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.

RainbowLion0320@rainbowlion0320

Install

openclaw skills install @rainbowlion0320/nutricoach

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

bash

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

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

2. Set Up User Profile

bash

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

bash

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

4. Log a Meal

bash

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

bash

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

6. Launch Web Dashboard

bash

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:

text

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

Backups are stored in:

text

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

Common Workflows

Daily Logging Workflow

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

Weekly Review Workflow

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

Photo Food Recognition Workflow

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

Pantry Management Workflow

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

Web Dashboard Workflow

Launch: launch_dashboard.py --user <username>
Browse Overview tab for trends
Manage Pantry tab for inventory
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：

text

[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：

bash

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

bash

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

步骤 2：填入 API key

yaml

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

bash

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.

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

nutricoach 营养专家

Install

NutriCoach Skill

Quick Start

1. Initialize Database

2. Set Up User Profile

3. Log Daily Weight

4. Log a Meal

5. Get Diet Recommendation

6. Launch Web Dashboard

Core Features

📊 Body Metrics Tracking

🍽️ Meal Logging

📷 OCR Food Recognition

🥬 Pantry Management

🍳 Smart Recipe Recommendations

📈 Web Dashboard (v2)

💾 Data Management

Architecture

Database Schema

Feature Guide

Developer Guide

Changelog

Available Scripts

Data Storage

Common Workflows

Daily Logging Workflow

Weekly Review Workflow

Photo Food Recognition Workflow

Pantry Management Workflow

Web Dashboard Workflow

Configuration

OCR Engine Setup

方式一：AI 助手识别（推荐，零配置）

方式二：本地识别（命令行，免费）

方式三：云端识别（命令行，可选）

Food Database

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