A lightweight habit-tracking tool that helps users maintain daily learning consistency through simple check-ins and intelligent reminders.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install learning-checkin
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 learning-checkin 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 Learning Check-in?
The Learning Check-in skill is a specialized tool designed to foster long-term educational habits within the Openclaw Skills ecosystem. By focusing on the psychological momentum of daily streaks, it provides a low-friction way for users to log their learning progress and stay committed to their personal development goals.
This skill operates with a privacy-first mindset, storing all habit data locally without requiring external network requests. It is built to be platform-agnostic, running seamlessly on Windows, Linux, and macOS using only the Python standard library, making it an ideal utility for developers and students alike who use Openclaw Skills for their daily workflows.
Learning Check-in Use Cases
- Building a daily coding habit by tracking consistent practice sessions.
- Maintaining language learning momentum through automated streak monitoring.
- Setting up non-intrusive reminders to study during preferred evening or morning hours.
- Visualizing long-term dedication with total check-in counts and historical records.
How Learning Check-in Works
- Initialize the skill to set up the local data structure and define user language preferences.
- Trigger the check-in command daily to log your learning activity and increment your current streak.
- Configure the optional reminder strategy to receive prompts at specific times (e.g., 20:00).
- The system automatically resets your streak if a day is missed, providing a clear incentive for consistency.
- Use the status command to retrieve a snapshot of your progress, including total days and current streak count.
Learning Check-in Setup
To integrate this tool into your Openclaw Skills setup, follow these steps:
- Initialize the environment:
python learning_checkin.py init
- Perform your first check-in:
python learning_checkin.py checkin
- View your current habit status:
python learning_checkin.py status
Learning Check-in Data Schema & Taxonomy
All data is organized within a local data directory to ensure portability and privacy:
| File | Purpose |
|---|---|
records.json |
Stores the timestamped history of every check-in. |
rule.md |
Contains customizable rules and personalized reminder text. |
cron_status.json |
Maintains the state of reminder configurations. |
reminder_log.json |
Logs when reminders were sent to prevent duplicate notifications. |
Learning Check-in Advanced Features
- Smart Reminder Suppression: The skill automatically checks if a user has already checked in before sending a reminder, preventing unnecessary interruptions.
- Customizable Logic: Users can modify
rule.mdto change the behavior of the habit builder or the tone of the reminder messages. - Streak Resilience: Logic is built to handle time zone variations and ensures streak counts accurately reflect daily participation.
- Translation Ready: While messages are stored in English, the skill provides a language environment check to facilitate seamless translation by the AI agent.
SKILL.md
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