食物数据库查询技能

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/food-database-query

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

食物数据库查询技能 技能名称: Food Database Query 技能类型: 数据查询与分析 创建日期: 2026-01-06 版本: v1.0 ---

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: 技能概述, 数据源, 功能模块, 数据结构, RDA参考值, 集成功能.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 需要查询食物营养成分、比较食物差异或做营养计算时使用。
• 任务涉及食物数据库检索、食物推荐、份量换算或分类筛选。
• 需要基于结构化食物数据生成分析结果而不是自由文本建议时使用。
• Use when the request clearly matches the imported source intent: Food Database Query.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: 技能概述

本技能提供全面的营养食物数据库查询功能,支持食物营养信息查询、比较、推荐和自动营养计算。

核心功能:

• ✅ 食物营养信息查询
• ✅ 食物比较分析
• ✅ 智能食物推荐
• ✅ 自动营养计算
• ✅ 分类浏览和搜索
• ✅ 份量转换和估算

Examples

Example 1: Ask for the upstream workflow directly

Use @food-database-query to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @food-database-query against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @food-database-query for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @food-database-query using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/food-database-query

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @2d-games

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @3d-games

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @daily-gift

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @design-taste-frontend

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: 数据源

主数据库

• 文件:

  data/food-database.json

• 内容: 50种常见食物的详细营养数据
• 结构: 每种食物包含30+营养素指标

分类体系

• 文件:

  data/food-categories.json

• 分类: 10大类,30+子类
• 支持: 按分类浏览和筛选

Imported: 功能模块

1. 食物查询 (Food Query)

1.1 精确查询

用途: 根据食物名称查询营养信息

支持输入:

• 中文名称: "燕麦", "西兰花", "三文鱼"
• 英文名称: "Oats", "Broccoli", "Salmon"
• 别名: "燕麦片", "broccoli", "三文鱼肉"

查询流程:

1. 接收食物名称
2. 在数据库中搜索匹配项
3. 支持模糊匹配和别名匹配
4. 返回完整营养信息

返回信息:

• 基本信息 (名称、分类、标准份量)
• 宏量营养素 (卡路里、蛋白质、碳水、脂肪、纤维)
• 微量营养素 (维生素、矿物质)
• 特殊营养素 (Omega-3/6、胆碱等)
• 升糖指数数据
• 健康标签和适用人群
• 常见份量
• 营养优势说明

示例:

# 用户输入: "燕麦"
# 返回:
{
  "name": "燕麦",
  "name_en": "Oats",
  "category": "谷物类",
  "nutrition_per_100g": {
    "calories": 389,
    "protein_g": 16.9,
    "carbs_g": 66.3,
    "fat_g": 6.9,
    "fiber_g": 10.6,
    # ... 更多营养素
  },
  "health_tags": ["高纤维", "低GI"],
  "glycemic_index": {"value": 55, "level": "低"}
}

1.2 模糊搜索

用途: 根据营养特征搜索食物

搜索条件:

• 营养素含量: "高蛋白", "高纤维", "低GI"
• 营养素组合: "高蛋白 低卡路里", "高纤维 低GI"
• 分类筛选: "谷物类", "蔬菜", "蛋白质"
• 适用人群: "素食友好", "高血压", "糖尿病"

搜索逻辑:

# 示例: 搜索"高蛋白 低卡路里"
def search_foods(criteria):
    results = []
    for food in database:
        protein = food.nutrition_per_100g.protein_g
        calories = food.nutrition_per_100g.calories

        # 定义阈值
        high_protein = protein >= 15  # 每100g≥15g蛋白质
        low_calorie = calories <= 150  # 每100g≤150卡

        if high_protein and low_calorie:
            results.append(food)

    return sorted(results, key=lambda x: x.protein_g, reverse=True)

返回格式:

• 按匹配度排序
• 显示关键营养素
• 标注匹配标签

1.3 分类浏览

用途: 按食物分类浏览所有食物

分类层级:

