WorkPilot - Productivity Assistant

中文文档 | English

An AI-powered intelligent productivity assistant designed to boost workplace efficiency. With six core features - daily report management, intelligent weekly report generation, OKR goal planning, career asset accumulation, and skills radar analysis - it helps you say goodbye to tedious document organization and makes report writing and career development planning simple and efficient.

Core Value:

• 📝 Daily Management: Calendar-style daily report entry with holiday display, built-in TODO reminder panel
• 🤖 Smart Weekly Reports: One-click generation of standardized weekly reports from daily entries, auto-categorize, deduplicate, and extract risks
• 🎯 OKR Planning: Intelligently generate quarterly OKRs based on historical materials, with quantitative metrics and milestone nodes
• 💼 Career Assets: Auto-extract STAR-format work achievements, accumulate career assets
• 📊 Skills Radar: Track skill growth, AI-powered categorization, visualize capability distribution
• 📤 Multi-format Export: Support export to CSV, Markdown, TXT formats
• 💾 Local Storage: All data stored in local SQLite database, secure and reliable
• 🚀 One-Click Deployment: Support Docker or local deployment, Windows users can use batch scripts for one-click start

📸 Product Showcase

Daily Report Entry

Calendar View + Holiday Display + TODO Tips Panel + Quick Template + Multi-format Export

Weekly Report Generator

Import from Daily Reports + Smart Categorization + Risk Analysis

Weekly Report Query

History Query + Edit/Delete Functions + Multi-format Export

OKR Management

Smart Generation + Quantitative Metrics + Milestone Planning + Multi-format Export

Career Assets

STAR Format Extraction + Project Timeline + Career Asset Management

Skills Radar

Skills Distribution Visualization + AI Categorization + Skill Details View

Configuration

Support LLM DIY，support models switching, support custom models

📋 Features

📅 Daily Report Entry

• Calendar View: Large calendar interface, click on a date to directly enter daily report
• Holiday Display: Calendar shows Chinese traditional holidays and international holidays (Spring Festival, Mid-Autumn, National Day, Christmas, etc.)
• Weekend Indicator: Saturdays and Sundays displayed in red text
• Data Persistence: Daily reports automatically saved to local SQLite database
• Quick Template: Support inserting daily report template for quick filling
• Status Indicator: Recorded dates marked in green for clear visibility
• Statistics: Display monthly and total entry counts
• TODO Tips: Left-side floating note panel, can add/check/delete TODO items
• Multi-format Export: Export current or all daily reports to CSV/Markdown/TXT format

📋 Weekly Report Generation

• Automatic Generation: Generate standardized weekly report email format from text daily reports
• Import from Daily Reports: One-click select recorded daily reports, support custom date ranges
• Flexible Date Range: Generate reports using actual imported date ranges, not fixed current week range
• Smart Date Recognition: Automatically recognize date formats (20251212 8h or 2025-12-12 8h)
• Smart Categorization: Auto-categorize into projects, capability building, research, and other administrative work
• Deduplication & Merging: Auto deduplicate and merge similar items
• Risk Analysis: Extract risk points and provide response suggestions
• Save Report: Generated reports can be saved to database

🔍 Weekly Report Query

• History Query: Query historical weekly reports by date range
• Edit Function: Can edit saved weekly reports
• Delete Function: Can delete unwanted weekly report records
• Multi-format Export: Export to CSV/Markdown/TXT format

🎯 OKR Management

• Smart Generation: Generate next quarter OKR based on historical materials
• Clear Date Node: Each KR contains clear date node (YYYY-MM-DD before)
• Quantitative Metrics: Each KR contains quantitative expression (threshold/ratio/quantity etc.)
• Milestone Planning: Key KRs contain phase milestones (M1/M2/M3)
• Goal Management: Generate 2-3 reasonable objectives
• Save OKR: Generated OKRs can be saved to database
• Multi-format Export: Export to CSV/Markdown/TXT format

💼 Career Assets (Resume Building Blocks)

• STAR Format Extraction: Intelligently extract Situation-Task-Action-Result format achievements from daily/weekly reports
• Project Classification: Auto-identify and categorize projects
• Timeline View: Display career achievements in chronological order
• Achievement Editing: Support editing STAR summaries to improve career assets
• Data Cleanup: Support merging similar projects and cleaning invalid data

📊 Skills Radar

• Skills Distribution Visualization: Radar chart showing skill usage frequency
• AI Smart Categorization: Use LLM to intelligently identify skill categories (Technical/Soft/Domain)
• Category Filtering: Filter and view by skill category
• Skill Details: Click on skills to view related work items
• Growth Tracking: Record first and last use time for each skill

💾 Data Storage

• Use SQLite lightweight database
• Database file location: backend/data/reports.db
• Tables: daily_reports, weekly_reports, okr_reports, todo_items, work_items, projects, skills, etc.

