Knowledge/chat-topics/vehicle-data-project.md

Vehicle data project

What this project is

A new local-first project to move Michael's vehicle / fuel tracking data away from a flaky sectioned CSV export and into a queryable SQLite database.

Project location:

• projects/vehicle-data

Seed data imported

Source export:

• vehicle-3-sync.csv

Imported into:

• vehicle_data.db

First-pass imported counts:

• 1 vehicle
• 196 fuel log entries
• 9 favourite stations
• 2 categories
• 1 trip log

Vehicle imported:

• Toyota LandCruiser (LandCruiser, description Nana's car)

Why this exists

The CSV export is fine as a backup/import format but not ideal as a living datastore for:

• multiple vehicles
• assistant-added entries
• efficient querying
• graphs and summaries
• station/location enrichment

SQLite is the intended working source of truth going forward.

Agreed future mobile entry shape

Keep phone capture dead simple.

Required basics:

• odo
• fuel
• cost
• full / not full
• location

This is the intended assistant-assisted workflow for logging future fills from Telegram.

Location handling decision

Telegram location shares are usable. They can provide:

• raw latitude / longitude
• reverse lookup context
• nearby station candidates
• human-friendly place labels

Best future entry quality will come from:

• fuel details + station name + Telegram location

But location share alone is still useful enough to support the workflow.

Current project status

Created:

• projects/vehicle-data/vehicle_data.db
• projects/vehicle-data/import_vehicle_csv.py
• projects/vehicle-data/PROJECT-STATUS.md

The project is still in pre-skill/prototype mode. No dedicated skill has been created yet.

Current aim

Short term:

• add assistant/manual append capability for new fuel entries
• prove the Telegram-driven entry workflow
• refine provenance/source fields and place naming

Medium term:

• formalise into a reusable workspace skill, likely vehicle-data

Notes to remember

• Treat CSV as import/export only.
• Treat SQLite as the living datastore.
• Keep manual entry format minimal and phone-friendly.
• Add skill packaging only after the next few workflow tweaks are proven.