Highlights
- Model breakdown is especially useful here
- Good fit for long research cycles
- Local-first default reduces friction around sensitive work
Use case
vibestats for researchers
Use vibestats to compare model usage, review activity patterns, and keep a local record of AI-assisted research coding.
Intent
Use case
Focus
vibestats for researchers focuses on a repeatable reporting workflow instead of raw token dumps.
Next step
Relevant commands
npx vibestats --modelnpx vibestats --monthlynpx vibestats --wrappedTypical challenge
Research workflows are often exploratory and model-heavy, which makes model breakdown and long-window trend views more useful than one raw total.
Useful vibestats workflow
Use model views for tool choice, daily or monthly reporting for cadence, and wrapped pages when you want a compact retrospective over a long research cycle.
Outcome
You get cleaner evidence for how AI coding tools were used during experiments, prototyping, and implementation work.
FAQ
Why does vibestats for researchers matter?
vibestats for researchers matters when people need a stable way to explain AI coding usage, patterns, or cost without reverse-engineering local files every time.
Is this only for one kind of developer?
No. The same reporting surface can support solo developers, client work, internal retrospectives, and manager reporting. The difference is usually which report you prioritize.
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