🛠️ Technology Stack

• Frontend: React + TypeScript
• Backend: Flask + Python
• LLM: OpenAI-like chat completions API
• Deployment: Docker / Local / Batch Scripts

🚀 Quick Start

Method 1: Windows Installer (Most Recommended) ⭐⭐⭐

The easiest way to use - no development environment required, just double-click to run!

Installation Steps

1. Download Installer

   • Download the latest WorkPilot-Setup-x.x.x.exe from the Releases page

2. Run Installer

   • Double-click WorkPilot-Setup-x.x.x.exe
   • Choose installation directory (default: C:\Program Files\WorkPilot)
   • Follow the wizard to complete installation

3. Launch Application

   • Double-click the desktop shortcut "WorkPilot Productivity Assistant"
   • Or launch from Start Menu
   • The app will automatically start backend services and frontend interface
   • Browser will automatically open the application page

4. First-time LLM Configuration

   • Click the "⚙️ Settings" tab in the top navigation bar
   • Fill in LLM API configuration:
     • API URL: e.g., https://api.deepseek.com/v1
     • API Key: Your API key
     • Model Name: e.g., deepseek-chat
   • Click "Test Connection" to verify configuration
   • Click "Save Configuration"
   • The status at the top of the page will automatically update to "LLM Configured"

5. Minimize to System Tray

   • Click the minimize button, the app will automatically minimize to system tray
   • Right-click the tray icon to open main interface or exit the app
   • Services continue running in the background

Features

• ✅ Double-click install, no development environment needed
• ✅ Automatically start backend and frontend services
• ✅ Support minimize to system tray
• ✅ Auto-start on boot (optional)
• ✅ Complete uninstall support

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Method 2: One-Click Launch Script (Developers/Advanced Users)

Simplest way for Windows users:

1. Clone project and install dependencies

git clone https://github.com/steven140811/WorkPilot.git
cd WorkPilot

# Install backend dependencies
cd backend
pip install -r requirements.txt

# Install frontend dependencies
cd ../frontend
npm install
cd ..

2. Configure environment variables

# Edit backend\.env file, fill in LLM API configuration
# If not configured, will use mock mode

3. One-click launch all services

# Double-click to run or execute in command line
start_services.bat

# Stop services
stop_services.bat

Features:

• ✅ Auto-detect and release port conflicts
• ✅ Backend runs completely in background with pythonw.exe (no window)
• ✅ Frontend runs in background
• ✅ Auto-open browser
• ✅ Log output to files: backend\backend.log and frontend\frontend.log
• ✅ Services continue running after script exits

4. Access application

• Frontend: http://localhost:5002
• Backend API: http://localhost:5001

Method 3: Docker Compose

# Build Docker images
docker-compose up -d

# Access application
# Frontend: http://localhost:3000
# Backend API: http://localhost:5000

Method 4: Manual Deployment

Backend

cd backend
pip install -r requirements.txt
python app.py

Frontend

cd frontend
npm install
npm start

📁 Project Structure

WorkPilot/
├── backend/                 # Flask backend application
│   ├── app.py              # Main application entry
│   ├── config.py           # Configuration management
│   ├── generator.py        # Report generation logic
│   ├── llm_client.py       # LLM client
│   ├── parser.py           # Text parser
│   ├── prompts.py          # AI prompt templates
│   ├── database.py         # SQLite database module
│   ├── requirements.txt    # Python dependencies
│   ├── data/               # Data directory
│   │   └── reports.db      # SQLite database file
│   └── tests/              # Test files
├── frontend/               # React frontend application
│   ├── src/
│   │   ├── components/
│   │   │   ├── DailyReportEntry.tsx      # Daily report entry component
│   │   │   ├── WeeklyReportGenerator.tsx # Weekly report generator component
│   │   │   ├── WeeklyReportQuery.tsx     # Weekly report query component
│   │   │   ├── OKRGenerator.tsx          # OKR generator component
│   │   │   ├── CareerAssets.tsx          # Career assets component
│   │   │   ├── SkillsRadar.tsx           # Skills radar component
│   │   │   └── ExportButton.tsx          # Export button component
│   │   ├── services/
│   │   │   └── api.ts      # API service layer
│   │   ├── utils/
│   │   │   ├── holidays.ts # Holiday data
│   │   │   └── export.ts   # Export utility functions
│   │   └── App.tsx         # Main application component
│   └── package.json
├── docker-compose.yml      # Docker compose file
├── start_services.bat      # Windows one-click start script
├── stop_services.bat       # Windows one-click stop script
└── README.md               # Project documentation