蛋白质来源
├── 肉类
├── 禽类
├── 鱼虾贝类
├── 蛋类
├── 豆类
├── 坚果种子
└── 乳制品

浏览模式:

• 列出某分类下所有食物
• 按营养素排序
• 按GI值排序
• 按健康标签筛选

2. 食物比较 (Food Comparison)

2.1 双食物比较

功能: 比较两种食物的营养差异

比较维度:

• 宏量营养素: 卡路里、蛋白质、碳水、脂肪、纤维
• 微量营养素: 主要维生素和矿物质
• 升糖指数: GI值、升糖负荷
• 营养密度: 综合评分

计算逻辑:

def compare_foods(food1, food2):
    comparison = {}

    # 宏量营养素差异
    for nutrient in ["calories", "protein_g", "fiber_g"]:
        val1 = food1.nutrition_per_100g[nutrient]
        val2 = food2.nutrition_per_100g[nutrient]
        diff = val1 - val2
        percent = (diff / val2) * 100

        comparison[nutrient] = {
            "food1": val1,
            "food2": val2,
            "difference": diff,
            "percent_change": percent,
            "better": "food1" if diff > 0 else "food2"
        }

    return comparison

输出格式:

• 对比表格
• 差异百分比
• 优势标注
• 推荐建议

2.2 多维度比较

支持模式:

• 全方位营养比较
• 仅比较特定营养素
• 仅比较GI值
• 仅比较特定健康标签

示例:

/nutrition compare 三文鱼 鸡胸肉 营养素

3. 食物推荐 (Food Recommendation)

3.1 基于营养素推荐

推荐逻辑:

def recommend_by_nutrient(nutrient, min_value=None, max_value=None):
    recommendations = []

    for food in database:
        value = food.nutrition_per_100g[nutrient]

        # 筛选符合条件
        if min_value and value < min_value:
            continue
        if max_value and value > max_value:
            continue

        recommendations.append({
            "food": food,
            "value": value,
            "rda_percent": (value / RDA[nutrient]) * 100
        })

    # 按含量排序
    return sorted(recommendations, key=lambda x: x["value"], reverse=True)

推荐类别:

• 高蛋白: ≥15g/100g
• 高纤维: ≥5g/100g
• 低GI: ≤55
• 富含维生素C: ≥50mg/100g
• 富含Omega-3: ≥1g/100g
• 高钙: ≥100mg/100g
• 高铁: ≥3mg/100g

3.2 多条件推荐

支持组合条件:

• "高蛋白 低卡路里"
• "高纤维 低GI"
• "富含铁 素食友好"

排序策略:

1. 按第一优先级排序
2. 筛选符合第二条件的
3. 综合评分排序

3.3 基于健康状况推荐

高血压 (DASH饮食):

• 低钠食物
• 高钾食物
• 高镁、高钙食物

糖尿病:

• 低GI食物
• 高纤维食物
• 低碳水化合物

高血脂:

• 高Omega-3食物
• 低饱和脂肪
• 高纤维食物

骨质疏松:

• 高钙食物
• 富含维生素D
• 高镁、高锌

贫血:

• 富含铁
• 富含叶酸
• 富含维生素B12

4. 自动营养计算 (Auto Nutrition Calculation)

4.1 食物识别

输入解析:

def parse_food_input(text):
    # 示例: "燕麦粥 1杯 + 鸡蛋 1个 + 牛奶 250ml"

    foods = []
    portions = []

    # 识别食物名称
    for item in text.split("+"):
        food_name = extract_food_name(item)  # "燕麦粥"
        portion = extract_portion(item)      # "1杯"

        # 标准化食物名称
        standard_name = normalize_food_name(food_name)  # "燕麦"

        # 查询数据库
        food_data = query_database(standard_name)

        foods.append(food_data)
        portions.append(parse_portion(portion))

    return foods, portions

4.2 份量转换

常见份量:

