{"id":"ac66e3ab-0b76-4004-a4b1-b057e60c12ef","shortId":"A6KurM","kind":"skill","title":"food-database-query","tagline":"Food Database Query","description":"# 食物数据库查询技能\n\n**技能名称**: Food Database Query\n**技能类型**: 数据查询与分析\n**创建日期**: 2026-01-06\n**版本**: v1.0\n\n---\n\n## When to Use\n- 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。\n- 任务涉及食物数据库检索、食物推荐、份量换算或分类筛选。\n- 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。\n\n## 技能概述\n\n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。\n\n**核心功能**:\n- ✅ 食物营养信息查询\n- ✅ 食物比较分析\n- ✅ 智能食物推荐\n- ✅ 自动营养计算\n- ✅ 分类浏览和搜索\n- ✅ 份量转换和估算\n\n---\n\n## 数据源\n\n### 主数据库\n- **文件**: `data/food-database.json`\n- **内容**: 50种常见食物的详细营养数据\n- **结构**: 每种食物包含30+营养素指标\n\n### 分类体系\n- **文件**: `data/food-categories.json`\n- **分类**: 10大类,30+子类\n- **支持**: 按分类浏览和筛选\n\n---\n\n## 功能模块\n\n### 1. 食物查询 (Food Query)\n\n#### 1.1 精确查询\n\n**用途**: 根据食物名称查询营养信息\n\n**支持输入**:\n- 中文名称: \"燕麦\", \"西兰花\", \"三文鱼\"\n- 英文名称: \"Oats\", \"Broccoli\", \"Salmon\"\n- 别名: \"燕麦片\", \"broccoli\", \"三文鱼肉\"\n\n**查询流程**:\n1. 接收食物名称\n2. 在数据库中搜索匹配项\n3. 支持模糊匹配和别名匹配\n4. 返回完整营养信息\n\n**返回信息**:\n- 基本信息 (名称、分类、标准份量)\n- 宏量营养素 (卡路里、蛋白质、碳水、脂肪、纤维)\n- 微量营养素 (维生素、矿物质)\n- 特殊营养素 (Omega-3/6、胆碱等)\n- 升糖指数数据\n- 健康标签和适用人群\n- 常见份量\n- 营养优势说明\n\n**示例**:\n```python\n# 用户输入: \"燕麦\"\n# 返回:\n{\n  \"name\": \"燕麦\",\n  \"name_en\": \"Oats\",\n  \"category\": \"谷物类\",\n  \"nutrition_per_100g\": {\n    \"calories\": 389,\n    \"protein_g\": 16.9,\n    \"carbs_g\": 66.3,\n    \"fat_g\": 6.9,\n    \"fiber_g\": 10.6,\n    # ... 更多营养素\n  },\n  \"health_tags\": [\"高纤维\", \"低GI\"],\n  \"glycemic_index\": {\"value\": 55, \"level\": \"低\"}\n}\n```\n\n#### 1.2 模糊搜索\n\n**用途**: 根据营养特征搜索食物\n\n**搜索条件**:\n- 营养素含量: \"高蛋白\", \"高纤维\", \"低GI\"\n- 营养素组合: \"高蛋白 低卡路里\", \"高纤维 低GI\"\n- 分类筛选: \"谷物类\", \"蔬菜\", \"蛋白质\"\n- 适用人群: \"素食友好\", \"高血压\", \"糖尿病\"\n\n**搜索逻辑**:\n```python\n# 示例: 搜索\"高蛋白 低卡路里\"\ndef search_foods(criteria):\n    results = []\n    for food in database:\n        protein = food.nutrition_per_100g.protein_g\n        calories = food.nutrition_per_100g.calories\n\n        # 定义阈值\n        high_protein = protein >= 15  # 每100g≥15g蛋白质\n        low_calorie = calories <= 150  # 每100g≤150卡\n\n        if high_protein and low_calorie:\n            results.append(food)\n\n    return sorted(results, key=lambda x: x.protein_g, reverse=True)\n```\n\n**返回格式**:\n- 按匹配度排序\n- 显示关键营养素\n- 标注匹配标签\n\n#### 1.3 分类浏览\n\n**用途**: 按食物分类浏览所有食物\n\n**分类层级**:\n```\n蛋白质来源\n├── 肉类\n├── 禽类\n├── 鱼虾贝类\n├── 蛋类\n├── 豆类\n├── 坚果种子\n└── 乳制品\n```\n\n**浏览模式**:\n- 列出某分类下所有食物\n- 按营养素排序\n- 按GI值排序\n- 按健康标签筛选\n\n---\n\n### 2. 