Agent skill

Nutrition Analyzer

by huifer in huifer/WellAlly-health

分析营养数据、识别营养模式、评估营养状况,并提供个性化营养建议。支持与运动、睡眠、慢性病数据的关联分析. An agent skill from huifer/WellAlly-health.

MITAuto-check passedProductivity & Automation

Install Nutrition Analyzer

skills CLI
$ npx skills add huifer/WellAlly-health --skill nutrition-analyzer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install huifer/WellAlly-health nutrition-analyzer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/huifer/WellAlly-health.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nutrition-analyzer .claude/skills/nutrition-analyzer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nutrition-analyzer
GitHub stars
960
Used in
5 other repos
Token cost
~3.3k tokens
SKILL.md length
292 words
Files
1
Skills in repo
15
Repo updated
First seen
Licence
MIT

At a glance

分析营养数据、识别营养模式、评估营养状况,并提供个性化营养建议。支持与运动、睡眠、慢性病数据的关联分析. An agent skill from huifer/WellAlly-health.

  • Works in 5 steps: 营养趋势分析 → 营养素摄入评估 → 营养状况评估 → …
  • Tasks that involve Health and fitness tracking
  • SKILL.md covers 功能, 使用说明, 输出格式 and 数据结构, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Nutrition Analyzer is an agent skill from huifer/WellAlly-health. 分析营养数据、识别营养模式、评估营养状况,并提供个性化营养建议。支持与运动、睡眠、慢性病数据的关联分析。

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Productivity & Automation, covering Health and fitness tracking. The repository describes itself as: Ally-Health is an intelligent healthcare assistant that harnesses advanced AI technology and medical expertise to transform personal health management. Through natural language…. The licence is MIT.

When your agent uses it

  • Tasks that involve Health and fitness tracking

Example prompts

  • “/nutrition-analyzer”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Grep, Glob, Write

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. 营养趋势分析
  2. 营养素摄入评估
  3. 营养状况评估
  4. 相关性分析
  5. 个性化建议生成

What it can do on your machine

Read from SKILL.md and the folder at commit f604350. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Glob
    • Write

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python, markdown and json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • cnsoc.org
    • dietaryguidelines.gov
    • fooddatacentral.usda.gov
    • who.int
    • naturalmedicines.therapeuticresearch.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nutrition Analyzer loads about 3.3k tokens when it runs. Until then it costs about 18 tokens; SKILL.md has 292 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~18
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from huifer/WellAlly-health at commit f604350, republished under its MIT licence (© huifer). 292 words, ~3,329 tokens.

Download SKILL.mdSave it as .claude/skills/nutrition-analyzer/SKILL.md (or your agent's skills folder).
name
nutrition-analyzer
description
分析营养数据、识别营养模式、评估营养状况,并提供个性化营养建议。支持与运动、睡眠、慢性病数据的关联分析。
allowed-tools
Read, Grep, Glob, Write

营养分析器技能

分析饮食和营养数据,识别营养模式,评估营养状况,并提供个性化营养改善建议。

功能

1. 营养趋势分析

分析营养素摄入的变化趋势,识别改善或需要关注的方面。

分析维度:

  • 宏量营养素趋势(蛋白质、碳水、脂肪、纤维、卡路里)
  • 微量营养素趋势(维生素、矿物质)
  • 热量来源分布变化
  • 餐食模式(饮食时间、频率)
  • 食物类别偏好

输出:

  • 趋势方向(改善/稳定/下降)
  • 变化幅度和百分比
  • 趋势显著性
  • 改进建议
2. 营养素摄入评估

评估营养素摄入是否达到推荐标准(RDA/AI)。

评估内容:

  • 宏量营养素评估:

    • 蛋白质摄入量和质量
    • 碳水化合物类型分布(精制 vs 复杂碳水)
    • 脂肪类型分布(饱和/单不饱和/多不饱和/反式脂肪)
    • 膳食纤维摄入量
  • 维生素评估:

    • 维生素A、C、D、E、K
    • 维生素B族(B1、B2、B3、B6、B12、叶酸、泛酸、生物素)
    • 与RDA对比
    • 缺乏风险评估
  • 矿物质评估:

    • 常量矿物质:钙、磷、镁、钠、钾、氯、硫
    • 微量矿物质:铁、锌、铜、锰、碘、硒、铬、钼
    • 与RDA对比
    • 缺乏风险评估
  • 特殊营养素评估:

    • Omega-3脂肪酸(EPA、DHA、ALA)
    • 胆碱
    • 辅酶Q10
    • 植物化学物(类黄酮、类胡萝卜素等)

输出:

  • 每种营养素的达成率
  • 缺乏/不足/充足/过量分级
  • 缺乏风险识别
  • 优先改善建议
3. 营养状况评估

综合评估用户的营养状况。

评估内容:

  • 整体营养质量评分:

    • 营养密度评分
    • 食物多样性评分
    • 均衡饮食评分
  • 营养模式识别:

    • 饮食模式类型(地中海式、DASH、素食等)
    • 饮食时间模式(进食频率、进食窗口)
    • 零食和加餐模式
  • 营养风险识别:

    • 营养缺乏风险(如维生素D缺乏、铁缺乏)
    • 营养过量风险(如维生素A过量、钠过量)
    • 不健康饮食习惯(高糖、高脂、高钠)

输出:

  • 营养状况等级(优秀/良好/一般/较差)
  • 主要营养问题识别
  • 风险因素列表
  • 改善优先级
4. 相关性分析

分析营养与其他健康指标的相关性。

支持的相关性分析:

  • 营养 ↔ 体重:

    • 卡路里摄入与体重变化的关系
    • 宏量营养素比例与体重管理
    • 进食时间与代谢关系
  • 营养 ↔ 运动:

    • 营养摄入对运动表现的影响
    • 运动日vs休息日的营养需求
    • 蛋白质摄入与肌肉恢复
  • 营养 ↔ 睡眠:

    • 咖啡因摄入与睡眠质量
    • 晚餐时间与入睡时间
    • 特定营养素(如镁、色氨酸)与睡眠
  • 营养 ↔ 血压:

    • 钠摄入与血压
    • 钾/钠比值与血压
    • DASH饮食依从性与血压控制
  • 营养 ↔ 血糖:

    • 碳水化合物类型与血糖波动
    • 膳食纤维与血糖控制
    • 进食时间与血糖曲线

输出:

  • 相关系数(-1到1)
  • 相关性强度(弱/中/强)
  • 统计显著性
  • 因果关系推断
  • 实践建议
5. 个性化建议生成

基于用户数据生成个性化营养改善建议。

建议类型:

  • 营养素调整建议:

    • 增加缺乏的营养素
    • 减少过量的营养素
    • 优化营养素比例
  • 食物选择建议:

    • 推荐特定食物类别
    • 食物替换建议(更健康的选择)
    • 食物搭配建议(促进吸收)
  • 饮食习惯建议:

    • 进食时间调整
    • 餐食频率调整
    • 烹饪方式建议
  • 补充剂建议(仅供参考):

    • 基于缺乏风险的补充剂建议
    • 补充剂剂量和时机
    • 相互作用警示

建议依据:

  • DRIs/RDA标准
  • 用户营养历史数据
  • 用户健康状况和目标
  • 循证营养学证据

使用说明

触发条件

当用户请求以下内容时触发本技能:

  • 营养趋势分析
  • 营养素摄入评估
  • 营养状况评估
  • 营养改善建议
  • 营养与其他健康指标的关联分析
执行步骤
步骤 1: 确定分析范围

明确用户请求的分析类型和时间范围:

  • 分析类型:趋势/评估/相关性/建议
  • 时间范围:周/月/季度/自定义
  • 分析深度:宏量营养素/微量营养素/全面分析
步骤 2: 读取数据

主要数据源:

  1. data-example/nutrition-tracker.json - 营养追踪主数据
  2. data-example/nutrition-logs/YYYY-MM/YYYY-MM-DD.json - 每日饮食记录

关联数据源:

  1. data-example/profile.json - 体重、BMI等基础数据
  2. data-example/fitness-tracker.json - 运动数据
  3. data-example/sleep-tracker.json - 睡眠数据
  4. data-example/hypertension-tracker.json - 血压数据
  5. data-example/diabetes-tracker.json - 血糖数据
步骤 3: 数据分析

