Smart Reminder System

Problem

When an agent conversation includes a request like “remind me to check the weather in 30 seconds,” a plain text reply often leaves no task ID, no scheduling state, and no trace for later inspection. If the user later wants to query, update, or remove reminders, unstructured replies become hard to manage.

How it works

The skill turns reminder creation into a parameterized agent call. It accepts inputs such as content, mode, and style, then uses SkillHub cron to create the reminder task. Depending on the task type, the output can include a reminder ID, delivery status, scheduling ID, next run time, or execution records. For simple reminders, the flow can be read as: parse the time expression, write the cron task, and return a traceable result. The documented FAQ also mentions recurring reminders and task updates by ID, which can serve as later management entry points.

Boundaries

It is intended for AI agent platforms that can load SKILL.md, such as Claude Code, Cursor, Codex, or Gemini CLI, and relies on the agent’s built-in LLM. It is not a decision system for complex human judgment. For production use, keep API keys out of hard-coded config, verify system time, network reachability, and cron settings, and review logs when reminders do not fire on schedule.