nutricoach 营养专家

Personal health data management and intelligent diet recommendation system. Use when users need to: - Record and track body metrics (weight, height, BMI, body fat) - Log daily meals and dietary intake - Query food nutrition information - Get personalized diet recommendations based on health goals - Analyze nutrition trends and generate reports - Identify foods from photos and estimate nutritional content - Manage pantry inventory and track food expiration - Get smart recipe recommendations based on available ingredients - Export data and create backups Supports multi-user data isolation, time-series tracking, extensible food database, OCR food recognition, and web dashboard visualization.

NutriCoach Skill

A comprehensive personal health management system for tracking body metrics, logging meals, analyzing nutrition, managing pantry inventory, and receiving intelligent diet recommendations.

Quick Start

1. Initialize Database

python3 scripts/init_db.py --user <username>

Creates isolated SQLite database for the user with all required tables.

2. Set Up User Profile

python3 scripts/user_profile.py --user <username> set \
  --name "Robert" \
  --gender male \
  --birth-date 1994-05-15 \
  --height-cm 175 \
  --target-weight 70 \
  --activity-level moderate \
  --goal-type maintain

3. Log Daily Weight

python3 scripts/body_metrics.py --user <username> log-weight --weight 72.5

4. Log a Meal

python3 scripts/meal_logger.py --user <username> log \
  --meal-type lunch \
  --foods "米饭:150g, 鸡胸肉:100g, 西兰花:100g"

5. Get Diet Recommendation

python3 scripts/diet_recommender.py --user <username> recommend --meal-type dinner

6. Launch Web Dashboard

python3 scripts/launch_dashboard.py --user <username>

Opens browser at http://127.0.0.1:5000 with visual health data overview.

Core Features

📊 Body Metrics Tracking

• Weight logging with automatic BMI calculation
• Body fat percentage tracking
• 7/30-day trend analysis
• TDEE (Total Daily Energy Expenditure) calculation

🍽️ Meal Logging

• Natural language food entry: 米饭:150g, 鸡胸肉:100g
• Automatic nutrition calculation
• Daily summary with remaining calories vs TDEE
• 569 built-in Chinese foods database

📷 OCR Food Recognition

• Scan food packaging with camera
• Extract nutrition facts automatically
• Barcode matching for existing products
• Support for Kimi Vision and macOS Vision engines

🥬 Pantry Management

• Track inventory by storage location (fridge/freezer/dry goods/counter)
• Expiration date tracking with alerts
• Automatic quantity deduction when logging meals
• Category-based organization (protein/vegetable/carb/fruit/dairy)

🍳 Smart Recipe Recommendations

• Generate recipes based on available pantry items
• Prioritize ingredients nearing expiration
• Match recipes to nutritional gaps
• Dynamic threshold adjustment based on inventory levels

📈 Web Dashboard (v2)

• Tabbed interface: Overview / Pantry / Weight History
• Visual charts for weight and nutrition trends
• Interactive pantry management
• One-click recipe generation

💾 Data Management

• JSON/CSV export
• Automatic database backups (keeps last 10)
• Easy restore from backup
• Multi-user data isolation

Architecture

See references/ARCHITECTURE.md for system design and data flow.

Database Schema

See references/DATABASE_SCHEMA.md for complete ER diagram and table definitions.

Feature Guide

See references/FEATURE_GUIDE.md for detailed user-facing documentation including:

• Complete feature descriptions
• OCR food recognition workflow
• Pantry management guide
• Smart recipe recommendations
• Web dashboard usage
• Quick command reference

Developer Guide

See references/DEVELOPER_GUIDE.md for:

• Complete API reference for all scripts
• Food database structure
• Database schema details
• Extension guidelines

Changelog

See CHANGELOG.md for version history and breaking changes.