根据分析类型执行相应的分析算法:

趋势分析算法:

  • 线性回归计算趋势斜率
  • 移动平均平滑波动
  • 统计显著性检验

RDA达成率计算:

python
rda_achievement = (actual_intake / rda_value) * 100

status_classification:
- < 50%: 严重缺乏
- 50-75%: 不足
- 75-100%: 接近目标
- 100-150%: 充足(理想范围)
- > 150%: 过量(注意安全上限UL)

营养密度评分:

python
nutrient_density_score = (
    (vitamins_achieved / total_vitamins) * 40 +
    (minerals_achieved / total_minerals) * 30 +
    (fiber_achieved / fiber_rda) * 30
)

相关性分析算法:

  • Pearson相关系数计算
  • 滞后相关性分析(考虑时间延迟效应)
  • 多变量回归分析
步骤 4: 生成报告

按照标准格式输出分析报告(见"输出格式"部分)


输出格式

营养趋势分析报告
markdown
# 营养摄入趋势分析报告

## 分析周期
2025-03-20 至 2025-06-20(3个月,90天记录)

## 宏量营养素趋势

### 卡路里摄入
- **趋势**:⬇️ 下降
- **开始**:平均2100卡/天
- **当前**:平均1950卡/天
- **变化**:-150卡/天 (-7.1%)
- **解读**:卡路里摄入适度减少,与减重目标一致

**趋势线**:

2100 ┤ ╭╮ 2050 ┤ ╭╯╰╮ 2000 ┼─╯ ╰╮ 1950 ┤ ╰ 1900 └─────────── 3月 4月 5月 6月


### 蛋白质
- **趋势**:➡️ 稳定
- **平均**:82g/天(范围:70-95g)
- **目标**:80g/天
- **达标率**:93%(84/90天达标)
- **解读**:蛋白质摄入稳定,基本达标

### 膳食纤维
- **趋势**:⬆️ 改善
- **开始**:平均18g/天
- **当前**:平均22g/天
- **变化**:+4g/天 (+22%)
- **目标**:30g/天
- **解读**:纤维摄入显著增加,但仍需继续努力

### 脂肪
- **趋势**:⬇️ 下降
- **开始**:平均75g/天
- **当前**:平均68g/天
- **变化**:-7g/天 (-9.3%)
- **目标**:≤65g/天
- **解读**:脂肪摄入减少,接近目标

**脂肪类型分布变化**:
| 脂肪类型 | 开始 | 当前 | 目标 | 趋势 |
|---------|------|------|------|------|
| 饱和脂肪 | 25g | 20g | <20g | ⬇️ 改善 |
| 单不饱和 | 30g | 32g | >35g | ⬆️ 略增 |
| 多不饱和 | 15g | 12g | 15-20g | ⬇️ 需增加 |
| 反式脂肪 | 2g | 0.5g | 0g | ⬇️ 改善 |

## 维生素状况趋势

### 维生素D
- **摄入趋势**:⬆️ 增加(补充剂开始)
- **开始**:平均2μg/天(饮食来源)
- **当前**:平均52μg/天(含2000IU补充剂)
- **RDA**:15μg/天
- **血清水平变化**:
  - 基线(2025-05):18 ng/mL
  - 当前(2025-06):22 ng/mL
  - 目标:30-100 ng/mL
- **解读**:✅ 补充剂起效,但需继续监测

### 维生素C
- **趋势**:⬆️ 改善
- **开始**:平均65mg/天
- **当前**:平均85mg/天
- **RDA**:100mg/天
- **达标率**:从65% → 85%
- **建议**:增加柑橘类、奇异果、草莓等水果