• "1杯": 240ml (液体) 或 重量依据食物
• "1个": 鸡蛋50g, 苹果150g
• "1片": 面包30g
• "100g": 直接使用

份量数据库:

{
  "common_portions": [
    {
      "amount": 1,
      "unit": "个",
      "weight_g": 50,
      "description": "1个大号鸡蛋"
    },
    {
      "amount": 1,
      "unit": "杯",
      "weight_g": 240,
      "description": "1杯牛奶"
    }
  ]
}

4.3 营养计算

计算公式:

def calculate_nutrition(food, portion_grams):
    nutrition = {}

    for nutrient, value_per_100g in food.nutrition_per_100g.items():
        # 按100g比例计算
        nutrition[nutrient] = (value_per_100g * portion_grams) / 100

    return nutrition

4.4 烹饪影响修正

考虑因素:

• 煮熟后重量变化
• 维生素损失
• 营养素保留率

示例:

• 燕麦生:100g → 煮熟:约300g (3倍重量)
• 维生素保留: 煮熟保留60-80%

5. 智能搜索 (Smart Search)

5.1 别名匹配

支持同义词:

• "燕麦" = "燕麦片" = "oats" = "rolled oats"
• "西兰花" = "绿花菜" = "broccoli"

匹配算法:

def find_food(name):
    # 1. 精确匹配主名称
    if name in database:
        return database[name]

    # 2. 匹配别名
    for food in database:
        if name in food.aliases:
            return food

    # 3. 模糊匹配
    matches = fuzzy_search(name)
    if matches:
        return matches[0]

    return None

5.2 拼写纠错

编辑距离算法:

def fuzzy_search(name, max_distance=2):
    matches = []

    for food in database:
        # 计算编辑距离
        distance = levenshtein_distance(name, food.name)

        if distance <= max_distance:
            matches.append((food, distance))

    # 按距离排序
    return sorted(matches, key=lambda x: x[1])

Imported: 数据结构

食物数据结构

{
  "id": "FD_001",
  "name": "燕麦",
  "name_en": "Oats",
  "aliases": ["燕麦片", "oats", "rolled oats"],
  "category": "grains",
  "subcategory": "whole_grains",

  "standard_portion": {
    "amount": 100,
    "unit": "g",
    "description": "100克"
  },

  "nutrition_per_100g": {
    "calories": 389,
    "protein_g": 16.9,
    "carbs_g": 66.3,
    "fat_g": 6.9,
    "fiber_g": 10.6,
    "sugar_g": 0.99,
    "saturated_fat_g": 1.4,
    "monounsaturated_fat_g": 2.5,
    "polyunsaturated_fat_g": 2.9,
    "trans_fat_g": 0,
    "water_g": 8.9,

    "vitamin_a_mcg": 0,
    "vitamin_c_mg": 0,
    "vitamin_d_mcg": 0,
    "vitamin_e_mg": 1.1,
    "vitamin_k_mcg": 1.9,
    "thiamine_mg": 0.763,
    "riboflavin_mg": 0.139,
    "niacin_mg": 6.921,
    "vitamin_b6_mg": 0.165,
    "folate_mcg": 56,
    "vitamin_b12_mcg": 0,
    "pantothenic_acid_mg": 1.349,
    "biotin_mcg": 0,

    "calcium_mg": 54,
    "iron_mg": 4.72,
    "magnesium_mg": 177,
    "phosphorus_mg": 523,
    "potassium_mg": 429,
    "sodium_mg": 2,
    "zinc_mg": 3.97,
    "copper_mg": 0.526,
    "manganese_mg": 4.916,
    "selenium_mcg": 2.8,
    "iodine_mcg": 0
  },

  "special_nutrients": {
    "omega_3_g": 0.685,
    "omega_6_g": 1.428,
    "choline_mg": 43.4,
    "beta_carotene_mcg": 0,
    "lutein_mcg": 0,
    "zeaxanthin_mcg": 0
  },

