ClawCoach Food

Food photo analysis and meal logging for ClawCoach. Send a photo of your meal and get instant macro breakdown via Claude Vision.

Security Scan

The skill is internally coherent for a photo-based meal-logging tool (it reasonably needs an Anthropic API key and stores logs locally), but it reads/writes local files and sends images to an external model provider — the skill should explicitly declare those file paths and data flows so you can make an informed choice.

Purpose & Capability

Name and description match the declared requirement (ANTHROPIC_API_KEY) for using Claude Vision; asking for an Anthropic key is appropriate. However, the skill operates on local files (~/.clawcoach/food-log.json and ~/.clawcoach/profile.json) but the registry metadata did not declare any required config paths — the use of those local paths should have been declared.

Instruction Scope

SKILL.md explicitly instructs the agent to analyze images (using vision capabilities), to write confirmed meals to ~/.clawcoach/food-log.json, and to read targets from ~/.clawcoach/profile.json. Those actions are within the stated purpose (meal logging) but they involve reading and writing files in the user's home directory and sending image data to an external model provider; the file I/O and external transmission are material privacy/security behaviors and should be explicitly documented in metadata.

Install Mechanism

Instruction-only skill with no install spec and no code files — lowest-risk install footprint. Nothing will be written to disk by an installer beyond the agent using the instructions at runtime.

Credentials

Only ANTHROPIC_API_KEY is required (appropriate for Claude Vision usage). No unrelated credentials or broad environment access are requested.

Persistence & Privilege

always:false (normal). The skill will create/read files under ~/.clawcoach which is expected for a local meal logger; it does not request system-wide changes or other skills' credentials. Note that the skill is allowed to be invoked autonomously by the agent by default (disable-model-invocation:false) — this is platform-normal but increases the importance of understanding data flows.

Assessment

This skill appears to do what it says: it uses Claude Vision (Anthropic) to analyze meal photos, confirms with the user, and stores confirmed meals in ~/.clawcoach/food-log.json while reading targets from ~/.clawcoach/profile.json. Before installing: 1) Confirm you are comfortable with images being sent to Anthropic (the ANTHROPIC_API_KEY implies outbound image/text data will go to that provider). 2) Be aware that the skill will create and modify files in your home directory (~/.clawcoach/food-log.json and profile.json); review these files after first use and back them up if they will contain sensitive info. 3) Ask the maintainer (or check the repo) to update registry metadata to explicitly list the config paths and to document any additional data sharing, retention, or deletion policies. 4) If you do not want autonomous invocation, disable model invocation for this skill or only enable it when needed. If you need greater assurance, request the source code or a privacy/security statement from the publisher before using.

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:

{
  "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") — recalculate and present updated totals
   • "redo" — ask for a new photo or text description

4. After confirmation, always show updated daily totals.

Text-Based Logging

When the user describes food in text:

1. Parse the food items and estimate portions from the description
2. Calculate macros for each item using your nutritional knowledge
3. Follow the same confirmation flow as photo analysis

Meal Type Auto-Detection

Categorize meals by time:

• Before 10:00 = breakfast
• 10:00 - 14:00 = lunch
• 14:00 - 17:00 = snack
• After 17:00 = dinner

The user can override: "log this as a snack"

Editing and Deleting

• "Delete my lunch" — find today's lunch entry, remove it from food-log.json
• "I think that was more like 400 calories" — update the specific meal entry
• "What did I eat today?" — list all confirmed meals for today with totals

Daily Totals

After any meal is confirmed, calculate and show:

1. Read profile from ~/.clawcoach/profile.json for targets
2. Sum all confirmed meals for today from food-log.json
3. Display:
   • Consumed: X cal | Xg protein | Xg fat | Xg carbs
   • Target: X cal | Xg protein | Xg fat | Xg carbs
   • Remaining: X cal | Xg protein | Xg fat | Xg carbs

Edge Cases

• Blurry or unclear photo: "I can't quite make out the food. Try a better lit photo, or just tell me what you had."
• Non-food photo: "That doesn't look like food! Send a photo of your meal, or type what you ate."
• Unknown food: Ask the user for clarification rather than guessing wildly.
• Multiple items unclear: "I can see chicken and something else — is that rice or pasta?"
• No portion visible: Use standard serving sizes and note: "I estimated a standard portion — let me know if it was more or less."

Nutritional Reference (Common Foods per 100g)

Use these as a baseline. Scale by estimated portion size.

Food	Cal	Protein	Fat	Carbs
Chicken breast (grilled)	165	31	3.6	0
Salmon (baked)	208	20	13	0
White rice (cooked)	130	2.7	0.3	28
Brown rice (cooked)	123	2.7	1.0	26
Pasta (cooked)	131	5	1.1	25
Broccoli (steamed)	35	2.4	0.4	7
Egg (whole, large ~50g)	155	13	11	1.1
Avocado	160	2	15	9
Sweet potato (baked)	90	2	0.1	21
Greek yogurt (plain)	59	10	0.7	3.6
Banana (~120g)	89	1.1	0.3	23
Oats (cooked)	68	2.4	1.4	12
Bread (white, per slice ~30g)	265	9	3.2	49
Cheese (cheddar)	403	25	33	1.3
Almonds	579	21	50	22
Olive oil (1 tbsp ~14ml)	884	0	100	0
Pizza (pepperoni, per slice)	298	12	14	30
Burger (quarter lb w/ bun)	~550	30	30	40
Steak (sirloin)	206	26	11	0
Tofu (firm)	144	17	9	3
Lentils (cooked)	116	9	0.4	20
Milk (whole, 250ml)	61	3.2	3.3	4.8
Protein shake (~1 scoop)	~120	25	1.5	3

For foods not on this list, use your general nutritional knowledge. Be transparent when estimating.

Important

• Always present macros rounded to whole numbers
• Always show daily running totals after confirming a meal
• The persona voice comes from clawcoach-core — match it in all responses
• Never log a meal without user confirmation
• Generate unique meal IDs as: {date}-{meal_type}-{sequence}