An automated health monitor for AI coding agents that tracks work intensity and triggers natural rest reminders.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mindbreak
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 mindbreak 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 MindBreak?
MindBreak is a sophisticated health-focused monitoring tool designed for developers and knowledge workers using AI agents. By integrating a background hook script, it meticulously tracks user activity timestamps during intense sessions such as coding, debugging, or research. This ensures that high-intensity productivity doesn't come at the expense of physical well-being by injecting subtle, non-intrusive reminders into the conversation flow.
As a vital addition to your collection of Openclaw Skills, MindBreak identifies continuous work segments and calculates specific thresholds for light breaks, meal times, or end-of-day wrap-ups. It operates silently in the background, ensuring the AI agent remains a supportive partner in maintaining a healthy work-life balance without disrupting the primary technical task.
MindBreak Use Cases
- Preventing burnout during long coding sessions by receiving a light nudge to stretch.
- Getting a summary of current progress right before a meal break to ensure a smooth transition back to work later.
- Managing overtime by receiving an end-of-day wrap-up reminder when working late into the night.
- Maintaining focus by tracking continuous work segments and only triggering reminders after 45 minutes of sustained activity.
How MindBreak Works
- A hook script executes on every user message to record a Unix timestamp in a local activity log.
- The system calculates the continuous work duration, where any gap of less than 15 minutes is considered part of the same work segment.
- If specific duration thresholds and cooldown periods are met, the hook generates a trigger signal (LIGHT, MEAL, or OVERTIME).
- The trigger signal is passed to the AI agent via tags in the user-prompt-submit-hook feedback.
- The AI agent appends a contextually relevant, natural-sounding reminder as the final paragraph of its response.
MindBreak Setup
To integrate this into your environment and start using Openclaw Skills for health monitoring, follow these steps:
- Create the hook script directory and file:
mkdir -p ~/.claude/scripts/
touch ~/.claude/scripts/mindbreak_hook.sh
chmod +x ~/.claude/scripts/mindbreak_hook.sh
- Configure your AI agent to monitor the output of the hook script.
- Ensure the agent has the instructions to append reminders to the end of responses when a trigger signal is detected.
MindBreak Data Schema & Taxonomy
MindBreak uses a simple, local data structure to manage session intensity without compromising privacy.
| Component | Description | Data Type |
|---|---|---|
| Activity Log | Local file storing Unix timestamps of user messages. | Integer (Unix Epoch) |
| Duration Logic | Calculates segments; gaps > 15 min reset the timer. | Time Duration |
| Trigger Signals | Signals like MINDBREAK_LIGHT or MINDBREAK_MEAL. | String |
| Thresholds | Configured for 45 min work duration and 30 min cooldowns. | Integer (Minutes) |
MindBreak Advanced Features
- Intelligent Segment Calculation: Automatically detects natural breaks and resets monitoring logic without manual input.
- Progress-Aware Reminders: During meal or overtime breaks, the skill summarizes current task status and next steps for a seamless return.
- Natural Language Overrides: Users can disable reminders for the current session using simple commands like "stop reminding."
- Contextual Placement: Reminders are strategically placed as the final paragraph to avoid interrupting the agent's technical output.
SKILL.md
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