### B族维生素
- **维生素B12**:✅ 充足(平均2.5μg,RDA 2.4μg)
- **叶酸**:⚠️ 不足(平均320μg,RDA 400μg)
- **B6**:✅ 充足(平均1.5mg,RDA 1.3mg)

## 矿物质趋势

### 钙
- **趋势**:➡️ 稳定
- **平均**:850mg/天
- **RDA**:1000mg/天
- **达标率**:85%
- **主要来源**:乳制品40%、豆腐25%、绿叶蔬菜20%

### 铁
- **趋势**:✅ 充足
- **平均**:12mg/天
- **RDA**:8mg/天(男性)
- **达标率**:150%
- **主要来源**:肉类、蛋类、豆类、绿叶蔬菜

### 钠
- **趋势**:⬇️ 改善
- **开始**:平均2800mg/天
- **当前**:平均2100mg/天
- **目标**:<2300mg/天(理想<1500mg)
- **解读**:✅ 达到一般目标,⚠️ 理想目标仍需努力

### 钾
- **趋势**:⬆️ 改善
- **开始**:平均2800mg/天
- **当前**:平均3200mg/天
- **目标**:3500-4700mg/天
- **钾/钠比值**:从1.0 → 1.5(目标>2)
- **建议**:继续增加水果和蔬菜

## 特殊营养素趋势

### Omega-3
- **趋势**:⬆️ 增加(鱼油补充剂)
- **开始**:平均150mg/天
- **当前**:平均850mg/天(含补充剂)
- **推荐量**:500-1000mg/天
- **状态**:✅ 达标

### 胆碱
- **趋势**:➡️ 稳定
- **平均**:350mg/天
- **AI(适宜摄入量)**:425mg/天
- **达标率**:82%
- **主要来源**:鸡蛋(60%)、肉类(25%)、豆类(15%)

## 饮食模式分析

### 食物类别分布
| 食物类别 | 占比 | 变化 | 评价 |
|---------|------|------|------|
| 蔬菜水果 | 35% | +8% | ✅ 增加 |
| 全谷物 | 20% | +5% | ✅ 改善 |
| 精制谷物 | 15% | -7% | ✅ 减少 |
| 蛋白质来源 | 20% | 稳定 | ✅ 充足 |
| 添加脂肪 | 8% | -3% | ✅ 减少 |
| 添加糖 | 2% | -2% | ✅ 减少 |

### 进食时间模式
- **平均进食窗口**:12.5小时(07:30 - 20:00)
- **进食频率**:平均4.2次/天
- **最常见餐食时间**:
  - 早餐:07:30(90%天数)
  - 午餐:12:15(95%天数)
  - 晚餐:18:45(98%天数)
  - 加餐:15:30(60%天数)

### 饮食质量评分
- **营养密度评分**:7.2/10(从6.5提升)
- **食物多样性评分**:6.8/10
- **均衡饮食评分**:7.5/10
- **综合评分**:7.2/10 → **良好**

## 洞察与建议

### 关键洞察

1. **膳食纤维持续改善但仍不足**
   - 从18g增至22g,但仍低于目标30g
   - 影响:饱腹感、肠道健康、血糖控制
   - 建议:每餐至少包含5g纤维

2. **脂肪质量改善**
   - 饱和脂肪减少,反式脂肪几乎消除
   - 多不饱和脂肪略低,需增加Omega-3食物
   - 建议:增加深海鱼类、坚果、亚麻籽

3. **钠摄入改善但钾/钠比仍低**
   - 钠减少33%,钾增加14%
   - 钾/钠比从1.0升至1.5,仍低于目标2.0
   - 建议:继续增加高钾食物(香蕉、橙子、土豆、菠菜)

4. **维生素D补充剂有效**
   - 血清水平从18升至22 ng/mL(4周+4ng)
   - 预计3-4个月可达目标范围
   - 建议:继续补充,定期监测

