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name: learning-checkin description: Daily learning habit builder with check-ins and smart reminders metadata: { "copaw": { "emoji": "📚" } }

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
├── cron_status.json  - Cron configuration status (auto-set by Agent)
└── reminder_log.json - Reminder sending log

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.

Returns:

• welcome_message - Welcome text for the user
• environment - Environment info (OS, timezone, language, etc.)
• cron_setup - Commands to set up reminder cron jobs
• cron_status - Current cron configuration status
• require_consent: true - Indicates cron requires user consent before setup

Agent action:

1. Run the init command
2. Collect environment information (OS, timezone, language)
3. Show welcome message and explain the check-in process
4. Ask user for consent before setting up reminders (see Agent Guidelines below)
5. Only if user agrees: create cron job, verify, call update-cron
6. 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.)

Returns:

• success - Whether check-in was recorded
• streak - Current streak count
• message - Celebration message (all messages in English, Agent translates)
• update - Version update info if available

Agent action:

• Run checkin command
• Translate the celebration message to user's language
• 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.

Returns:

• checked_in_today - Whether user has checked in today
• streak - Current streak count
• total_checkins - Total days checked in
• message - Status message (in English)

Agent action:

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

4. Get Environment Information

python <skill_path>/learning_checkin.py env

When to use: Before initialization to gather environment context.

Returns:

• os - Operating system (Windows, Linux, Darwin)
• os_version - OS version string
• python_version - Python version
• locale - System locale
• timezone - User's timezone
• user_language - Detected user language (zh/en)

Agent action: Use this to understand the user's environment and adapt accordingly.

5. 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.

Returns:

• message - Reminder text (in English, Agent translates)

Agent action:

• Run message command with appropriate time slot
• Translate the message to user's language
• Send the reminder to user

6. 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).

Returns:

• should_send - Whether reminder should be sent
• checked_in - Whether user has already checked in today

Agent action:

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

7. Update Cron Status

python <skill_path>/learning_checkin.py update-cron <times>

Where <times> is comma-separated times (e.g., "20:00" or "09:00,20:00")

When to use: After successfully setting up cron jobs.

Agent action: Call this to save the configured reminder times.

8. Get Cron Status

python <skill_path>/learning_checkin.py cron-status

When to use: To check if reminders are configured.

Returns:

• configured - Whether cron is set up
• times - Configured reminder times

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 user for consent before setting up reminders - explain that reminders require creating scheduled tasks
   • Example: "Would you like me to send you daily reminders? I'll create a scheduled task to remind you at 8 PM each day. You can always opt out later."
4. Only create cron jobs after user explicitly agrees
5. If user agrees: create cron job, verify success, call update-cron to save status
6. If user declines or cron fails: inform them they can set up reminders later anytime
7. Ask: "Ready to start your first check-in?"

Cron Setup (Requires User Consent)

Creating scheduled cron jobs is a system-level change that produces persistent behavior outside immediate agent invocation.

The Agent MUST:

1. Never auto-create cron jobs without user consent
2. Clearly explain what reminders are and what they do before asking
3. Wait for user's explicit "yes" before creating any cron jobs
4. Provide easy way for users to opt out or modify later

If user consents:

1. Create cron job using the command from cron_setup
2. Verify it was created successfully
3. Call update-cron <times> to save the status

If user declines or setup fails:

1. Do NOT create cron job
2. Inform user they can enable reminders anytime by asking
3. Provide manual setup instructions if requested

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
5. Translate all messages to user's language

Technical Notes

• All messages are in English - Agent should translate to user's language
• 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: 3.0.6

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