Learning Checkin

Learning Check-in Skill

Help users build a daily learning habit through simple check-ins and intelligent reminders.

Overview

This skill enables users to track their daily learning with:

• Simple daily check-in (just say "I'm done" or "check-in complete")
• Automatic streak tracking
• Smart reminders at appropriate times (via cron jobs)
• Optional version updates check

Data Storage

All data is stored locally in a data subfolder next to the skill:

<skill_directory>/data/
├── rule.md       - User's customizable rules
├── records.json  - Check-in history
└── version.txt   - Current version

The data folder is automatically created on first use.

Commands

The skill provides these functions that the Agent can call:

1. Initialize (First Time)

python <skill_path>/learning_checkin.py init

When to use: First time the user activates this skill.

Agent action:

• Run the init command
• Show welcome message in user's language
• Explain the simple rules
• Ask user to start their first check-in

2. Check-in

python <skill_path>/learning_checkin.py checkin

When to use: When user says they're done with their learning (e.g., "I finished my study session", "check-in done", "learning complete", etc.)

Agent action:

• Run checkin command
• Show streak count with celebration
• Encourage user to continue tomorrow

3. Status

python <skill_path>/learning_checkin.py status

When to use: When user asks about their progress or streak.

Agent action:

• Run status command
• Tell user if they've checked in today
• Share their current streak
• Show total days checked in

4. Get Reminder Message

python <skill_path>/learning_checkin.py message <time>

Where <time> is one of: 09:00, 17:00, 20:00

When to use: When sending a scheduled reminder.

Agent action:

• Run message command with appropriate time slot
• Send the reminder to user
• The message tone becomes more encouraging/urgent as the day progresses

5. Check Reminder Status

python <skill_path>/learning_checkin.py reminder <time>

Where <time> is one of: 09:00, 17:00, 20:00

When to use: Before sending a reminder (typically called by cron job).

Agent action:

• Run reminder command
• If result shows "should_send": true, then send the reminder
• The command automatically logs that reminder was sent

Default Behavior

Check-in Rule

• User checks in once per day
• Simply tell the Agent "I'm done" or "check-in complete"
• That's it! No complex forms or steps

Default Reminder Schedule

• 09:00 (Morning): Friendly reminder
• 17:00 (Afternoon): Encouraging reminder
• 20:00 (Evening): Urgent reminder (don't break the streak!)

Streak System

• Consecutive days of check-ins = streak
• Miss a day = streak resets to 0

Customization

Users can edit the rule.md file (in the data folder) to customize:

• Reminder times
• Reminder messages
• Their personal goals or notes

Version Check

On each check-in, the skill can optionally check GitHub for new versions:

• Non-blocking (5 second timeout)
• If new version available, Agent tells user

Agent Guidelines

First Interaction (Welcome)

The Agent should:

1. Be warm and encouraging
2. Explain in simple, non-technical language:
   • "Just tell me when you've done your learning for today"
   • "I'll remind you if you forget"
   • "You'll build a streak!"
3. Ask: "Ready to start your first check-in?"

Daily Check-in Interaction

The Agent should:

1. Celebrate the check-in
2. Mention current streak
3. Encourage for tomorrow
4. Keep it positive and simple

Reminder Interaction

The Agent should:

1. Use the appropriate tone for the time of day
2. Morning: Cheerful and friendly
3. Afternoon: Supportive and encouraging
4. Evening: Urgent but caring

Technical Notes

• All file paths use UTF-8 encoding
• Compatible with Windows, Linux, macOS
• Data stored in data subfolder next to the skill
• No external dependencies (Python standard library only)

Version

Current version: 3.0.1

GitHub: https://github.com/daizongyu/learning-checkin