### 优先级行动计划

#### Priority 1:提升膳食纤维至30g/天(2周)

**具体行动**:
1. 早餐:全谷物(燕麦/全麦面包)+ 水果(9g)
2. 午餐:糙米/全麦面 + 2份蔬菜(8g)
3. 晚餐:红薯/杂粮 + 2份蔬菜(8g)
4. 加餐:水果 + 坚果(5g)
**总计**:30g ✅

#### Priority 2:优化钾/钠比值至2.0(4周)

**具体行动**:
1. 减少加工食品(主要钠源)
2. 每日2-3份高钾水果(香蕉、橙子、猕猴桃)
3. 蔬菜选择菠菜、土豆、蘑菇、番茄
4. 使用香料替代盐调味

#### Priority 3:维持维生素D补充(长期)

**监测计划**:
- 3个月后复查血清水平
- 目标:40-60 ng/mL
- 根据结果调整剂量

## 营养目标进度

| 目标 | 开始 | 当前 | 目标值 | 进度 | 状态 |
|------|------|------|--------|------|------|
| 卡路里 | 2100 | 1950 | 1800-2000 | 100% | ✅ 达标 |
| 蛋白质 | 75g | 82g | 80g | 100% | ✅ 达标 |
| 膳食纤维 | 18g | 22g | 30g | 73% | ⚠️ 进行中 |
| 维生素D | 18 ng/mL | 22 ng/mL | 30-100 | 20% | ⚠️ 改善中 |
| 钠摄入 | 2800mg | 2100mg | <2300 | 100% | ✅ 达标 |
| Omega-3 | 150mg | 850mg | 500-1000mg | 100% | ✅ 达标 |

---

**报告生成时间**:2025-06-20
**分析周期**:2025-03-20 至 2025-06-20(90天)
**数据记录数**:90天
**营养分析器版本**:v1.0

数据结构

饮食记录数据
json
{
  "date": "2025-06-20",
  "meals": [
    {
      "type": "breakfast",
      "time": "07:30",
      "foods": ["鸡蛋", "牛奶", "全麦面包"],
      "calories": 450,
      "macronutrients": {
        "protein_g": 20,
        "carbs_g": 55,
        "fat_g": 15,
        "fiber_g": 5,
        "saturated_fat_g": 5,
        "monounsaturated_fat_g": 6,
        "polyunsaturated_fat_g": 3,
        "trans_fat_g": 0.1
      },
      "micronutrients": {
        "vitamin_a_mcg": 150,
        "vitamin_c_mg": 5,
        "vitamin_d_mcg": 1.5,
        "vitamin_e_mg": 1,
        "vitamin_k_mcg": 5,
        "thiamine_mg": 0.3,
        "riboflavin_mg": 0.4,
        "niacin_mg": 4,
        "vitamin_b6_mg": 0.1,
        "folate_mcg": 30,
        "vitamin_b12_mcg": 0.6,
        "calcium_mg": 250,
        "iron_mg": 2,
        "magnesium_mg": 40,
        "phosphorus_mg": 200,
        "zinc_mg": 2,
        "selenium_mcg": 10,
        "potassium_mg": 350,
        "sodium_mg": 300
      },
      "special_nutrients": {
        "omega_3_g": 0.1,
        "choline_mg": 150
      }
    }
  ],
  "daily_summary": {
    "total_calories": 2000,
    "total_macronutrients": {
      "protein_g": 80,
      "carbs_g": 250,
      "fat_g": 65,
      "fiber_g": 30
    },
    "rda_achievement": {
      "protein": 100,
      "vitamin_c": 85,
      "vitamin_d": 35,
      "calcium": 90,
      "iron": 75
    },
    "goal_achieved": true
  }
}

算法说明

RDA达成率计算
python
def calculate_rda_achievement(actual_intake, rda_value, ul_value=None):
    """
    计算RDA达成率和状态

    参数:
    - actual_intake: 实际摄入量
    - rda_value: 推荐膳食供给量
    - ul_value: 可耐受最高摄入量(可选)

    返回:
    - achievement_rate: 达成率百分比
    - status: 状态标签
    """
    achievement_rate = (actual_intake / rda_value) * 100

    if ul_value and actual_intake > ul_value:
        status = "exceeds_ul"
        category = "过量(危险)"
    elif achievement_rate < 50:
        status = "severe_deficiency"
        category = "严重缺乏"
    elif achievement_rate < 75:
        status = "insufficient"
        category = "不足"
    elif achievement_rate < 100:
        status = "approaching_target"
        category = "接近目标"
    elif achievement_rate <= 150:
        status = "adequate"
        category = "充足"
    else:
        status = "high_intake"
        category = "较高"

    return {
        'achievement_rate': round(achievement_rate, 1),
        'status': status,
        'category': category
    }
营养密度评分
python
def calculate_nutrient_density_score(meal_data):
    """
    计算食物营养密度评分(0-10分)