📡 API Endpoints

Weekly Report Generation

• POST /api/generate/weekly-report - Generate weekly report

OKR Generation

• POST /api/generate/okr - Generate OKR

Daily Report Management

• GET /api/daily-reports?start_date=YYYY-MM-DD&end_date=YYYY-MM-DD - Query daily reports
• GET /api/daily-report/<date> - Get daily report for specific date
• POST /api/daily-report - Save daily report
• PUT /api/daily-report/<date> - Update daily report
• DELETE /api/daily-report/<date> - Delete daily report

Weekly Report Management

• GET /api/weekly-reports?start_date=YYYY-MM-DD&end_date=YYYY-MM-DD - Query weekly reports
• GET /api/weekly-report/<id> - Get specific weekly report
• POST /api/weekly-report - Save weekly report
• PUT /api/weekly-report/<id> - Update weekly report
• DELETE /api/weekly-report/<id> - Delete weekly report

OKR Management

• GET /api/okr-reports?quarter=YYYY-QN - Query OKRs
• GET /api/okr-report/<id> - Get specific OKR
• POST /api/okr-report - Save OKR
• PUT /api/okr-report/<id> - Update OKR
• DELETE /api/okr-report/<id> - Delete OKR

TODO Management

• GET /api/todo-items - Get all TODO items
• POST /api/todo-items - Create TODO item
• PUT /api/todo-items/<id> - Update TODO item (content/completion status)
• DELETE /api/todo-items/<id> - Delete TODO item

🔧 Environment Variables Configuration

Configure LLM-related parameters in the backend/.env file. You can refer to the .env.example file in the project root directory for configuration.

Configuration Example:

# LLM Configuration (Required for real LLM calls)
# If not configured, the application will use mock mode
LLM_API_URL=https://api.deepseek.com/v1    # LLM API URL
LLM_API_KEY=sk-your-api-key-here           # Your API key
LLM_MODEL=deepseek-chat                     # Model name

# Optional: LLM timeout and retry settings
LLM_TIMEOUT=30                              # Timeout in seconds, default 30
LLM_RETRY=2                                 # Retry count, default 2

# Flask Configuration
PORT=5001                                   # Backend service port
FLASK_DEBUG=false                           # Debug mode switch

Configuration Steps:

1. Copy the example configuration file:

   cp .env.example backend/.env

2. Edit the backend/.env file and fill in your LLM API configuration

   • LLM_API_URL: API address of the LLM service
   • LLM_API_KEY: Your API key (required)
   • LLM_MODEL: Model name to use

3. If LLM is not configured, the application will run in mock mode (returning test data)

Supported LLM Providers:

• DeepSeek: https://api.deepseek.com/v1
• OpenAI: https://api.openai.com/v1
• Azure OpenAI: https://your-resource.openai.azure.com/
• Other services compatible with OpenAI API format

📝 License

MIT License

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🔄 Data Migration Guide

Use Cases

When you need to migrate WorkPilot data to a new environment (e.g., new computer, system reinstall, migrate from development to installed version), follow these steps.

Database File Locations

Environment Type	Database Path
Development	project_directory/backend/data/reports.db
Installed Version	installation_directory/backend/_internal/data/reports.db

⚠️ Note: The installed version's database is in the _internal/data/ directory, NOT the data/ directory!

Migration Steps

1. Stop All WorkPilot Services

Ensure both source and target environments have WorkPilot completely closed.

2. Locate Source Database File

• Development Source: E:\your_project_path\WorkPilot\backend\data\reports.db
• Installed Source: C:\Program Files\WorkPilot\backend\_internal\data\reports.db

3. Copy to Target Location

Method 1: Manual Copy

Directly copy the reports.db file to the target location, overwriting the existing file.

Method 2: Command Line (PowerShell)

# From development to installed version
Copy-Item "E:\your_project_path\WorkPilot\backend\data\reports.db" "C:\Program Files\WorkPilot\backend\_internal\data\reports.db" -Force

# From installed version to development
Copy-Item "C:\Program Files\WorkPilot\backend\_internal\data\reports.db" "E:\your_project_path\WorkPilot\backend\data\reports.db" -Force

# From old installation to new installation
Copy-Item "D:\old_location\WorkPilot\backend\_internal\data\reports.db" "E:\new_location\WorkPilot\backend\_internal\data\reports.db" -Force

4. Launch Target Environment

Start WorkPilot in the target environment and verify data was migrated correctly.

Backup Recommendations

• 💾 Regularly backup reports.db file to a safe location
• 📁 Consider using cloud storage services for synchronized backups
• 🗓️ Recommend weekly backups of important data

FAQ

Q: Data not showing after migration?

A: Make sure you copied to the correct directory. Installed version uses _internal/data/ directory, not data/.

Q: Can I use development and installed versions simultaneously?

A: Yes, but data is independent. Manual database file copying is required to sync data.

Q: How to completely reset data?

A: Delete the reports.db file, restart the app and a new empty database will be created automatically.