heartbeat-manager

Manage periodic execution cycles for AI agents using configuration files.

6|2|Updated Jan 29, 2026

Search Tags:#automation #monitoring #agent #maintenance #scheduling #heartbeat

Ready to run

npx skills add https://github.com/filipexyz/ravi --skill heartbeat-manager-filipexyz

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: heartbeat-manager
Source: https://github.com/filipexyz/ravi/tree/main/src/plugins/internal/ravi-system/skills/heartbeat
Command: npx skills add https://github.com/filipexyz/ravi --skill heartbeat-manager-filipexyz

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the issue of agent inactivity and lack of oversight by enabling autonomous, periodic check-ins that ensure agents remain responsive and proactive.

Core Features & Use Cases

Periodic Check-ins: Configure automated heartbeat intervals for agents to perform routine tasks like log monitoring or status reporting.
Contextual Execution: Agents utilize a dedicated HEARTBEAT.md file to define specific actions to take during each check-in.
Use Case: Configure a background agent to perform a health check every hour, verifying that all system tasks are running and sending a summary report if any errors are detected.

Quick Start

Enable the heartbeat for the main agent with a thirty minute interval by running the heartbeat enable main 30m command.

Frequently Asked Questions about heartbeat-manager

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

FAQPage Schema

How do I automate periodic check-ins and status reporting for AI agents?▼

No, you need the Ravi runtime daemon to schedule and trigger execution prompts at defined intervals. The daemon works with agent-specific configuration files and a dedicated HEARTBEAT.md file to facilitate automated status reporting and routine check-ins.

How do I configure an hourly health check for a background agent?▼

You can configure an hourly health check by enabling the heartbeat for your agent with a sixty minute interval. The agent then uses its HEARTBEAT.md file to define specific actions, like verifying system tasks and sending summary reports if errors are detected.

What is the best way to monitor AI agent inactivity and ensure continuous oversight?▼

The best way to monitor agent inactivity is to enable autonomous, periodic check-ins that ensure agents remain responsive and proactive. This solves the lack of oversight by facilitating automated status reporting and error monitoring based on configuration files.

Can I use a HEARTBEAT.md file to define specific maintenance tasks during automated check-ins?▼

Yes, agents utilize a dedicated HEARTBEAT.md file to define specific actions to take during each check-in. This allows you to configure routine tasks like log monitoring, verifying running system tasks, and sending error reports.

Do I need a specific runtime environment to schedule automated agent heartbeat cycles?▼

Yes, you need the Ravi runtime daemon to schedule and trigger execution prompts at defined intervals. This daemon manages the periodic execution cycles required for continuous operational awareness and task maintenance.