食物比较 (Food Comparison)\n\n#### 2.1 双食物比较\n\n**功能**: 比较两种食物的营养差异\n\n**比较维度**:\n- **宏量营养素**: 卡路里、蛋白质、碳水、脂肪、纤维\n- **微量营养素**: 主要维生素和矿物质\n- **升糖指数**: GI值、升糖负荷\n- **营养密度**: 综合评分\n\n**计算逻辑**:\n```python\ndef compare_foods(food1, food2):\n    comparison = {}\n\n    # 宏量营养素差异\n    for nutrient in [\"calories\", \"protein_g\", \"fiber_g\"]:\n        val1 = food1.nutrition_per_100g[nutrient]\n        val2 = food2.nutrition_per_100g[nutrient]\n        diff = val1 - val2\n        percent = (diff / val2) * 100\n\n        comparison[nutrient] = {\n            \"food1\": val1,\n            \"food2\": val2,\n            \"difference\": diff,\n            \"percent_change\": percent,\n            \"better\": \"food1\" if diff > 0 else \"food2\"\n        }\n\n    return comparison\n```\n\n**输出格式**:\n- 对比表格\n- 差异百分比\n- 优势标注\n- 推荐建议\n\n#### 2.2 多维度比较\n\n**支持模式**:\n- 全方位营养比较\n- 仅比较特定营养素\n- 仅比较GI值\n- 仅比较特定健康标签\n\n**示例**: `/nutrition compare 三文鱼 鸡胸肉 营养素`\n\n---\n\n### 3. 食物推荐 (Food Recommendation)\n\n#### 3.1 基于营养素推荐\n\n**推荐逻辑**:\n```python\ndef recommend_by_nutrient(nutrient, min_value=None, max_value=None):\n    recommendations = []\n\n    for food in database:\n        value = food.nutrition_per_100g[nutrient]\n\n        # 筛选符合条件\n        if min_value and value < min_value:\n            continue\n        if max_value and value > max_value:\n            continue\n\n        recommendations.append({\n            \"food\": food,\n            \"value\": value,\n            \"rda_percent\": (value / RDA[nutrient]) * 100\n        })\n\n    # 按含量排序\n    return sorted(recommendations, key=lambda x: x[\"value\"], reverse=True)\n```\n\n**推荐类别**:\n- **高蛋白**: ≥15g/100g\n- **高纤维**: ≥5g/100g\n- **低GI**: ≤55\n- **富含维生素C**: ≥50mg/100g\n- **富含Omega-3**: ≥1g/100g\n- **高钙**: ≥100mg/100g\n- **高铁**: ≥3mg/100g\n\n#### 3.2 多条件推荐\n\n**支持组合条件**:\n- \"高蛋白 低卡路里\"\n- \"高纤维 低GI\"\n- \"富含铁 素食友好\"\n\n**排序策略**:\n1. 按第一优先级排序\n2. 筛选符合第二条件的\n3. 综合评分排序\n\n#### 3.3 基于健康状况推荐\n\n**高血压 (DASH饮食)**:\n- 低钠食物\n- 高钾食物\n- 高镁、高钙食物\n\n**糖尿病**:\n- 低GI食物\n- 高纤维食物\n- 低碳水化合物\n\n**高血脂**:\n- 高Omega-3食物\n- 低饱和脂肪\n- 高纤维食物\n\n**骨质疏松**:\n- 高钙食物\n- 富含维生素D\n- 高镁、高锌\n\n**贫血**:\n- 富含铁\n- 富含叶酸\n- 富含维生素B12\n\n---\n\n### 4. 自动营养计算 (Auto Nutrition Calculation)\n\n#### 4.1 食物识别\n\n**输入解析**:\n```python\ndef parse_food_input(text):\n    # 示例: \"燕麦粥 1杯 + 鸡蛋 1个 + 牛奶 250ml\"\n\n    foods = []\n    portions = []\n\n    # 识别食物名称\n    for item in text.split(\"+\"):\n        food_name = extract_food_name(item)  # \"燕麦粥\"\n        portion = extract_portion(item)      # \"1杯\"\n\n        # 标准化食物名称\n        standard_name = normalize_food_name(food_name)  # \"燕麦\"\n\n        # 查询数据库\n        food_data = query_database(standard_name)\n\n        foods.append(food_data)\n        portions.append(parse_portion(portion))\n\n    return foods, portions\n```\n\n#### 4.2 份量转换\n\n**常见份量**:\n- \"1杯\": 240ml (液体) 或 重量依据食物\n- \"1个\": 鸡蛋50g, 苹果150g\n- \"1片\": 面包30g\n- \"100g\": 直接使用\n\n**份量数据库**:\n```json\n{\n  \"common_portions\": [\n    {\n      \"amount\": 1,\n      \"unit\": \"个\",\n      \"weight_g\": 50,\n      \"description\": \"1个大号鸡蛋\"\n    },\n    {\n      \"amount\": 1,\n      \"unit\": \"杯\",\n      \"weight_g\": 240,\n      \"description\": \"1杯牛奶\"\n    }\n  ]\n}\n```\n\n#### 4.3 营养计算\n\n**计算公式**:\n```python\ndef calculate_nutrition(food, portion_grams):\n    nutrition = {}\n\n    for nutrient, value_per_100g in food.nutrition_per_100g.items():\n        # 按100g比例计算\n        nutrition[nutrient] = (value_per_100g * portion_grams) / 100\n\n    return nutrition\n```\n\n#### 4.4 烹饪影响修正\n\n**考虑因素**:\n- 煮熟后重量变化\n- 维生素损失\n- 营养素保留率\n\n**示例**:\n- 燕麦生:100g → 煮熟:约300g (3倍重量)\n- 维生素保留: 煮熟保留60-80%\n\n---\n\n### 5. 智能搜索 (Smart Search)\n\n#### 5.1 别名匹配\n\n**支持同义词**:\n- \"燕麦\" = \"燕麦片\" = \"oats\" = \"rolled oats\"\n- \"西兰花\" = \"绿花菜\" = \"broccoli\"\n\n**匹配算法**:\n```python\ndef find_food(name):\n    # 1. 精确匹配主名称\n    if name in database:\n        return database[name]\n\n    # 2. 匹配别名\n    for food in database:\n        if name in food.aliases:\n            return food\n\n    # 3. 模糊匹配\n    matches = fuzzy_search(name)\n    if matches:\n        return matches[0]\n\n    return None\n```\n\n#### 5.2 拼写纠错\n\n**编辑距离算法**:\n```python\ndef fuzzy_search(name, max_distance=2):\n    matches = []\n\n    for food in database:\n        # 计算编辑距离\n        distance = levenshtein_distance(name, food.name)\n\n        if distance <= max_distance:\n            matches.append((food, distance))\n\n    # 按距离排序\n    return sorted(matches, key=lambda x: x[1])\n```\n\n---\n\n## 数据结构\n\n### 食物数据结构\n\n```json\n{\n  \"id\": \"FD_001\",\n  \"name\": \"燕麦\",\n  \"name_en\": \"Oats\",\n  \"aliases\": [\"燕麦片\", \"oats\", \"rolled oats\"],\n  \"category\": \"grains\",\n  \"subcategory\": \"whole_grains\",\n\n  \"standard_portion\": {\n    \"amount\": 100,\n    \"unit\": \"g\",\n    \"description\": \"100克\"\n  },\n\n  \"nutrition_per_100g\": {\n    \"calories\": 389,\n    \"protein_g\": 16.9,\n    \"carbs_g\": 66.3,\n    \"fat_g\": 6.9,\n    \"fiber_g\": 10.6,\n    \"sugar_g\": 0.99,\n    \"saturated_fat_g\": 1.4,\n    \"monounsaturated_fat_g\": 2.5,\n    \"polyunsaturated_fat_g\": 2.9,\n    \"trans_fat_g\": 0,\n    \"water_g\": 8.9,\n\n    \"vitamin_a_mcg\": 0,\n    \"vitamin_c_mg\": 0,\n    \"vitamin_d_mcg\": 0,\n    \"vitamin_e_mg\": 1.1,\n    \"vitamin_k_mcg\": 1.9,\n    \"thiamine_mg\": 0.763,\n    \"riboflavin_mg\": 0.139,\n    \"niacin_mg\": 6.921,\n    \"vitamin_b6_mg\": 0.165,\n    \"folate_mcg\": 56,\n    \"vitamin_b12_mcg\": 0,\n    \"pantothenic_acid_mg\": 1.349,\n    \"biotin_mcg\": 0,\n\n    \"calcium_mg\": 54,\n    \"iron_mg\": 4.72,\n    \"magnesium_mg\": 177,\n    \"phosphorus_mg\": 523,\n    \"potassium_mg\": 429,\n    \"sodium_mg\": 2,\n    \"zinc_mg\": 3.97,\n    \"copper_mg\": 0.526,\n    \"manganese_mg\": 4.916,\n    \"selenium_mcg\": 2.8,\n    \"iodine_mcg\": 0\n  },\n\n  \"special_nutrients\": {\n    \"omega_3_g\": 0.685,\n    \"omega_6_g\": 1.428,\n    \"choline_mg\": 43.4,\n    \"beta_carotene_mcg\": 0,\n    \"lutein_mcg\": 0,\n    \"zeaxanthin_mcg\": 0\n  },\n\n  \"glycemic_index\": {\n    \"value\": 55,\n    \"level\": \"低\",\n    \"glycemic_load\": 11\n  },\n\n  \"common_portions\": [\n    {\n      \"amount\": 30,\n      \"unit\": \"g\",\n      \"description\": \"1/4杯\",\n      \"approximate_volume\": \"1/4 cup\"\n    },\n    {\n      \"amount\": 40,\n      \"unit\": \"g\",\n      \"description\": \"1/3杯\",\n      \"approximate_volume\": \"1/3 cup\"\n    },\n    {\n      \"amount\": 200,\n      \"unit\": \"ml\",\n      \"description\": \"煮熟1杯\",\n      \"notes\": \"煮熟后体积增加\"\n    }\n  ],\n\n  \"cooking_effects\": {\n    \"boiling\": {\n      \"weight_change_percent\": 200,\n      \"nutrient_changes\": {\n        \"vitamin_c_retention\": 0,\n        \"b_vitamins_retention\": 60\n      }\n    }\n  },\n\n  \"health_tags\": [\"高纤维\", \"低GI\", \"无麸质选项\", \"心脏健康\"],\n\n  \"suitable_for\": [\"素食者\", \"高血压\", \"糖尿病\", \"高血脂\"],\n\n  \"notes\": \"富含β-葡聚糖,有助于降低胆固醇\"\n}\n```\n\n---\n\n## RDA参考值\n\n### 成年男性 (19-50岁)\n\n```python\nRDA = {\n  # 宏量营养素\n  \"calories\": 2500,  # 中等活动水平\n  \"protein_g\": 56,\n  \"carbs_g\": 130,  # 最低值\n  \"fiber_g\": 38,\n\n  # 维生素\n  \"vitamin_a_mcg\": 900,\n  \"vitamin_c_mg\": 90,\n  \"vitamin_d_mcg\": 15,\n  \"vitamin_e_mg\": 15,\n  \"vitamin_k_mcg\": 120,\n  \"thiamine_mg\": 1.2,\n  \"riboflavin_mg\": 1.3,\n  \"niacin_mg\": 16,\n  \"vitamin_b6_mg\": 1.3,\n  \"folate_mcg\": 400,\n  \"vitamin_b12_mcg\": 2.4,\n  \"pantothenic_acid_mg\": 5,\n  \"biotin_mcg\": 30,\n\n  # 矿物质\n  \"calcium_mg\": 1000,\n  \"iron_mg\": 8,\n  \"magnesium_mg\": 400,\n  \"phosphorus_mg\": 700,\n  \"potassium_mg\": 3400,\n  \"sodium_mg\": 1500,  # 上限\n  \"zinc_mg\": 11,\n  \"copper_mg\": 0.9,\n  \"manganese_mg\": 2.3,\n  \"selenium_mcg\": 55\n}\n```\n\n### 成年女性 (19-50岁)\n\n```python\nRDA_FEMALE = {\n  \"calories\": 2000,  # 中等活动水平\n  \"protein_g\": 46,\n  \"fiber_g\": 25,\n  \"iron_mg\": 18,  # 育龄期\n  # ... 其他略有差异\n}\n```\n\n---\n\n## 集成功能\n\n### 与营养模块集成\n\n1. **记录饮食**: 自动查询营养数据\n2. **营养分析**: 基于数据库的精确计算\n3. **营养建议**: 数据驱动的食物推荐\n\n### 与健康模块集成\n\n1. **高血压**: 推荐DASH饮食友好食物\n2. **糖尿病**: 筛选低GI食物\n3. **高血脂**: 推荐高Omega-3食物\n\n### 与运动模块集成\n\n1. **运动前后**: 推荐合适的食物\n2. **增肌**: 高蛋白食物推荐\n3. **减脂**: 低卡路里高蛋白食物\n\n---\n\n## 使用示例\n\n### 示例1: 记录早餐\n\n**用户输入**:\n```\n/nutrition record breakfast 燕麦粥 1杯 + 鸡蛋 1个 + 牛奶 250ml\n```\n\n**系统处理**:\n1. 识别食物: 燕麦、鸡蛋、牛奶\n2. 查询营养数据\n3. 计算份量营养\n4. 汇总整餐营养\n5. 