A privacy-first AI journaling tool that uses natural language processing to analyze Chinese diary entries and track emotional health locally.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mood-diary
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 mood-diary 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 Mood Diary?
Mood Diary is a specialized emotional intelligence tool developed for Openclaw Skills that allows users to record their daily reflections in natural Chinese. Unlike standard journals, it automatically identifies seven core emotions (Happiness, Calmness, Excitement, Anxiety, Sadness, Anger, and Fatigue) and assigns intensity scores from 1 to 10.
By leveraging localized keyword analysis and smart tag extraction, Mood Diary provides a deep understanding of your mental state without compromising your privacy. All data is stored in a structured JSON format on your local machine, making it a secure alternative to cloud-based mood trackers. This skill is ideal for developers and users who want a high-performance, scriptable way to manage their emotional data.
Mood Diary Use Cases
- Daily mental health monitoring using natural language entries.
- Automated emotional trend analysis for personal development and therapy support.
- Identifying external triggers (people, places, weather) that impact daily mood.
- Creating a secure, local-first repository of personal reflections within the Openclaw Skills ecosystem.
How Mood Diary Works
- The user provides a diary entry in Chinese through a simple CLI command.
- The skill parses the text using a predefined dictionary of emotional triggers and sentiment indicators.
- Entities such as people, events, and locations are automatically tagged for contextual analysis.
- An emotional score is generated or extracted directly from the text (e.g., 'Mood 8/10').
- The entry is appended to a local JSON database using an atomic write mechanism to ensure data integrity.
- Users run reporting scripts to generate daily, weekly, or monthly summaries and trend visualizations.
Mood Diary Setup
To get started with this entry in the Openclaw Skills collection, ensure you have Python 3.9+ installed. No external dependencies are required as it uses the Python standard library.
# Add a new diary entry
python scripts/journal.py add "今天工作很顺利，心情不错"
# View your mood calendar
python scripts/journal.py calendar
# Generate a weekly emotional report
python scripts/mood-report.py weekly
Mood Diary Data Schema & Taxonomy
Data is organized in a local JSON file located at ~/.openclaw/workspace/data/journal/entries.json. The schema is designed for easy parsing and portability:
| Attribute | Type | Description |
|---|---|---|
| id | String | Unique 8-character short ID |
| date | String | ISO-8601 formatted date |
| content | String | Cleaned text content |
| mood | String | Primary identified emotion (e.g., 开心) |
| score | Integer | Emotional intensity (1-10) |
| tags | Array | Extracted entities (People, Events, etc.) |
| raw_text | String | The original raw input string |
Mood Diary Advanced Features
- Custom Emotion Configuration: Modify
assets/moods.jsonto define new emotion categories, keywords, and color codes. - Python Module Integration: Import the
JournalTrackerclass directly into your own Python scripts for automated logging. - Trend Anomaly Detection: Use the
trendcommand to identify sudden emotional fluctuations over a specified duration. - Smart Tag Extractor: Automatically categorizes entities like 'Family', 'Work', 'Coffee', and 'Weather' to reveal correlation with mood patterns.
- Local-Only Architecture: Zero network requests, ensuring total data sovereignty within the Openclaw Skills framework.
SKILL.md
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