Diet Logger

Record meals via photo or text, auto-recognize food items and calculate nutrition.

Data Storage

All diet records are stored in diet-log.jsonl (same directory as this skill file, one JSON object per line). Create the file if it doesn't exist.

Each record schema:

{
  "id": "uuid",
  "timestamp": "ISO-8601",
  "meal_type": "breakfast|lunch|dinner|snack",
  "items": [
    {
      "name": "食物名称",
      "portion_g": 150,
      "calories_kcal": 230,
      "protein_g": 12,
      "fat_g": 8,
      "carb_g": 28,
      "fiber_g": 2
    }
  ],
  "total_calories": 460,
  "notes": ""
}

User Preferences

Stored in diet-preferences.json (same directory as this skill file). Create the file if it doesn't exist.

{
  "photo_auto_log": null,
  "dietary_restrictions": [],
  "allergies": [],
  "disliked_foods": [],
  "favorite_foods": [],
  "diet_goal": null,
  "daily_calorie_target": null,
  "meal_routine": null,
  "notes": ""
}

Fields:

• photo_auto_log: true = auto-log on photo upload, false = confirm first, null = not yet set.
• dietary_restrictions: e.g. ["素食", "清真", "无麸质", "低碳水"]
• allergies: e.g. ["花生", "海鲜", "乳糖不耐"]
• disliked_foods: foods user explicitly dislikes
• favorite_foods: frequently eaten or preferred foods
• diet_goal: e.g. "减脂", "增肌", "维持体重", "均衡饮食"
• daily_calorie_target: e.g. 1800 (kcal), null if not set
• meal_routine: e.g. "一日三餐", "16:8轻断食", "少食多餐"
• notes: any other dietary habits or notes from user

Preference Discovery

Photo auto-log preference: On the first food photo upload (or when photo_auto_log is null), recognize items as usual, then ask: "以后发食物照片时，要自动帮你记录饮食吗？还是每次先确认再记录？"

Dietary habits: Whenever user mentions dietary preferences, restrictions, allergies, goals, or habits in conversation, extract and save to the corresponding fields. Examples:

• "我对花生过敏" → add "花生" to allergies
• "我在减脂" → set diet_goal to "减脂"
• "我不吃香菜" → add "香菜" to disliked_foods
• "我每天控制在1500卡" → set daily_calorie_target to 1500
• "我在做16:8轻断食" → set meal_routine to "16:8轻断食"

Preferences are accumulated over time — update individual fields without overwriting unrelated ones. Read preferences before each interaction to provide personalized feedback (e.g. warn if a meal exceeds calorie target, flag allergens in recognized food).

Workflow

Photo Input

1. Receive food photo from user
2. Read diet-preferences.json to check photo_auto_log
3. Analyze the image: identify each food item, estimate portion size
4. Look up nutrition data per item (use the reference table below)
5. If photo_auto_log is null: present result, ask preference (see above), then log
6. If photo_auto_log is true: calculate totals, log directly, respond with summary
7. If photo_auto_log is false: present recognized items — ask user to confirm or correct, then log
8. Append record to diet-log.jsonl

Text Input