  "glycemic_index": {
    "value": 55,
    "level": "低",
    "glycemic_load": 11
  },

  "common_portions": [
    {
      "amount": 30,
      "unit": "g",
      "description": "1/4杯",
      "approximate_volume": "1/4 cup"
    },
    {
      "amount": 40,
      "unit": "g",
      "description": "1/3杯",
      "approximate_volume": "1/3 cup"
    },
    {
      "amount": 200,
      "unit": "ml",
      "description": "煮熟1杯",
      "notes": "煮熟后体积增加"
    }
  ],

  "cooking_effects": {
    "boiling": {
      "weight_change_percent": 200,
      "nutrient_changes": {
        "vitamin_c_retention": 0,
        "b_vitamins_retention": 60
      }
    }
  },

  "health_tags": ["高纤维", "低GI", "无麸质选项", "心脏健康"],

  "suitable_for": ["素食者", "高血压", "糖尿病", "高血脂"],

  "notes": "富含β-葡聚糖,有助于降低胆固醇"
}

Imported: RDA参考值

成年男性 (19-50岁)

RDA = {
  # 宏量营养素
  "calories": 2500,  # 中等活动水平
  "protein_g": 56,
  "carbs_g": 130,  # 最低值
  "fiber_g": 38,

  # 维生素
  "vitamin_a_mcg": 900,
  "vitamin_c_mg": 90,
  "vitamin_d_mcg": 15,
  "vitamin_e_mg": 15,
  "vitamin_k_mcg": 120,
  "thiamine_mg": 1.2,
  "riboflavin_mg": 1.3,
  "niacin_mg": 16,
  "vitamin_b6_mg": 1.3,
  "folate_mcg": 400,
  "vitamin_b12_mcg": 2.4,
  "pantothenic_acid_mg": 5,
  "biotin_mcg": 30,

  # 矿物质
  "calcium_mg": 1000,
  "iron_mg": 8,
  "magnesium_mg": 400,
  "phosphorus_mg": 700,
  "potassium_mg": 3400,
  "sodium_mg": 1500,  # 上限
  "zinc_mg": 11,
  "copper_mg": 0.9,
  "manganese_mg": 2.3,
  "selenium_mcg": 55
}

成年女性 (19-50岁)

RDA_FEMALE = {
  "calories": 2000,  # 中等活动水平
  "protein_g": 46,
  "fiber_g": 25,
  "iron_mg": 18,  # 育龄期
  # ... 其他略有差异
}

Imported: 集成功能

与营养模块集成

1. 记录饮食: 自动查询营养数据
2. 营养分析: 基于数据库的精确计算
3. 营养建议: 数据驱动的食物推荐

与健康模块集成

1. 高血压: 推荐DASH饮食友好食物
2. 糖尿病: 筛选低GI食物
3. 高血脂: 推荐高Omega-3食物

与运动模块集成

1. 运动前后: 推荐合适的食物
2. 增肌: 高蛋白食物推荐
3. 减脂: 低卡路里高蛋白食物

Imported: 使用示例

示例1: 记录早餐

用户输入:

/nutrition record breakfast 燕麦粥 1杯 + 鸡蛋 1个 + 牛奶 250ml

系统处理:

1. 识别食物: 燕麦、鸡蛋、牛奶
2. 查询营养数据
3. 计算份量营养
4. 汇总整餐营养
5. 记录到日志

返回结果:

✅ 早餐已记录

**食物**: 燕麦粥(1杯) + 鸡蛋(1个) + 牛奶(250ml)

**营养汇总**:
- 卡路里: 417 卡
- 蛋白质: 25.1g
- 碳水化合物: 48.5g
- 脂肪: 15.2g
- 膳食纤维: 8.2g

**微量营养素亮点**:
- 维生素D: 3.1 μg (21% RDA)
- 钙: 332 mg (33% RDA)
- 维生素B12: 1.3 μg (54% RDA)

示例2: 查询食物

用户输入:

/nutrition food 三文鱼

返回结果:

# 三文鱼 营养信息

#### Imported: 基本信息

- **名称**: 三文鱼 (Salmon)
- **分类**: 蛋白质来源 > 鱼虾贝类
- **标准份量**: 100克

#### Imported: 宏量营养素 (每100克)

- **卡路里**: 208 卡
- **蛋白质**: 20g ✅
- **碳水化合物**: 0g
- **脂肪**: 13g
- **Omega-3**: 2.5g ✅✅✅

#### Imported: 营养亮点

- ✅✅✅ 富含Omega-3脂肪酸 (EPA+DHA)
- ✅✅ 高质量蛋白质
- ✅ 富含维生素D (11μg)
- ✅ 富含维生素B12 (3.2μg)

#### Imported: 健康标签

- ✅ 高蛋白
- ✅ 富含Omega-3
- ✅ 心脏健康
- ✅ 大脑健康

#### Imported: 推荐份量

- 100-150g/餐 (每周2-3次)

示例3: 比较食物

用户输入:

/nutrition compare 鸡胸肉 三文鱼

返回结果:

# 食物比较: 鸡胸肉 vs 三文鱼

#### Imported: 营养对比 (每100克)

| 营养素 | 鸡胸肉 | 三文鱼 | 差异 |
|--------|--------|--------|------|
| 卡路里 | 165 | 208 | +26% |
| 蛋白质 (g) | 31 | 20 | -35% ✅ |
| 脂肪 (g) | 3.6 | 13 | +261% |
| Omega-3 (g) | 0.1 | 2.5 | +2400% ✅✅✅ |

#### Imported: 推荐建议

**选择鸡胸肉更适合**:
- ✅ 减脂期间 (低卡高蛋白)
- ✅ 控制脂肪摄入
- ✅ 蛋白质需求高

**选择三文鱼更适合**:
- ✅ 心脏健康 (高Omega-3)
- ✅ 大脑健康 (DHA)
- ✅ 抗炎需求

Imported: 扩展计划

短期 (1-2个月)

• ✅ 完成50种常见食物
• ⏳ 扩展至100种食物
• ⏳ 添加更多常见份量
• ⏳ 优化搜索算法

中期 (3-6个月)

• ⏳ 扩展至300种食物
• ⏳ 添加品牌食品
• ⏳ 支持用户自定义食物
• ⏳ 添加食物照片

长期 (持续)

• ⏳ 持续更新数据库
• ⏳ 添加季节性食物
• ⏳ 集成条形码扫描
• ⏳ AI食物识别

Imported: 质量保证

数据准确性

• 来源: 《中国食物成分表(第6版)》+ USDA
• 验证: 交叉验证多个来源
• 更新: 定期更新数据

功能测试

• 查询准确性测试
• 计算精度测试
• 边界条件测试
• 性能测试

Imported: 注意事项

⚠️ 重要限制

1. 数据范围: 当前仅覆盖50种常见食物
2. 烹饪影响: 数据基于生食/标准烹饪
3. 个体差异: 实际营养吸收因人而异
4. 地域差异: 不同地区食物营养可能不同

⚠️ 使用建议

1. 均衡饮食: 不要依赖单一食物
2. 多样化选择: 轮换不同食物
3. 适量原则: 即使健康食物也需适量
4. 专业指导: 特殊需求咨询营养师

Imported: 技术实现

文件位置

• 数据库:

  data/food-database.json

• 分类:

  data/food-categories.json

• 命令:

  .claude/commands/nutrition.md

• 技能:

  .claude/skills/food-database-query/SKILL.md

性能优化

• 数据库索引 (食物名称、分类)
• 缓存常用查询
• 模糊搜索优化

技能版本: v1.0 最后更新: 2026-01-06 维护者: WellAlly Tech

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.