snap

name: snap description: Log meals from food photos or natural language descriptions — infer ingredients, call the nutrition tool immediately, and return full micronutrient detail. Do not estimate nutrition in chat manually. user-invocable: true

Use this skill when:

the user sends a likely food photo
the user describes what they ate or are eating (e.g. "had salmon with rice for lunch", "just grabbed a yogurt and some berries")
the user invokes /snap (legacy shortcut)

Behavior rules:

Reply in the user's language.
Always log via the nutrition tool. Never substitute manual text-based nutrition estimation for an actual tool call. The script handles deterministic enrichment (calories, macros, full micronutrients) — your job is to infer ingredients and call the tool, not to play nutritionist in chat.
Infer ingredients and portions, but do not invent detailed nutrient numbers when the script can enrich them deterministically.
After logging, show the full micronutrient breakdown in a compact inline format, for example: Zn 3.2mg · Ca 58mg · VitD 16.4µg · Se 73mcg · Fe 1.8mg · Folate 57µg · Omega-3 1.98g This level of quantitative detail is a core differentiator — do not abbreviate to just "Notable: Vitamin D".

When to proceed vs. when to confirm

Proceed directly (no confirmation needed):
- Photo + any meal context (e.g. "正在吃早餐", "lunch", "having a snack") — the text removes ambiguity.
- Natural language description (e.g. "had salmon and rice") — user intent is clear.
- Photo where you can confidently identify at least the main dish/food category.
Ask ONE brief confirmation, then proceed:
- Photo-only (no accompanying text) AND you genuinely cannot identify the food category (e.g. blurry photo, unfamiliar dish, ambiguous container).
- The confirmation question should be specific: "這看起來像是優格碗，對嗎？" — not an open-ended ingredient list request.
- Any affirmative response ("對", "可以", "是", "幫我判斷", thumbs up) counts as confirmation. After confirmation, immediately call the tool with your best estimate. Do not ask again or offer more estimates.
Never: Do multiple rounds of estimation-and-confirmation before logging. One round max. If you're wrong, the user can correct after seeing the logged result.

Meal-type inference rules:

Always use the current wall-clock time (not photo content) as the primary signal for meal_type:
- 05:00–10:29 → breakfast
- 10:30–14:29 → lunch
- 14:30–17:29 → snack
- 17:30–21:59 → dinner
- 22:00–04:59 → snack
If the user explicitly states a meal type (e.g. "this was my breakfast"), use their stated type regardless of the time.
Never infer meal_type from the visual content of the photo (e.g. do not classify eggs as "breakfast" if it is dinner time).

Logging flow:

Infer a meal-level estimate and decompose it into ingredients.
For each ingredient, provide:
- name
- either amount_g or portion
- optional confidence
Only include explicit nutrient fields if the user supplied a trustworthy label, barcode, or exact recipe and you want the script to preserve those values as provided.
Call the nutrition tool with command: "log" and input_json containing the meal payload:

{
  "command": "log",
  "input_json": {
    "timestamp": "2026-03-18T12:30:00-07:00",
    "meal_type": "lunch",
    "source": "photo",
    "photo_ref": "telegram:file-id-or-message-ref",
    "confidence": 0.82,
    "notes": "optional free text",
    "ingredients": [
      {
        "name": "salmon",
        "amount_g": 150,
        "confidence": 0.78
      }
    ]
  }
}

The logger will:

normalize ingredient names
enrich ingredients from deterministic nutrition data when nutrient fields are omitted
preserve explicitly supplied nutrient values as provided
record the nutrient source in storage

After logging:

confirm what was logged
show meal calories/macros
show the full micronutrient breakdown (all non-zero micronutrients) in compact inline format
include today's running totals if they are useful
keep the response short unless the user asks for detail

Weekly nutrition review:

When the user asks about weekly nutrition (e.g. "how's my nutrition looking this week?", "weekly summary"), call the nutrition tool:

{
  "command": "weekly_summary",
  "end_date": "YYYY-MM-DD",
  "rda_profile": "default"
}

Present the results as:

daily averages for macros and key micronutrients
percentage of RDA for each nutrient
highlight gaps (below 75% of RDA) and strengths (above 100%)
suggest specific foods to address the biggest gaps