Available Scripts

Script	Purpose
init_db.py	Initialize user database
user_profile.py	Manage user profile and goals
body_metrics.py	Log weight and track trends
meal_logger.py	Log meals and view daily summary
diet_recommender.py	Get meal recommendations
food_analyzer.py	Search foods, OCR scan, add custom
pantry_manager.py	Manage ingredient inventory
smart_recipe.py	Generate recipes from pantry
launch_dashboard.py	Start web dashboard
report_generator.py	Generate weekly/nutrition reports
export_data.py	Export to JSON/CSV
backup_db.py	Backup and restore database
food_ocr.py	OCR engine wrapper
food_matcher.py	Match OCR results to database
migrate_db.py	Database migration tool

Data Storage

Each user has an isolated SQLite database stored at:

~/.openclaw/workspace/skills/nutricoach/data/<username>.db

Backups are stored in:

~/.openclaw/workspace/skills/nutricoach/data/backups/

Common Workflows

Daily Logging Workflow

1. Morning: Log weight with body_metrics.py log-weight
2. After meals: Log food with meal_logger.py log
3. Evening: Review daily nutrition summary

Weekly Review Workflow

1. Generate weekly report with report_generator.py weekly
2. Check weight trend
3. Adjust targets if needed with user_profile.py update

Photo Food Recognition Workflow

1. Save food photo to accessible path
2. Run: python3 scripts/food_analyzer.py --user <username> scan --image <path>
3. Review identified foods and confirm/edit quantities
4. Log confirmed foods with meal_logger.py log

Pantry Management Workflow

1. Add groceries: pantry_manager.py add --food "鸡胸肉" --quantity 500
2. Log meals (auto-deducts from pantry)
3. Check expiring items: pantry_manager.py list --expiring 3
4. Get recipe ideas: smart_recipe.py --count 3

Web Dashboard Workflow

1. Launch: launch_dashboard.py --user <username>
2. Browse Overview tab for trends
3. Manage Pantry tab for inventory
4. Use smart recipe buttons directly from pantry view

Configuration

OCR Engine Setup

OCR 食品包装识别支持多种方式，零配置即可使用。

方式一：AI 助手识别（推荐，零配置）

如果你使用 AI 助手（如 OpenClaw），直接发送食品包装图片：

[发送图片]
"录入这个食材，生产日期 2025-01-15"

助手会直接识别图片并录入，无需任何配置。

方式二：本地识别（命令行，免费）

使用 macOS 内置 Vision 框架，无需配置：

python3 scripts/food_analyzer.py --user <name> scan --image food.jpg --engine macos

方式三：云端识别（命令行，可选）

如需更高识别精度，配置云端 Vision API：

步骤 1：创建配置文件

cp data/user_config.example.yaml data/user_config.yaml

步骤 2：填入 API key

vision:
  api_key: "your-api-key"
  base_url: "https://api.moonshot.cn/v1"  # 或其他兼容 OpenAI 的 API
  model: "kimi-k2.5"

步骤 3：使用

python3 scripts/food_analyzer.py --user <name> scan --image food.jpg

支持的 API 提供商：

• Moonshot (Kimi)
• OpenAI
• 阿里 DashScope
• 其他兼容 OpenAI 接口的服务

说明：

• user_config.yaml 已被加入 .gitignore，不会意外提交
• 云端 OCR 仅在命令行模式下需要配置
• AI 助手模式始终优先使用助手内置的视觉能力

Food Database

Built-in database includes 569 Chinese foods across categories:

• Proteins: 鸡胸肉, 牛肉, 鸡蛋, 豆腐, etc.
• Vegetables: 西兰花, 菠菜, 西红柿, etc.
• Carbs: 米饭, 燕麦, 红薯, etc.
• Fruits: 苹果, 香蕉, 橙子, etc.
• Dairy: 牛奶, 酸奶, 奶酪, etc.

Add custom foods via OCR scan or manual entry.

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Last updated: 2026-03-28 (v2.0: Added pantry management, smart recipes, OCR, web dashboard)