Automate Your Fitness API Weekly Report with n8n

Pulling data from a fitness API every week, trying to summarize it, then turning it into something useful for your team or users can feel like a chore, right? If you’re doing it by hand, it’s easy to miss trends, forget a step, or just run out of time.

This is where the Fitness API Weekly Report workflow template in n8n steps in. It handles the whole pipeline for you: it ingests your weekly data, turns it into embeddings, stores those vectors in Supabase, runs a RAG (retrieval-augmented generation) agent to create a smart summary, then logs everything in Google Sheets and pings Slack if something breaks.

In this guide, we’ll walk through what this template does, when it’s worth using, and how to get it running in your own n8n setup, without going into dry, textbook mode. Think of it as a practical walkthrough with all the technical details preserved.

What this n8n template actually does

Let’s start with the big picture. The workflow takes a weekly payload from your fitness API, processes it with AI, and stores the results in a way that’s easy to track over time.

Here’s the core flow, simplified:

• Webhook Trigger – receives the JSON payload from your fitness data source.
• Text Splitter – breaks long text or logs into manageable chunks.
• Embeddings (Cohere) – converts those chunks into numeric vectors.
• Supabase Insert – stores vectors in a dedicated vector table.
• Supabase Query + Vector Tool – retrieves relevant chunks when the AI needs context.
• Window Memory – keeps short-term context during the conversation or report generation.
• RAG Agent – uses the vector store and a chat model to generate a weekly report.
• Append Sheet – adds the final report as a new row in Google Sheets.
• Slack Alert – sends a message to Slack if something goes wrong.

The result: every week, you get a consistent, AI-generated summary of fitness activity, stored in a sheet you can search, chart, or share.

Why automate weekly fitness reports in the first place?

You might be wondering: is it really worth automating this? In most cases, yes.

• Save time – no more manual copying, pasting, or writing summaries.
• Reduce human error – the workflow runs the same way every time.
• Stay consistent – weekly reports actually happen every week, not “when someone gets to it.”
• Highlight trends – fitness data is all about patterns, outliers, and progress over time.

This is especially helpful for product teams working with fitness apps, coaches who want regular insights, or power users tracking their own performance. Instead of spending energy on data wrangling, you can focus on decisions and improvements.

When to use this template

This n8n workflow template is a great fit if:

• You receive weekly or periodic fitness data from an API or aggregator.
• You want summaries, insights, or recommendations instead of raw logs.
• You need a central log of reports, like a Google Sheet, for auditing or tracking.
• You care about alerts when something fails instead of silently missing a week.

If your data is irregular, very large, or needs heavy preprocessing, you can still use this template as a base and customize it, but the default setup is optimized for weekly reporting.

How the workflow is structured

Let’s walk through the main pieces of the pipeline and how they fit together. We’ll start from the incoming data and end with the final report and alerts.

1. Webhook Trigger: the entry point

The workflow starts with a Webhook Trigger node. This node listens for incoming POST requests from your fitness API or from a scheduler that aggregates weekly data.

Key settings:

• Method: POST
• Path: something like /fitness-api-weekly-report
• Security: use a secret token, IP allow-listing, or both.

The webhook expects a JSON payload that includes user details, dates, activities, and optionally notes or comments.

Sample webhook payload

Here’s an example of what your fitness data aggregator might send to the webhook:

{  "user_id": "user_123",  "week_start": "2025-08-18",  "week_end": "2025-08-24",  "activities": [  {"date":"2025-08-18","type":"run","distance_km":5.2,"duration_min":28},  {"date":"2025-08-20","type":"cycle","distance_km":20.1,"duration_min":62},  {"date":"2025-08-23","type":"strength","exercises":12}  ],  "notes":"High HR during runs; hydration may be low."
}

You can adapt this structure to match your own API, as long as the workflow knows where to find the relevant fields.

2. Text Splitter: prepping content for embeddings

Once the raw JSON is in n8n, the workflow converts the relevant data into text and passes it through a Text Splitter node. This is important if you have long logs or multi-day summaries that would be too big to embed in one go.

Typical configuration:

• Chunk size: 400 characters
• Chunk overlap: 40 characters

These values keep each chunk semantically meaningful while allowing a bit of overlap so context is not lost between chunks.

3. Embeddings with Cohere: turning text into vectors

Next, the workflow uses the Embeddings (Cohere) node. Each chunk of text is sent to Cohere’s embed-english-v3.0 model (or another embeddings model you prefer) and transformed into a numeric vector.

Setup steps:

• Store your Cohere API key in n8n credentials, not in the workflow itself.
• Select the embed-english-v3.0 model or an equivalent embedding model.
• Map the text field from the Text Splitter to the embeddings input.

These vectors are what make similarity search possible later, which is crucial for the RAG agent to find relevant context.

4. Supabase as your vector store

Once embeddings are created, they’re stored in Supabase, which acts as the vector database for this workflow.

Supabase Insert

The Supabase Insert node writes each vector into a table or index, typically named:

fitness_api_weekly_report

Along with the vector itself, you can store metadata such as user_id, dates, and raw text. This makes it easier to filter or debug later.

Supabase Query

When the RAG agent needs context, the workflow uses a Supabase Query node to retrieve the most relevant vectors. The query runs a similarity search against the vector index and returns the top matches.

This is what lets the agent “remember” previous activities or notes when generating a weekly summary.