记录到日志\n\n**返回结果**:\n```markdown\n✅ 早餐已记录\n\n**食物**: 燕麦粥(1杯) + 鸡蛋(1个) + 牛奶(250ml)\n\n**营养汇总**:\n- 卡路里: 417 卡\n- 蛋白质: 25.1g\n- 碳水化合物: 48.5g\n- 脂肪: 15.2g\n- 膳食纤维: 8.2g\n\n**微量营养素亮点**:\n- 维生素D: 3.1 μg (21% RDA)\n- 钙: 332 mg (33% RDA)\n- 维生素B12: 1.3 μg (54% RDA)\n```\n\n### 示例2: 查询食物\n\n**用户输入**:\n```\n/nutrition food 三文鱼\n```\n\n**返回结果**:\n```markdown\n# 三文鱼 营养信息\n\n## 基本信息\n- **名称**: 三文鱼 (Salmon)\n- **分类**: 蛋白质来源 > 鱼虾贝类\n- **标准份量**: 100克\n\n## 宏量营养素 (每100克)\n- **卡路里**: 208 卡\n- **蛋白质**: 20g ✅\n- **碳水化合物**: 0g\n- **脂肪**: 13g\n- **Omega-3**: 2.5g ✅✅✅\n\n## 营养亮点\n- ✅✅✅ 富含Omega-3脂肪酸 (EPA+DHA)\n- ✅✅ 高质量蛋白质\n- ✅ 富含维生素D (11μg)\n- ✅ 富含维生素B12 (3.2μg)\n\n## 健康标签\n- ✅ 高蛋白\n- ✅ 富含Omega-3\n- ✅ 心脏健康\n- ✅ 大脑健康\n\n## 推荐份量\n- 100-150g/餐 (每周2-3次)\n```\n\n### 示例3: 比较食物\n\n**用户输入**:\n```\n/nutrition compare 鸡胸肉 三文鱼\n```\n\n**返回结果**:\n```markdown\n# 食物比较: 鸡胸肉 vs 三文鱼\n\n## 营养对比 (每100克)\n\n| 营养素 | 鸡胸肉 | 三文鱼 | 差异 |\n|--------|--------|--------|------|\n| 卡路里 | 165 | 208 | +26% |\n| 蛋白质 (g) | 31 | 20 | -35% ✅ |\n| 脂肪 (g) | 3.6 | 13 | +261% |\n| Omega-3 (g) | 0.1 | 2.5 | +2400% ✅✅✅ |\n\n## 推荐建议\n\n**选择鸡胸肉更适合**:\n- ✅ 减脂期间 (低卡高蛋白)\n- ✅ 控制脂肪摄入\n- ✅ 蛋白质需求高\n\n**选择三文鱼更适合**:\n- ✅ 心脏健康 (高Omega-3)\n- ✅ 大脑健康 (DHA)\n- ✅ 抗炎需求\n```\n\n---\n\n## 扩展计划\n\n### 短期 (1-2个月)\n- ✅ 完成50种常见食物\n- ⏳ 扩展至100种食物\n- ⏳ 添加更多常见份量\n- ⏳ 优化搜索算法\n\n### 中期 (3-6个月)\n- ⏳ 扩展至300种食物\n- ⏳ 添加品牌食品\n- ⏳ 支持用户自定义食物\n- ⏳ 添加食物照片\n\n### 长期 (持续)\n- ⏳ 持续更新数据库\n- ⏳ 添加季节性食物\n- ⏳ 集成条形码扫描\n- ⏳ AI食物识别\n\n---\n\n## 质量保证\n\n### 数据准确性\n- 来源: 《中国食物成分表(第6版)》+ USDA\n- 验证: 交叉验证多个来源\n- 更新: 定期更新数据\n\n### 功能测试\n- 查询准确性测试\n- 计算精度测试\n- 边界条件测试\n- 性能测试\n\n---\n\n## 注意事项\n\n### ⚠️ 重要限制\n1. **数据范围**: 当前仅覆盖50种常见食物\n2. **烹饪影响**: 数据基于生食/标准烹饪\n3. **个体差异**: 实际营养吸收因人而异\n4. **地域差异**: 不同地区食物营养可能不同\n\n### ⚠️ 使用建议\n1. **均衡饮食**: 不要依赖单一食物\n2. **多样化选择**: 轮换不同食物\n3. **适量原则**: 即使健康食物也需适量\n4. **专业指导**: 特殊需求咨询营养师\n\n---\n\n## 技术实现\n\n### 文件位置\n- 数据库: `data/food-database.json`\n- 分类: `data/food-categories.json`\n- 命令: `.claude/commands/nutrition.md`\n- 技能: `.claude/skills/food-database-query/SKILL.md`\n\n### 性能优化\n- 数据库索引 (食物名称、分类)\n- 缓存常用查询\n- 模糊搜索优化\n\n---\n\n**技能版本**: v1.0\n**最后更新**: 2026-01-06\n**维护者**: WellAlly Tech\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.","tags":["food","database","query","antigravity","awesome","skills","sickn33","agent-skills","agentic-skills","ai-agent-skills","ai-agents","ai-coding"],"capabilities":["skill","source-sickn33","skill-food-database-query","topic-agent-skills","topic-agentic-skills","topic-ai-agent-skills","topic-ai-agents","topic-ai-coding","topic-ai-workflows","topic-antigravity","topic-antigravity-skills","topic-claude-code","topic-claude-code-skills","topic-codex-cli","topic-codex-skills"],"categories":["antigravity-awesome-skills"],"synonyms":[],"warnings":[],"endpointUrl":"https://skills.sh/sickn33/antigravity-awesome-skills/food-database-query","protocol":"skill","transport":"skills-sh","auth":{"type":"none","details":{"cli":"npx