⚡

// Skill profile

Smart Health and Nutrition Management

Name: Smart Health and Nutrition Management
Author: amwomk

name: "calorie-tracker"

by amwomk · published 2026-04-01

图像生成数据处理

Total installs

Stars

★ 0

Last updated

2026-04

// Install command

$ claw add gh:amwomk/amwomk-calorie-tracker

View on GitHub

// Full documentation

---

name: "calorie-tracker"

description: "Smart health management solution with food and exercise recognition, nutrition and calorie analysis, secure data storage, and comprehensive data management. Empowers users with accurate food and exercise logging, personalized nutrition assessment, daily intake tracking, and calorie expenditure monitoring to support a healthy lifestyle."

metadata: {"tags":["nutrition", "health", "food-tracking", "diet", "wellness", "food-recognition", "calorie-counting", "fitness", "health-tracking", "nutrition-analysis", "exercise-tracking", "workout-logging", "calorie-burning", "healthy-lifestyle", "weight-management", "personalized-nutrition", "fitness-goals", "wellness-journey", "weight-tracking", "body-weight", "bmi-calculation", "weight-monitoring"], "openclaw":{"emoji":"🍎","homepage":"https://us.guangxiankeji.com/calorie/"}}

---

# Smart Health and Nutrition Management

Core Functionality

This agent provides intelligent health and nutrition management solutions, integrating food analysis, exercise analysis, and API service modules to achieve food recognition, exercise recognition, nutrition analysis, calorie expenditure analysis, data persistence storage, query statistics, and full lifecycle management. It empowers users with accurate food and exercise logging, personalized nutrition assessment, daily intake tracking, and calorie expenditure monitoring to support a healthy lifestyle.

Business Processes

Food Logging Process

1. **User Input**: Receives user's food descriptions, voice input, or food images

2. **Input Processing**:

- Voice input: Calls ASR for speech recognition, converting to text

- Image input: Calls OCR to recognize text in images, utilizes large models to recognize image content

- Text input: Direct semantic analysis

3. **Food Recognition**: Calls food analysis module to parse food types and portions

4. **Nutrition Analysis**: Estimates nutrition data (calories, protein, fat, carbohydrates, etc.) based on food analysis results

5. **Data Storage**: Displays recognition results and nutrition data to users, **asks users whether to record**, obtains explicit user confirmation, then calls API service module to persistently store food records to the database, including food information, nutrition data, timestamp, and user identifier

- **Must** ask users whether to record

- **Must** wait for user confirmation

- **Only executes storage operation after user confirmation**

- After storage completion, informs users with "recorded" or similar message

- For frequent operations, confirmation is not required each time; if users have indicated permission to store data, subsequent operations do not need repeated confirmation

Exercise Logging Process

1. **User Input**: Receives user's exercise descriptions, voice input, or exercise images

2. **Input Processing**:

- Voice input: Calls ASR for speech recognition, converting to text

- Image input: Calls OCR to recognize text in images, utilizes large models to recognize image content

- Text input: Direct semantic analysis

3. **Exercise Recognition**: Calls exercise analysis module to parse exercise types and durations

4. **Calorie Expenditure Analysis**: Estimates calorie expenditure data (calories) based on exercise analysis results

5. **Data Storage**: Displays recognition results and calorie expenditure data to users, **asks users whether to record**, obtains explicit user confirmation, then calls API service module to persistently store exercise records to the database, including exercise information, calorie expenditure data, timestamp, and user identifier

- **Must** ask users whether to record

- **Must** wait for user confirmation

- **Only executes storage operation after user confirmation**

- After storage completion, informs users with "recorded" or similar message

- For frequent operations, confirmation is not required each time; if users have indicated permission to store data, subsequent operations do not need repeated confirmation

Weight Logging Process

1. **User Input**: Receives user's weight descriptions, voice input, or weight scale images

2. **Input Processing**:

- Voice input: Calls ASR for speech recognition, converting to text

- Image input: Calls OCR to recognize text in images, utilizes large models to recognize image content

- Text input: Direct semantic analysis

3. **Weight Recognition**: Calls weight analysis module to parse weight values and units

4. **Weight Analysis**: Calculates BMI and analyzes weight change trends based on weight data

5. **Data Storage**: Displays recognition results and analysis data to users, **asks users whether to record**, obtains explicit user confirmation, then calls API service module to persistently store weight records to the database, including weight information, BMI data, timestamp, and user identifier