5. Vector Tool: connecting Supabase to the RAG agent

To make Supabase usable by the AI agent, the workflow exposes it as a Vector Tool. This tool is what the agent calls when it needs extra context.

Typical configuration:

• Name: something friendly, like Supabase
• Description: clearly explain that this tool retrieves relevant fitness context from a vector store.

A clear name and description help the agent understand when and how to use this tool during report generation.

6. Window Memory: short-term context

The Window Memory node keeps a limited history of recent messages and summaries so the agent can maintain a sense of continuity during the workflow run.

This is especially useful if the workflow involves multiple internal steps or if you extend it later to handle follow-up questions or multi-part reports.

7. RAG Agent: generating the weekly report

Now comes the fun part: the RAG Agent. This agent combines:

• A system prompt that defines its role.
• Access to the vector tool backed by Supabase.
• Window memory for short-term context.

For example, your system prompt might look like:

You are an assistant for Fitness API Weekly Report.

The agent uses this prompt, plus the retrieved vector context, to generate a concise weekly summary that typically includes:

• A short recap of the week’s activities.
• Status or notable changes, such as performance shifts or unusual metrics.

Example output from the RAG agent

Here’s a sample of the kind of report you might see:

Week: 2025-08-18 to 2025-08-24
User: user_123
Summary: The user completed 2 cardio sessions (run, cycle) and 1 strength session. Running pace was slower than usual with elevated heart rate; hydration flagged.
Recommendations: Reduce intensity on next run, increase hydration, schedule mobility work.

You can customize the prompt to change tone, structure, or level of detail depending on your use case.

8. Append Sheet: logging reports in Google Sheets

Once the RAG agent generates the weekly report, the Append Sheet node writes it into a Google Sheet so you have a persistent record.

Typical setup:

• Sheet name: Log
• Columns: include fields like Week, User, Status, Summary, or whatever fits your schema.
• Mapping: map the RAG agent output to a column such as Status or Report.

This makes it easy to filter by user, date, or status, and to share reports with stakeholders who live in spreadsheets.

9. Slack Alert: catching errors quickly

If something fails along the way, you probably don’t want to discover it three weeks later. The workflow routes errors to a Slack Alert node that posts a message in a channel, for example:

#alerts

The message typically includes the error details so you can troubleshoot quickly. You can also add retry logic or backoff strategies if you want to handle transient issues more gracefully.

Best practices for this workflow

To keep this automation reliable and cost-effective, a few habits go a long way.

• Secure your webhook: use HMAC signatures or a token header so only your systems can call it.
• Tune chunk size: if your data is very short or extremely long, try different chunk sizes and overlaps to see what works best.
• Watch embedding costs: embedding APIs usually bill per token, so consider batching and pruning if volume grows.
• Manage vector retention: you probably don’t need to store every vector forever. Archive or prune old ones periodically.
• Respect rate limits: keep an eye on limits for Cohere, Supabase, Google Sheets, and Slack to avoid unexpected failures.

Troubleshooting common issues

If things don’t look quite right at first, here are some quick checks.

• RAG agent is off-topic: tighten the system prompt, give clearer instructions, or add examples of desired output.
• Embeddings seem poor: confirm you’re using the correct model, and pre-clean the text (strip HTML, normalize whitespace).
• Google Sheets append fails: verify the document ID, sheet name, and that the connected Google account has write access.
• Slack alerts are flaky: add retries or exponential backoff, and double-check Slack app permissions and channel IDs.

Scaling and operational tips

As your usage grows, you might want to harden this setup a bit.

• Dedicated Supabase project: use a separate project or database for vectors to keep query performance snappy.
• Observability: log runtimes and errors in a monitoring tool or central log sink so you can spot issues early.
• Offload heavy preprocessing: if you hit n8n execution-time limits, move heavy data prep to a background worker or separate service.
• Per-user quotas: control API and embedding costs by limiting how many reports each user can generate in a given period.

Security and privacy considerations

Fitness data is personal, so treating it carefully is non-negotiable.

• Store secrets in n8n credentials: never hardcode API keys in workflow JSON.
• Use HTTPS everywhere: for the webhook, Supabase, Cohere, Google Sheets, and Slack.
• Minimize PII: mask or omit personally identifiable information before storing vectors, especially if you need to comply with privacy regulations.
• Limit access: restrict who can view the Supabase project and the Google Sheets document.

How to get started quickly

Ready to try this out in your own n8n instance? Here’s a simple setup checklist.

1. Import the workflow JSON into your n8n instance using the built-in import feature.
2. Configure credentials for:
   • Cohere (or your chosen embeddings provider)
   • Supabase
   • OpenAI (or your preferred chat model)
   • Google Sheets
   • Slack
3. Create a Supabase table/index named fitness_api_weekly_report to store vectors and metadata.
4. Secure the webhook and point your fitness API aggregator or scheduler to the webhook URL.
5. Send a test payload and confirm:
   • A new row appears in your Google Sheet.
   • The generated summary looks reasonable.
   • Slack receives an alert if you simulate or trigger an error.

Wrapping up: why this template makes life easier

With this n8n template, your weekly fitness reporting goes from “manual, repetitive task” to “reliable background automation.” Embeddings and a vector store give the RAG agent enough context to generate meaningful summaries, not just generic text, and Google Sheets plus Slack keep everything visible and auditable.

If you’ve been wanting to add smarter reporting to your fitness product, coaching workflow, or personal tracking, this is a practical way to get there without building everything from scratch.