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Weightloss Analyzer

Community
huifer
weightloss-analyzer

分析减肥数据、计算代谢率、追踪能量缺口、管理减肥阶段

Overview

Publisherhuifer
RepositoryWellAlly-health
Skill nameweightloss-analyzer
Stars
960
Forks
123
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by huifer on GitHub. Read the source before you install it.

Installation

Install the Weightloss Analyzer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huifer/WellAlly-health/tree/main/.claude/skills/weightloss-analyzer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huifer/WellAlly-health.git /tmp/WellAlly-health
mkdir -p .claude/skills
cp -r /tmp/WellAlly-health/.claude/skills/weightloss-analyzer .claude/skills/weightloss-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Weightloss Analyzer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Weightloss Analyzer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Weightloss Analyzer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

减肥分析技能

分析减肥数据,计算代谢率,追踪能量缺口,管理减肥阶段。

功能

1. 身体成分分析

BMI计算与分类

  • BMI = 体重(kg) / 身高(m)²
  • 分类标准(WHO亚洲标准):
    • 偏瘦:BMI < 18.5
    • 正常:18.5 ≤ BMI < 24
    • 超重:24 ≤ BMI < 28
    • 肥胖:BMI ≥ 28

体脂率评估

  • 男性:15-20%(正常),20-25%(偏高),>25%(肥胖)
  • 女性:20-25%(正常),25-30%(偏高),>30%(肥胖)

围度分析

  • 腰围评估
    • 男性:< 90cm(正常),≥ 90cm(腹部肥胖)
    • 女性:< 85cm(正常),≥ 85cm(腹部肥胖)
  • 腰臀比
    • 男性:< 0.9(正常),≥ 0.9(腹部肥胖)
    • 女性:< 0.85(正常),≥ 0.85(腹部肥胖)

理想体重计算

  • BMI法:理想体重 = 身高(m)² × 22
  • Broca法修正:理想体重 = (身高cm - 100) × 0.9

2. 代谢率计算

Harris-Benedict公式(1919原始版)

  • 男性:BMR = 88.362 + (13.397 × 体重kg) + (4.799 × 身高cm) - (5.677 × 年龄)
  • 女性:BMR = 447.593 + (9.247 × 体重kg) + (3.098 × 身高cm) - (4.330 × 年龄)

Mifflin-St Jeor公式(推荐,更准确)

  • 男性:BMR = (10 × 体重kg) + (6.25 × 身高cm) - (5 × 年龄) + 5
  • 女性:BMR = (10 × 体重kg) + (6.25 × 身高cm) - (5 × 年龄) - 161

Katch-McArdle公式(基于瘦体重)

  • BMR = 370 + (21.6 × 瘦体重kg)
  • 瘦体重 = 体重kg × (1 - 体脂率)

TDEE计算

  • TDEE = BMR × 活动系数
  • 活动系数:
    • 久坐:1.2
    • 轻度活动:1.375
    • 中度活动:1.55
    • 高度活动:1.725
    • 非常高度活动:1.9

3. 能量缺口管理

每日能量缺口追踪

  • 缺口 = TDEE - 实际摄入 + 运动消耗
  • 缺口达标分析:实际缺口 vs 目标缺口

减重估算

  • 1kg脂肪 ≈ 7700大卡
  • 预计周减重 = 每日缺口 × 7 / 7700
  • 安全减重速度:0.5-1kg/周(缺口500-1000大卡/天)

热量安全边界

  • 男性最低热量:1500大卡/天
  • 女性最低热量:1200大卡/天
  • 绝对最低:BMR × 1.2

4. 阶段管理

减重期

  • 追踪体重变化
  • 计算减重进度
  • 监测减重速度

平台期检测

  • 定义:2周以上体重无明显变化(波动<0.5kg)
  • 原因分析:代谢适应、水分滞留、肌肉增加
  • 突破方法:调整热量、改变运动、间歇性断食

维持期

  • 目标体重±2kg范围内
  • 定期监测体重
  • 及时调整方案

数据源

主要数据源

  1. 健身追踪器

    • 路径:data/fitness-tracker.json
    • 内容:体重记录、身体成分、代谢率、阶段管理
  2. 营养追踪器

    • 路径:data/nutrition-tracker.json
    • 内容:热量摄入、能量缺口、膳食计划
  3. 健康日志

    • 路径:data/health-logs/YYYY-MM/YYYY-MM-DD.json
    • 内容:每日体重、饮食记录

输出格式

身体成分分析报告

markdown
# 身体成分分析报告

## 基本信息
- 性别:男
- 年龄:52岁
- 身高:175cm
- 体重:75kg

## 身体指标

### BMI
- 当前BMI:24.5
- 分类:超重
- 理想体重:67kg(BMI=22)
- 需减重:8kg

### 体脂率
- 当前体脂率:25%
- 分类:偏高
- 目标体脂率:15-20%

### 围度分析
- 腰围:92cm(腹部肥胖风险)
- 臀围:98cm
- 腰臀比:0.94(腹部肥胖)

## 建议
1. 每周减重0.5-1kg
2. 目标减重时间:8-16周
3. 综合干预:饮食+运动

代谢率分析报告

markdown
# 代谢率分析报告

## BMR计算

| 公式 | BMR | 说明 |
|------|-----|------|
| Harris-Benedict | 1650 | 1919原始公式 |
| Mifflin-St Jeor | 1620 | 推荐使用 ⭐ |
| Katch-McArdle | 1700 | 基于体脂率 |

