I can't write Python. Built a local health data pipeline anyway.
I'm a mechanical engineer, my job is to design automated warehouse systems. I have no Python background. After reading an article about a Garmin connector that pipes your health data into Claude or ChatGPT via a third-party service, my reaction was: cool idea, not my data.
So I started building a local alternative. And while doing it I noticed something: file sizes for older days were off. 450 KB for recent data, under 1 KB for anything older than 2.5 years. That's not a bug — that's Garmin quietly downsampling your history. Once it's gone, it's gone.
My approach was to design the data flow like material flow. Define what goes in, what comes out, what happens where. Claude writes the code. I verify the flow.
I started with a couple of scripts and then somehow it escalated.
- three build targets (dev scripts / standard EXE / standalone EXE)
- a full dashboard pipeline with Plotly,
- automated tests
What actually made it work wasn't the AI. It was the process:
- 'Assess first' — a short command that hard-stops the LLM before it builds something you didn't ask for. More than half of all session interactions were scope checks, not code.
- `Don't write anything yet. Analyze how feature X would affect the data flow in module Y. List what breaks, what depends on it, what needs to change first. Wait for my decision.`
- One change per step, always verify
- Multi-LLM review for architecture decisions (Claude builds, Gemini/ChatGPT/LeChat/Copilot reviews, I decide)
- Roughly half the total project time went into planning and architecture — not implementation. The ratio of human design input to generated code is approximately 1:1.
The architecture held because it was built like a machine — every module owns exactly one thing, no crossover. Claude recognized the patterns had names — sole write authority, single source of truth, separation of concerns. I just knew from experience what happens when two modules own the same resource.
github.com/Wewoc/Garmin_Local_Archive