agent-signal

Manage AI agent signal lifecycles with event-driven handlers in LobeHub.

Updated Jul 5, 2026
One-click install
npx skills add https://github.com/xkl2013/lobe --skill agent-signal-xkl2013
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-signal
Source: https://github.com/xkl2013/lobe/tree/main/.agents/skills/agent-signal
Command: npx skills add https://github.com/xkl2013/lobe --skill agent-signal-xkl2013

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill unit provides the infrastructure to create and manage event-driven background work for AI agents within the LobeHub system, enabling efficient and scalable AI operations.

Core Features & Use Cases

  • Event-Driven Workflows: Design and implement workflows that react to events without being tied to foreground chat requests.
  • Signal Interpretation: Derive semantic interpretations from events and define actions based on these signals.
  • Action Execution: Execute concrete side effects based on signals, such as processing data or triggering other services.
  • Policy Management: Create and install middleware bundles that register source, signal, and action handlers.
  • Use Case: Use this Skill to automate the handling of user feedback, such as analyzing user satisfaction and storing feedback in a database.

Quick Start

Use the agent-signal skill to create a new policy for handling user feedback by defining the source event, signal interpretation, and action execution steps.

Frequently Asked Questions about agent-signal

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build event-driven background tasks for AI agents?

To build event-driven background tasks for AI agents, you manage the AI agent signal lifecycle by implementing source handlers to capture events, signal handlers for interpretation, and action handlers to execute side effects.

What is signal interpretation in AI workflow management?

Signal interpretation in AI workflow management is deriving semantic meaning from captured events. You define specific actions based on these interpreted signals to execute concrete side effects, such as processing data or triggering external services.

How do I configure event-driven workflows using policy management?

You configure event-driven workflows using policy management by creating and installing middleware bundles. These bundles systematically register your custom source, signal, and action handlers to govern the background task lifecycle.

Can I automate user feedback processing with event-driven AI pipelines?

Yes, you can automate user feedback processing with event-driven AI pipelines. You configure a policy to capture feedback events, interpret user satisfaction signals, and execute actions like storing the analyzed data in a database.

Does this event-driven workflow approach require foreground chat requests?

No, this event-driven workflow approach operates entirely in the background and is not tied to foreground chat requests. It enables scalable AI operations by reacting to events asynchronously through installed signal policies.

What are the limitations of managing background tasks through signal handlers?

Managing background tasks through signal handlers requires implementing separate source, signal, and action handlers for each workflow. This policy management approach demands explicit handler registration and is not suited for synchronous foreground processing.