skills add sickn33/antigravity-awesome-skills","source_repo":"https://github.com/sickn33/antigravity-awesome-skills","install_from":"skills.sh"}},"qualityScore":"0.700","qualityRationale":"deterministic score 0.70 from registry signals: · indexed on github topic:agent-skills · 34793 github stars · SKILL.md body (11,984 chars)","verified":false,"liveness":"unknown","lastLivenessCheck":null,"agentReviews":{"count":0,"score_avg":null,"cost_usd_avg":null,"success_rate":null,"latency_p50_ms":null,"narrative_summary":null,"summary_updated_at":null},"enrichmentModel":"deterministic:skill-github:v1","enrichmentVersion":1,"enrichedAt":"2026-04-24T00:50:57.484Z","embedding":null,"createdAt":"2026-04-18T21:37:21.425Z","updatedAt":"2026-04-24T00:50:57.484Z","lastSeenAt":"2026-04-24T00:50:57.484Z","tsv":"'+2400':1278 '+26':1262 '+261':1272 '-01':17,1378 '-06':18,1379 '-150':1234 '-2':1295 '-3':107,421,1211,1229,1274,1288 '-35':1267 '-50':959,1059 '-6':1303 '-80':616 '/6':108 '/nutrition':338,1115,1183,1243 '0':320,669,774,781,785,789,817,824,857,874,877,880,934 '0.1':1276 '0.139':803 '0.165':810 '0.526':848 '0.685':863 '0.763':800 '0.9':1050 '0.99':758 '001':715 '0g':1207 '1':61,83,437,556,565,638,709,1080,1090,1102,1125,1294,1332,1346 '1.1':65,793 '1.2':154,1000 '1.3':231,1003,1010,1176 '1.349':821 '1.4':762 '1.428':867 '1.9':797 '1/3':908,912 '1/4':897,901 '10.6':142,755 '100':304,399,599,734,1233 '1000':1028 '100g':128,291,296,370,549,588,596,610,741 '100mg/100g':424 '100克':738,1198 '10大类':55 '11':889,1047 '11μg':1222 '120':997 '13':1271 '130':972 '13g':1209 '15':200,989,993 '15.2':1159 '150':206 '1500':1043 '150卡':208 '15g/100g':413 '15g蛋白质':202 '16':1006 '16.9':133,746 '165':1260 '177':833 '18':1075 '19':958,1058 '1g/100g':422 '1个':488,544,1121,1145 '1个大号鸡蛋':563 '1杯':486,509,539,1119,1143 '1杯牛奶':572 '1片':547 '2':85,249,439,647,682,842,1083,1093,1105,1130,1335,1349 '2.1':253 '2.2':330 '2.3':1053 '2.4':1017 '2.5':766,1212,1277 '2.8':854 '2.9':770 '20':1266 '200':915,928 '2000':1065 '2026':16,1377 '208':1202,1261 '20g':1205 '21':1168 '240':570 '240ml':540 '25':1072 '25.1':1153 '2500':965 '250ml':490,1123,1147 '3':87,343,441,659,861,1086,1096,1108,1132,1302,1339,1352 '3.1':347,1166 '3.2':427,1224 '3.3':443 '3.6':1270 '3.97':845 '30':56,893,1024 '31':1265 '33':1173 '332':1171 '3400':1040 '38':976 '389':130,743 '3mg/100g':426 '3倍重量':613 '3次':1239 '3脂肪酸':1217 '3食物':458,1100 '4':89,470,1134,1342,1355 '4.1':475 '4.2':536 '4.3':573 '4.4':602 '4.72':830 '4.916':851 '40':904 '400':1013,1034 '417':1150 '429':839 '43.4':870 '46':1069 '48.5':1156 '5':617,1021,1136 '5.1':621 '5.2':672 '50':561 '50mg/100g':419 '50种常见食物的详细营养数据':47 '523':836 '54':827,1178 '55':151,417,884,1056 '56':813,969 '5g/100g':415 '6':865 '6.9':139,752 '6.921':806 '60':938 '66.3':136,749 '700':1037 '8':1031 '8.2':1162 '8.9':777 '90':985 '900':981 'acid':819,1019 'ai食物识别':1314 'alias':721 'amount':555,564,733,892,903,914 'approxim':899,910 'ask':1416 'auto':472 'b':935 'b12':815,1015 'b6':808,1008 'beta':871 'better':316 'biotin':822,1022 'boil':924 'boundari':1424 'breakfast':1117 'broccoli':76,80,631 'c':783,932,983 'calcium':825,1026 'calcul':474,578 'calori':129,194,204,205,214,283,742,964,1064 'carb':134,747,970 'caroten':872 'categori':124,726 'chang':314,926,930 'cholin':868 'clarif':1418 'claude/commands/nutrition.md':1365 'claude/skills/food-database-query/skill.md':1367 'clear':1391 'common':553,890 'compar':274,339,1244 'comparison':252,278,305,324 'continu':380,388 'cook':922 'copper':846,1048 'criteria':185,1427 'cup':902,913 'd':787,987 'dash饮食':446 'data':521,528 'data/food-categories.json':53,1363 'data/food-database.json':45,1361 'databas':3,6,11,190,366,523,643,645,652,687 'def':182,273,351,479,577,634,676 'describ':1395 'descript':562,571,737,896,907,918 'dha':1219,1290 'diff':298,302,312,319 'differ':311 'distanc':681,689,691,695,697,700 'e':791,991 'effect':923 'els':321 'en':122,719 'environ':1407 'environment-specif':1406 'epa':1218 'expert':1412 'extract':500,506 'fat':137,750,760,764,768,772 'fd':714 'femal':1063 'fiber':140,286,753,974,1070 'find':635 'folat':811,1011 'food':2,5,10,63,184,188,216,251,275,345,364,390,391,481,491,498,501,514,516,520,527,534,580,636,650,658,685,699,1184 'food-database-queri':1 'food.aliases':656 'food.name':693 'food.nutrition':368 'food.nutrition_per_100g.calories':195 'food.nutrition_per_100g.items':590 'food.nutrition_per_100g.protein':192 'food1':276,307,317 'food1.nutrition':289 'food2':277,309,322 'food2.nutrition':294 'foods.append':526 'fuzzi':662,677 'g':132,135,138,141,193,224,285,287,560,569,736,745,748,751,754,757,761,765,769,773,776,862,866,895,906,968,971,975,1068,1071,1154,1157,1160,1163,1213,1235,1264,1269,1275 'gi值':267 'glycem':148,881,887 'grain':727,730 'gram':582,598 'health':144,939 'high':197,210 'id':713 'index':149,882 'input':482,1421 'iodin':855 'iron':828,1029,1073 'item':495,503,508 'json':552,712 'k':795,995 'key':220,404,705 'lambda':221,405,706 'level':152,885 'levenshtein':690 'limit':1383 'load':888 'low':203,213 'lutein':875 'magnesium':831,1032 'manganes':849,1051 'markdown':1139,1187,1248 'match':661,666,668,683,704,1392 'matches.append':698 'max':359,382,386,680,696 'mcg':780,788,796,812,816,823,853,856,873,876,879,980,988,996,1012,1016,1023,1055 'mg':784,792,799,802,805,809,820,826,829,832,835,838,841,844,847,850,869,984,992,999,1002,1005,1009,1020,1027,1030,1033,1036,1039,1042,1046,1049,1052,1074,1172 'min':356,374,378 'miss':1429 'ml':917 'monounsatur':763 