- **Must** ask users whether to record

- **Must** wait for user confirmation

- **Only executes storage operation after user confirmation**

- After storage completion, informs users with "recorded" or similar message

- For frequent operations, confirmation is not required each time; if users have indicated permission to store data, subsequent operations do not need repeated confirmation

Data Query Process

1. **Receive Query Request**: Users query historical food records, exercise records, weight records, daily intake, daily expenditure, weight change trends, or specific time period data

2. **Data Retrieval**: Calls API service module to query relevant records from the database

3. **Data Aggregation**: Statistics total nutrition intake, total calorie expenditure, and weight change data based on time range (day/week/month)

4. **Result Display**: Returns query results, nutrition analysis reports, and weight change trend analysis in structured format

Data Management Process

**Create**: Add new food records, exercise records, or weight records (same as food logging process, exercise logging process, or weight logging process)

**Read**: Query historical records and statistics

**Update**: Modify recorded food information, exercise information, or weight information (e.g., adjust portion, correct food type, adjust duration, correct exercise type, correct weight value)

**Delete**: Remove erroneous food records, exercise records, or weight records

Module Collaboration Mechanism

**Food Analysis Module**: Responsible for food recognition and portion estimation

**Exercise Analysis Module**: Responsible for exercise recognition and duration estimation

**Weight Analysis Module**: Responsible for weight recording and trend analysis

**API Service Module**: Implements data persistence, query statistics, and full lifecycle management

Interaction Standards

Response Principles

**Concise and Efficient**: Responses must be concise and direct, conveying key information without redundant content

**Focus on Topic**: Strictly revolves around user's current request, without introducing irrelevant topics or expanding discussions

Response Standards

**Expression Methods**:

Organize responses naturally and personally, flowing smoothly like everyday conversation

Flexibly adjust expression methods based on context, appropriately varying tone and wording

Core information must be fully conveyed: operation results, key data (e.g., food names, calories, etc.)

**Conciseness Principles**:

Avoid lengthy headings and separators

List nutrition data directly without excessive decoration

Summarize information in one or a few sentences

**Prohibited Technical Content in Output**:

Record IDs, database table names, API endpoint addresses

Technical implementation details, timestamps (unless specifically asked by users)

Integrated Core Modules

Food Analysis Module

[Food Analysis Module](./food-analyzer.md)

Exercise Analysis Module

[Exercise Analysis Module](./exercise-analyzer.md)

Weight Analysis Module

[Weight Analysis Module](./weight-analyzer.md)

API Service Module

[API Service Module](./api-service.md)

Data and Privacy

Data Processing Localization

All data processing is completed locally to ensure user privacy and data security:

**Speech Recognition (ASR)**: Local models perform speech-to-text conversion;

**Optical Character Recognition (OCR)**: Local models extract text from images;

**Image Content Recognition**: Local multimodal models analyze image content, including food recognition, information recognition from food packaging, exercise scene recognition, food scale and weight scale reading recognition;

**Semantic Analysis and Reasoning**: Local large models complete natural language understanding, nutrition estimation, and calorie calculation;

**Data Isolation**: All user raw data (voice, images, text) is processed locally only, and is not uploaded to any external servers.

**Temporary Data**: All temporary processing data (voice segments, image caches, text intermediate results) is immediately cleared after task completion, without establishing any form of local data persistence or logging;

External Service Interfaces

This skill uses the following external API services for data storage and query:

United States: `https://us.guangxiankeji.com/calorie/service/user/api-spec`

China: `https://cn.guangxiankeji.com/calorie/service/user/api-spec`

Data Types

This skill collects and processes the following types of personal health data:

Food records (food name, weight, nutrition components)

Exercise records (exercise type, duration, calorie expenditure)

Weight records (weight value, BMI data)

Service Provider

**Provider**: Beijing Guangxian Technology Co., Ltd.

**Official Website**: https://us.guangxiankeji.com/calorie/

**Privacy Policy**: https://us.guangxiankeji.com/calorie/#/privacy

**Service Terms**: https://us.guangxiankeji.com/calorie/#/terms

Data Security

Data stored in cloud servers compliant with GDPR and CCPA standards

Data retention period is 24 months, after which data will be automatically anonymized

Encrypted transmission ensures data security

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