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Meal Tracker

Community
AtlasOmnia
meal-tracker

meal-tracker — Track meals and calories from a photo or text description. Vision-based portion estimates, daily calorie/macro budget, markdown food log.

Overview

PublisherAtlasOmnia
Repositorydonna-starter
Skill namemeal-tracker
Stars
131
Forks
9
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by AtlasOmnia on GitHub. Read the source before you install it.

Installation

Install the Meal Tracker AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/AtlasOmnia/donna-starter/tree/main/skills/health/meal-tracker
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AtlasOmnia/donna-starter.git /tmp/donna-starter
mkdir -p .claude/skills
cp -r /tmp/donna-starter/skills/health/meal-tracker .claude/skills/meal-tracker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Meal Tracker in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Meal Tracker on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Meal Tracker is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Meal Tracker

Log meals from a photo or a text description, track calories and macros against the user's daily budget, and keep a dated markdown food log.

Setup (ask once, then remember)

  • Daily calorie target and optional macro targets (protein/fat/carbs in grams).
  • Log location — the user's notes app or a folder (for example an Obsidian vault, ~/Food Logs, or their notes directory). Ask once; do not assume a path.
  • If the user later changes the target, update the stored value and the current log header.

Meal logging workflow

Photo input (user sends image)

  1. Use vision_analyze with the image — ask: "Identify each food item, estimate portion sizes in common units (oz, cups, pieces), and provide calorie + macro breakdown per item."
  2. If the photo is clearly a Nutrition Facts label, skip broad estimation and use the exact visible label values: serving size, calories, protein, fat, carbs, and useful notes (sodium/fiber/sugar). Treat one visible bag/container as one serving unless the user says otherwise.
  3. If the user combines text + image (e.g. "add 1 coffee and this"), log both in the same timestamped meal/snack section.
  4. For quick repeated "Add" messages with a label photo, use the visible label values, append a timestamped snack/drink section to today's log, recalculate totals, and verify the file. Do not ask for product names when the label gives enough to log an accurate entry.
  5. A bare food/label image after an active food-log exchange is an implicit "add this" — log one serving/container from the visible label unless the image suggests multiple servings.
  6. If today's log is missing but the chat already contains same-day entries, reconstruct the daily log from the conversation before appending.
  7. Parse the response into structured entries, log to the daily file, and report: items logged, calories used, remaining for the day.

Text input (user describes a meal)

  1. Estimate portions from common serving sizes when not specified; flag uncertain estimates.
  2. Use standard nutrition values (USDA) for known foods.
  3. For recurring packaged items ("the protein bars", "same coffee"), reuse the most recent matching line item's calories/macros and mention that briefly.
  4. Log to the daily file and report remaining calories.

Batch corrections (user says "make sure X total" or "log N of these")

  1. Read the current daily log.
  2. Adjust line items so the count matches the user's stated total.
  3. Recalculate subtotals and daily totals across all macros, not just calories.
  4. Rewrite the whole section so the math is self-consistent — never leave stale subtotals.

Daily log format

markdown
# [YYYY-MM-DD] Food Log

**Daily Target:** <target> cal | Protein <P>g | Fat <F>g | Carbs <C>g

## Meals

### Breakfast (~[time])
- Item: ~cal | P: Xg | F: Xg | C: Xg
*Subtotal: XXX cal / PXg / FXg / CXg*

### Lunch (~[time])
- ...

### Dinner (~[time])
- ...

### Snacks (~[time])
- ...

## Daily Totals
**Calories:** XXX/<target> (XXX remaining)
**Protein:** XX/<P>g | **Fat:** X/<F>g | **Carbs:** XX/<C>g

Response format

When logging a meal, respond with:

  • What was logged (brief item list)
  • Calories used from the target
  • Remaining calories for the day
  • Flag if over target or running low on protein

Example: "Logged lunch — chicken salad (~520 cal). Down 780/1800 today. 1020 remaining. Protein at 92/140g, still need 48g."

Edge cases

  • Unclear portions: estimate conservatively and flag it: "Estimated small bowl — could be more if larger portion."
  • Restaurant meals: use chain nutrition data when available; estimate otherwise and note uncertainty.
  • Drinks with calories: count them (coffee drinks, alcohol, juice). Water/unsweetened tea/coffee = 0 cal.
  • User corrects a total ("make sure X bars total"): rewrite the log to match and recalculate all subtotals + daily totals from scratch.
  • Recurring ambiguous items: reuse the most recent matching entry when available instead of asking for brand details every time; state the estimate source.

Weekly review (when asked)

  • Average daily calories
  • Protein hit rate (share of days hitting the target)
  • Estimated trend (from daily totals, not medical claims)
  • Suggested adjustments

Privacy and safety

  • Food logs are personal health data: store them only in the user's own files; never send them to third parties or share automatically.
  • This skill tracks food intake only. It is not medical advice: no diagnosis, no supplement or medication interaction guidance, no dosing. Refer the user to a qualified professional for health decisions.

Frequently asked questions

What does the Meal Tracker AI skill do?

meal-tracker — Track meals and calories from a photo or text description. Vision-based portion estimates, daily calorie/macro budget, markdown food log.

Why use Meal Tracker on TypingMind?

Because you install it once and use it with any model. Meal Tracker is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Meal Tracker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AtlasOmnia/donna-starter/tree/main/skills/health/meal-tracker. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Meal Tracker?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Meal Tracker?

As many as you like. As long as a model supports skills, you can use Meal Tracker with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Meal Tracker AI skill free?

Yes. It is published on GitHub by AtlasOmnia under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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