**推荐BMR:1620 大卡/天**

## TDEE计算

- 活动水平:中度运动
- 活动系数:1.55
- TDEE:1620 × 1.55 = **2511 大卡/天**

### 热量分配
- BMR基础代谢:65% ≈ 1632 大卡
- 运动消耗:20% ≈ 502 大卡
- NEAT日常活动:15% ≈ 377 大卡

## 减肥热量目标

### 温和减重方案
- 每日缺口:500 大卡
- 目标摄入:2011 大卡/天
- 预计减重:0.5kg/周

### 积极减重方案
- 每日缺口:750 大卡
- 目标摄入:1761 大卡/天
- 预计减重:0.75kg/周

### 快速减重方案
- 每日缺口:1000 大卡
- 目标摄入:1511 大卡/天
- 预计减重:1kg/周
- ⚠️ 仅限短期使用

## 安全检查
- 最低热量要求:1500 大卡/天(男性)
- 快速方案热量:1511 大卡/天 ✅
- 建议选择:温和或积极方案

能量缺口追踪报告

markdown
# 能量缺口追踪报告

## 本周汇总(2025-06-16 至 2025-06-22)

| 日期 | 摄入 | 运动消耗 | NEAT | 缺口 | 达标 |
|------|------|---------|------|------|------|
| 周一 | 1800 | 350 | 300 | 961 | ✅ |
| 周二 | 2100 | 200 | 250 | 461 | ❌ |
| 周三 | 1750 | 400 | 300 | 1061 | ✅ |
| 周四 | 1950 | 300 | 280 | 741 | ✅ |
| 周五 | 2200 | 150 | 200 | 261 | ❌ |
| 周六 | 2400 | 100 | 150 | -89 | ❌ |
| 周日 | 1850 | 350 | 300 | 911 | ✅ |

**目标缺口:500 大卡/天**

## 统计分析
- 平均缺口:642 大卡/天
- 达标天数:5/7天(71%)
- 总缺口:4494 大卡
- 预计减重:0.58kg

## 趋势分析
- 周末缺口偏小(社交活动增加)
- 建议提前规划周末饮食

## 下周目标
- 达标天数:7/7天
- 平均缺口:700 大卡/天
- 预计减重:0.64kg

阶段管理报告

markdown
# 减肥阶段管理报告

## 当前阶段:减重期

### 进度追踪
- 开始日期:2025-01-01
- 初始体重:82kg
- 当前体重:75kg
- 目标体重:67kg
- 已减重:7kg
- 剩余:8kg
- 进度:47%

### 减重速度
- 总周数:24周
- 平均减重:0.29kg/周
- 最近4周:0.35kg/周 ⬆️ 加速中

## 状态分析

### 当前状态:✅ 良好
- 减重速度在健康范围(0.5-1kg/周)
- 代谢率稳定
- 肌肉量维持良好

### 平台期监测
- 最近2周变化:-0.8kg
- 状态:❌ 非平台期

## 下一步行动
1. 继续当前热量方案
2. 增加力量训练频率
3. 每周监测身体成分

使用方法

通过 /fitness:weightloss-* 和 /nutrition:weightloss-* 命令调用。

示例命令

bash
# 设置减肥计划
/fitness:weightloss-setup --weight 75 --height 175 --age 52 --gender male

# 计算代谢率
/fitness:weightloss-bmr --formula mifflin

# 追踪能量缺口
/nutrition:weightloss-track --intake 1800 --exercise 350

# 生成阶段报告
/fitness:weightloss-report

# 检测平台期
/fitness:weightloss-plateau-check

安全原则

热量安全边界

  • 不推荐 < 1200大卡/天(女性)
  • 不推荐 < 1500大卡/天(男性)
  • 绝对最低不低于 BMR × 1.2

减重速度控制

  • 安全范围:0.5-1kg/周
  • 最大不超过:1.5kg/周
  • 长期平均:0.5-0.8kg/周

医学免责声明

本技能仅供健康参考,不构成医疗建议。

以下情况请咨询医生:

  • BMI > 35
  • 有心脏病、高血压、糖尿病等慢性病
  • 服用处方药物
  • 女性怀孕或哺乳期
  • 任何健康状况不确定的情况

技能版本: v1.0 最后更新: 2026-01-14 维护者: WellAlly Tech

Frequently asked questions

What does the Weightloss Analyzer AI skill do?

分析减肥数据、计算代谢率、追踪能量缺口、管理减肥阶段

Why use Weightloss Analyzer on TypingMind?

Because you install it once and use it with any model. Weightloss Analyzer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Weightloss Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huifer/WellAlly-health/tree/main/.claude/skills/weightloss-analyzer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Weightloss Analyzer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Weightloss Analyzer?

As many as you like. As long as a model supports skills, you can use Weightloss Analyzer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Weightloss Analyzer AI skill free?

Yes. It is published on GitHub by huifer under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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