Nutrition And Fitness Tracker
Users log food or activity via webhook; the system estimates calories, updates daily totals in Google Sheets.
developers and engineersHealthcare
What it does
You automatically log your meals and workouts via a webhook to instantly get calorie and protein estimates while tracking your daily totals in Google Sheets. You receive immediate Slack alerts whenever you exceed your limits or face potential health risks, ensuring you stay informed about your nutrition and fitness progress without manual entry.
Step by step
- Configure a webhook endpoint to receive raw user input strings.
- Set up a code node to parse the JSON payload and extract the text field.
- Define Google Sheets columns for date, food item, calories, protein, and activity type.
- Create Slack workflow steps that trigger only when specific boolean flags are true.
Where the LLM does the work
- Prompt the OpenAI model to classify whether the input describes a food item or physical activity.
- Ask the LLM to estimate calorie count and protein content based on common portion sizes for the identified items.
- Instruct the model to flag potential health risks such as excessive sugar intake or missing macronutrients.
Watch out for
Ambiguous user inputs like 'lunch' can confuse the classifier, so add a system prompt that requests specific details or defaults to a generic entry.
Google Sheets API quotas may be exceeded during high-volume testing, so implement batch updates instead of row-by-row writes.
Slack alerts might spam users if thresholds are too sensitive, so add a cooldown period or daily summary aggregation for non-critical warnings.
Calorie estimates from LLMs can vary significantly between runs, so standardize the output format and consider adding a confidence score to each prediction.
Tools that fit
OpenAI Chat Model LLM Classify food or activity and estimate nutrition data
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Nutrition And Fitness Tracker: Users log food or activity via webhook; the system estimates calories, updates daily totals in Google Sheets. ## Specification - What it does: You automatically log your meals and workouts via a webhook to instantly get calorie and protein estimates while tracking your daily totals in Google Sheets. You receive immediate Slack alerts whenever you exceed your limits or face potential health risks, ensuring you stay informed about your nutrition and fitness progress without manual entry. - Trigger: Triggered by an event (Webhook input and Scheduled daily reset) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - OpenAI Chat Model: Classify food or activity and estimate nutrition data - Google Sheets: Store user profiles and daily metrics - Slack: Send alerts for calorie limits or health risks ## Known pitfalls, handle each one explicitly in your implementation 1. Ambiguous user inputs like 'lunch' can confuse the classifier, so add a system prompt that requests specific details or defaults to a generic entry. 2. Google Sheets API quotas may be exceeded during high-volume testing, so implement batch updates instead of row-by-row writes. 3. Slack alerts might spam users if thresholds are too sensitive, so add a cooldown period or daily summary aggregation for non-critical warnings. 4. Calorie estimates from LLMs can vary significantly between runs, so standardize the output format and consider adding a confidence score to each prediction. ## Reference implementation https://n8n.io/workflows/14858 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/automated-nutrition-and-fitness-tracker/ via usecasesforagents.com
Frequently asked questions
Can I use a different LLM than OpenAI?
Yes. OpenAI is only the example LLM in this recipe. The same flow works with Anthropic, Cohere, DeepSeek and Google Gemini. Swap the LLM connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for developers who want the busywork around code automated, not the code itself.
Also fits Healthcare.