Skills diet-record

Diet recording skill. Log meals via text description or food photo upload, auto-recognize food items and estimate nutrition/calories. Activate when user sends a food photo, describes what they ate, asks to log a meal, or queries calorie/nutrition info. Triggers include "记录饮食", "午饭吃了", "帮我记一下", "这个多少卡", "拍了张照片", "今天吃了什么", "log my meal", "what did I eat today".

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/734818028/diet-record" ~/.claude/skills/openclaw-skills-diet-record && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/734818028/diet-record" ~/.openclaw/skills/openclaw-skills-diet-record && rm -rf "$T"
manifest: skills/734818028/diet-record/SKILL.md
source content

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

  1. Parse food description (e.g. "一碗牛肉面加个煎蛋")
  2. Break into individual items with estimated portions
  3. Look up nutrition and calculate totals
  4. Append record

Daily Summary

When user asks "今天吃了什么" or "daily summary":

python3 -c "
import json, datetime
today = datetime.date.today().isoformat()
with open('diet-log.jsonl') as f:
    meals = [json.loads(l) for l in f if today in l]
total = sum(m['total_calories'] for m in meals)
print(f'Today: {len(meals)} meals, {total:.0f} kcal')
for m in meals:
    items = ', '.join(i['name'] for i in m['items'])
    print(f\"  {m['meal_type']}: {m['total_calories']:.0f} kcal — {items}\")
"

Nutrition Reference (per 100g)

Common foods for quick lookup (no API needed):

FoodkcalProteinFatCarb
白米饭1162.60.325.9
面条(煮)1103.50.522
鸡胸肉133311.20
鸡蛋(煮)14413101.1
牛肉(瘦)125204.20.2
三文鱼20820130
豆腐738.13.71.5
西兰花342.80.45
番茄180.90.23.9
苹果520.30.213.8
香蕉891.10.322.8
牛奶(全脂)613.23.34.8
酸奶(原味)613.53.34.7
全麦面包247133.441
燕麦片379136.567

For unlisted foods, estimate based on similar items or ask the user for specifics.

Cooking Method Calorie Adjustments

  • 清蒸/水煮: baseline
  • 炒(少油): +10-15%
  • 炒(多油): +20-30%
  • 油炸: +30-50%
  • 烧烤: -5% (fat drips off)

Portion Estimation

  • 一碗米饭 ≈ 200g
  • 一盘菜 ≈ 200-300g
  • 一个拳头 ≈ 150g (水果/肉)
  • 一汤匙油 ≈ 10g (90 kcal)
  • 一杯(240ml)牛奶 ≈ 245g

Key Rules

  • Present results in a clean table format
  • When recognition confidence is low, ask user to confirm before logging
  • Auto-detect meal_type from time: 06-10 breakfast, 11-14 lunch, 17-20 dinner, else snack
  • Remind if daily protein is under 1.2g/kg body weight (when user weight is known)
  • Never make moral judgments about food choices