'name':119,121,499,502,512,515,517,525,637,641,646,654,664,679,692,716,718 'niacin':804,1004 'none':358,361,671 'normal':513 'note':920,951 'nutrient':281,292,297,306,354,355,371,398,585,593,859,929 'nutrit':126,473,579,583,592,601,739 'oat':75,123,626,628,720,723,725 'omega':106,860,864,1210,1273 'output':1401 'pantothen':818,1018 'pars':480,530 'per':127,290,295,369,587,595,740 'percent':301,313,315,395,927 'permiss':1422 'phosphorus':834,1035 'polyunsatur':767 'portion':492,505,507,531,532,535,554,581,597,732,891 'portions.append':529 'potassium':837,1038 'protein':131,191,198,199,211,284,744,967,1067 'python':115,177,272,350,478,576,633,675,961,1061 'queri':4,7,12,64,522 'rda':394,397,962,1062,1169,1174,1179 'rda参考值':956 'recommend':346,352,362,403 'recommendations.append':389 'record':1116 'requir':1420 'result':186,219 'results.append':215 'retent':933,937 'return':217,323,401,533,600,644,657,667,670,702 'revers':225,409 'review':1413 'riboflavin':801,1001 'roll':627,724 'safeti':1423 'salmon':77,1193 'satur':759 'scope':1394 'search':183,620,663,678 'selenium':852,1054 'skill':1386 'skill-food-database-query' 'smart':619 'sodium':840,1041 'sort':218,402,703 'source-sickn33' 'special':858 'specif':1408 'standard':511,524,731 'stop':1414 'subcategori':728 'substitut':1404 'success':1426 'sugar':756 'suitabl':945 'tag':145,940 'task':1390 'tech':1382 'test':1410 'text':483 'text.split':497 'thiamin':798,998 'topic-agent-skills' 'topic-agentic-skills' 'topic-ai-agent-skills' 'topic-ai-agents' 'topic-ai-coding' 'topic-ai-workflows' 'topic-antigravity' 'topic-antigravity-skills' 'topic-claude-code' 'topic-claude-code-skills' 'topic-codex-cli' 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'低gi食物':452 '低卡路里':165,181,431 '低卡路里高蛋白食物':1110 '低卡高蛋白':1282 '低碳水化合物':454 '低钠食物':447 '低饱和脂肪':459 '使用建议':1345 '使用示例':1111 '健康标签':1226 '健康标签和适用人群':111 '全方位营养比较':333 '其他略有差异':1077 '内容':46 '减脂':1109 '减脂期间':1281 '分类':54,94,1194,1362,1371 '分类体系':51 '分类层级':235 '分类浏览':232 '分类浏览和搜索':40 '分类筛选':168 '列出某分类下所有食物':245 '创建日期':15 '别名':78 '别名匹配':622 '功能':255 '功能模块':60 '功能测试':1325 '匹配别名':648 '匹配算法':632 '升糖指数':266 '升糖指数数据':110 '升糖负荷':268 '卡':1151,1203 '卡路里':97,259,1149,1201,1259 '即使健康食物也需适量':1354 '双食物比较':254 '名称':93,1191 '命令':1364 '在数据库中搜索匹配项':86 '地域差异':1343 '均衡饮食':1347 '坚果种子':242 '基于健康状况推荐':444 '基于数据库的精确计算':1085 '基于营养素推荐':348 '基本信息':92,1190 '增肌':1106 '多条件推荐':428 '多样化选择':1350 '多维度比较':331 '大脑健康':1231,1289 '子类':57 '完成50种常见食物':1297 '宏量营养素':96,258,963,1199 '宏量营养素差异':279 '定义阈值':196 '定期更新数据':1324 '实际营养吸收因人而异':1341 '富含omega':420,1216,1228 '富含omega-3脂肪酸':1215 '富含β':953 '富含β-葡聚糖':952 '富含叶酸':468 '富含维生素b12':469,1223 '富含维生素c':418 '富含维生素d':463,1221 '富含铁':434,467 '对比表格':326 '岁':960,1060 '差异':1258 '差异百分比':327 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