native system notifications within Claude Code that alert you when a background agent makes progress, uses tools, or completes its assigned task.

Can I perform other tasks while an agent is running?

Yes, this skill is specifically designed to let you continue productive work immediately after launching a background task, as the system will notify you when it's time to check results.

Background Agent Monitoring

Name: Background Agent Monitoring
Author: scooter-lacroix

byscooter-lacroix

生産性とワークフロー

Optimizes asynchronous workflows by using system notifications instead of polling to track background agent progress.

概要

The Background Agent Pings skill implements an efficient management pattern for background tasks in Claude Code. By leveraging native system reminders rather than manual polling, it allows developers to initiate long-running tasks and immediately move on to other work. This approach prevents context flooding from repeated task output checks and significantly reduces token consumption, ensuring that the main development thread remains clean and productive while background agents operate autonomously.

主な機能

0 GitHub stars
Prevents main context flooding from task transcripts
Optimizes concurrent multi-tasking workflows
Leverages native system notifications for progress
Streamlines background agent output retrieval
Eliminates token-wasting status polling

ユースケース

Maintaining focus on primary tasks during long-running AI operations
Managing multiple autonomous agents without manual oversight
Running extensive code refactoring tasks in the background

概要

主な機能

0 GitHub stars
Prevents main context flooding from task transcripts
Optimizes concurrent multi-tasking workflows
Leverages native system notifications for progress
Streamlines background agent output retrieval
Eliminates token-wasting status polling

ユースケース

Maintaining focus on primary tasks during long-running AI operations
Managing multiple autonomous agents without manual oversight
Running extensive code refactoring tasks in the background