clawcoach-food

# ClawCoach Food — Photo Analysis & Meal Logging

This skill handles food photo analysis via Claude Vision, text-based meal logging, and the confirmation flow.

## When to Activate

- User sends a photo — assume it is food unless context clearly suggests otherwise
- User types a food description ("I had 2 eggs and toast for breakfast")
- User says "log [food]" or "I ate [food]"
- User wants to edit or delete a previous meal

## Data Storage

All meals are stored in `~/.clawcoach/food-log.json` with this structure:

```json
{
  "meals": [
    {
      "id": "2026-02-22-lunch-001",
      "date": "2026-02-22",
      "type": "lunch",
      "status": "confirmed",
      "items": [
        {
          "name": "grilled chicken breast",
          "portion": "6 oz",
          "calories": 280,
          "protein_g": 52,
          "fat_g": 6,
          "carbs_g": 0
        }
      ],
      "total_calories": 520,
      "total_protein_g": 62,
      "total_fat_g": 14,
      "total_carbs_g": 48,
      "source": "photo",
      "timestamp": "2026-02-22T12:35:00Z"
    }
  ]
}
```

## Photo Analysis Flow

When the user sends a photo:

1. **Analyze the image** using your vision capabilities. Identify every distinct food item visible. For each item estimate:
   - Name (be specific: "grilled chicken breast" not just "chicken")
   - Portion in common units (oz, cups, pieces, slices)
   - Calories and macros (protein, fat, carbs in grams)

   Use your nutritional knowledge. For common foods, these are well-established values. Be conservative with portions if uncertain.

2. **Present the results** in the user's persona voice:
   - List each item with portion and macros
   - Show meal total
   - Show daily running totals (consumed / target / remaining)
   - Ask: "confirm? (yes / edit / redo)"

3. **Handle response:**
   - **"yes" / "confirm"** — Write the meal to `~/.clawcoach/food-log.json` with status "confirmed"
   - **Correction** (e.g., "the rice was brown rice" or "it was more like 8oz")...