    因素权重:
    - 维生素达成率:40%
    - 矿物质达成率:30%
    - 膳食纤维:20%
    - 限制性营养素(饱和脂肪、钠、添加糖):10%
    """
    score = 0

    # 维生素评分
    vitamin_achievements = [
        meal_data['micronutrients'][v] / RDA[v]
        for v in ['vitamin_a', 'vitamin_c', 'vitamin_d', 'vitamin_e', 'vitamin_k']
    ]
    vitamin_score = min(sum(vitamin_achievements) / len(vitamin_achievements), 1.5) * 10
    score += min(vitamin_score, 10) * 0.40

    # 矿物质评分
    mineral_achievements = [
        meal_data['micronutrients'][m] / RDA[m]
        for m in ['calcium', 'iron', 'magnesium', 'zinc']
    ]
    mineral_score = min(sum(mineral_achievements) / len(mineral_achievements), 1.5) * 10
    score += min(mineral_score, 10) * 0.30

    # 膳食纤维评分
    fiber_score = min(meal_data['macronutrients']['fiber_g'] / 5, 2) * 10
    score += min(fiber_score, 10) * 0.20

    # 限制性营养素扣分
    penalty = 0
    if meal_data['macronutrients']['saturated_fat_g'] > 10:
        penalty += 2
    if meal_data['micronutrients']['sodium_mg'] > 600:
        penalty += 2
    if meal_data.get('added_sugars_g', 0) > 10:
        penalty += 2

    score = max(0, score - penalty * 0.10)

    return round(score, 1)
健康饮食指数评分
python
def calculate_healthy_eating_index(daily_data):
    """
    计算健康饮食指数(HEI-2015改编)

    评分范围:0-100分
    """
    score = 0

    # 充足性成分(满分50分)
    # 1. 水果(5分)
    fruit_servings = daily_data['fruit_servings']
    score += min(fruit_servings, 2.5) * 2

    # 2. 蔬菜(5分)
    veg_servings = daily_data['vegetable_servings']
    score += min(veg_servings, 3) * 1.67

    # 3. 全谷物(10分)
    whole_grains_oz = daily_data['whole_grains_oz']
    score += min(whole_grains_oz, 3) * 3.33

    # 4. 乳制品(10分)
    dairy_servings = daily_data['dairy_servings']
    score += min(dairy_servings, 3) * 3.33

    # 5. 蛋白质(5分)
    protein_oz = daily_data['protein_oz']
    score += min(protein_oz, 5) * 1

    # 6. 海鲜/植物蛋白(5分)
    plant_protein_oz = daily_data['plant_protein_oz']
    score += min(plant_protein_oz, 2) * 2.5

    # 7. 脂肪酸比例(10分)
    fat_ratio = daily_data['unsaturated_fat_g'] / max(daily_data['saturated_fat_g'], 1)
    score += min(fat_ratio, 2.5) * 4

    # 适度性成分(满分40分,反向计分)
    # 8. 精制谷物(10分,越少越好)
    refined_grains_oz = daily_data['refined_grains_oz']
    score += max(10 - refined_grains_oz * 2, 0)

    # 9. 钠(10分,越少越好)
    sodium_g = daily_data['sodium_mg'] / 1000
    score += max(10 - sodium_g * 2, 0)

    # 10. 添加糖(10分,越少越好)
    added_sugars_pct = daily_data['added_sugars_g'] / (daily_data['total_calories'] / 100)
    score += max(10 - added_sugars_pct * 10, 0)

    # 11. 饱和脂肪(10分,越少越好)
    saturated_fat_pct = daily_data['saturated_fat_g'] / (daily_data['total_calories'] / 100)
    score += max(10 - saturated_fat_pct * 10, 0)

    return round(score, 1)

医学安全边界

⚠️ 重要声明

本分析仅供健康参考,不构成医疗诊断或营养处方。

分析能力范围

✅ 能做到:

  • 营养数据统计和分析
  • 趋势识别和可视化
  • RDA达成率计算
  • 营养缺乏风险评估
  • 一般性营养建议
  • 补充剂相互作用检查

❌ 不做到:

