A task scheduling engine that manages the timing of automated AI agent actions while keeping the user in control of execution.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install schedule
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install schedule using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Schedule skill is a robust timing and automation management tool designed specifically for AI agents. It logically decouples the task definition from its execution timing, allowing users to define specific actions while the skill manages the complex cron-like timing logic. As a core component of the Openclaw Skills ecosystem, it provides a persistent way to handle daily reviews, reminders, and recurring triggers without the need to manually modify system crontabs or underlying system launch agents.
By leveraging this skill, developers and power users can ensure their agents perform consistently over time. The skill maintains a local database of jobs and user preferences, ensuring that every automated action is performed within the correct timezone and context, strictly following the permissions granted by the user during the initial setup.
To prepare the environment for the skill, you must manually create the history directory to allow for logging:
mkdir -p ~/schedule/history
The skill will automatically initialize the jobs.json and preferences.json files upon the first successful scheduling request. No further system-level configuration is required as this operates independently of system cron.
The skill organizes its data within the ~/schedule/ directory using the following structure:
| File/Directory | Purpose |
|---|---|
jobs.json |
Stores active job definitions, including cron expressions, task descriptions, and dependency requirements. |
preferences.json |
Tracks user-specific data such as timezones and definitions for abstract times like morning or evening. |
history/ |
Contains execution logs in JSONL format, partitioned by year and month (e.g., YYYY-MM.jsonl). |
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