  • 诊断营养缺乏疾病
  • 开具补充剂处方
  • 替代注册营养师
  • 处理严重营养不良
  • 评估食物过敏
危险信号检测

在分析过程中检测以下危险信号:

  1. 营养素过量:

    • 维生素A > 3000μg(长期)
    • 维生素D > 100μg(长期)
    • 铁 > 45mg(长期)
    • 硒 > 400μg
    • 钠 > 2300mg(持续)
  2. 营养素缺乏:

    • 维生素D < 10μg/天(血清<12 ng/mL)
    • 维生素B12 < 1.5μg/天(素食者)
    • 铁 < 6mg/天(育龄女性)
    • 钙 < 500mg/天
  3. 能量摄入异常:

    • 持续<1200卡/天(可能营养不良)
    • 持续>3500卡/天(可能超重)
  4. 饮食模式异常:

    • 膳食纤维<10g/天
    • 添加糖>25%热量
    • 饱和脂肪>15%热量
建议分级

Level 1: 一般性建议

  • 基于DRIs/RDA标准
  • 适用于一般人群
  • 无需医疗监督

Level 2: 参考性建议

  • 基于用户数据和健康状况
  • 需结合个人情况
  • 建议咨询营养师

Level 3: 医疗建议

  • 涉及疾病管理或补充剂
  • 需医生确认
  • 不得自行调整药物剂量

参考资源


技能版本: v1.0 创建日期: 2026-01-06 维护者: WellAlly Tech

© huifer, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/nutrition-analyzer of huifer/WellAlly-health.

Open the folder on GitHubat commit f604350

Used in 5 other repositories

We found 15 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 5 other GitHub owners. This page covers the copy in huifer/WellAlly-health, which our catalogue first saw on October 7, 2026.

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Questions about Nutrition Analyzer

What does Nutrition Analyzer do?

分析营养数据、识别营养模式、评估营养状况,并提供个性化营养建议。支持与运动、睡眠、慢性病数据的关联分析. An agent skill from huifer/WellAlly-health. Nutrition Analyzer is an agent skill from huifer/WellAlly-health.

When should I use Nutrition Analyzer?

Nutrition Analyzer fits situations like: tasks that involve Health and fitness tracking.

How do I install Nutrition Analyzer in Claude Code?

Run `npx skills add huifer/WellAlly-health --skill nutrition-analyzer -a claude-code`. Or copy the skill folder (skills/nutrition-analyzer in huifer/WellAlly-health) into .claude/skills/nutrition-analyzer in your project. Claude Code loads it when a task matches its description.

How do I install Nutrition Analyzer in Codex?

Run `npx skills add huifer/WellAlly-health --skill nutrition-analyzer -a codex`. Or copy the skill folder (skills/nutrition-analyzer in huifer/WellAlly-health) into .agents/skills/nutrition-analyzer in your project. Codex loads it when a task matches its description.

Can I use Nutrition Analyzer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add huifer/WellAlly-health --skill nutrition-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nutrition-analyzer, .gemini/skills/nutrition-analyzer, .github/skills/nutrition-analyzer and .opencode/skills/nutrition-analyzer in your project.

What does Nutrition Analyzer need to run?

SKILL.md names no scripts, command-line tools or credentials: Nutrition Analyzer is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Grep, Glob, Write.

Does Nutrition Analyzer access the network?

SKILL.md names 5 domains. As links in the text: cnsoc.org, dietaryguidelines.gov, fooddatacentral.usda.gov, who.int and naturalmedicines.therapeuticresearch.com. This is read from the text; nothing was executed.

Is Nutrition Analyzer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Nutrition Analyzer use?

Nutrition Analyzer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nutrition Analyzer use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Nutrition Analyzer?

Skills that share tags, products or a category with Nutrition Analyzer: Coach (felixrieseberg/claude-coach, 199 stars), Master Ajahn Chah (xr843/Master-skill, 447 stars), Ghealth (Google-Health-API/google-health-cli, 266 stars) and Crisis Detection Intervention AI (curiositech/some_claude_skills, 244 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nutrition Analyzer?

huifer (a GitHub user) maintains it in huifer/WellAlly-health, which has 960 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on July 16, 2026.

Source: huifer/WellAlly-health on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.