84 lines
2.1 KiB
Markdown
84 lines
2.1 KiB
Markdown
# Vehicle data project
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## What this project is
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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.
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Project location:
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- `projects/vehicle-data`
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## Seed data imported
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Source export:
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- `vehicle-3-sync.csv`
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Imported into:
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- `vehicle_data.db`
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First-pass imported counts:
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- 1 vehicle
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- 196 fuel log entries
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- 9 favourite stations
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- 2 categories
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- 1 trip log
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Vehicle imported:
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- Toyota LandCruiser (`LandCruiser`, description `Nana's car`)
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## Why this exists
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The CSV export is fine as a backup/import format but not ideal as a living datastore for:
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- multiple vehicles
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- assistant-added entries
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- efficient querying
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- graphs and summaries
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- station/location enrichment
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SQLite is the intended working source of truth going forward.
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## Agreed future mobile entry shape
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Keep phone capture dead simple.
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Required basics:
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- odo
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- fuel
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- cost
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- full / not full
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- location
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This is the intended assistant-assisted workflow for logging future fills from Telegram.
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## Location handling decision
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Telegram location shares are usable.
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They can provide:
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- raw latitude / longitude
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- reverse lookup context
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- nearby station candidates
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- human-friendly place labels
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Best future entry quality will come from:
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- fuel details + station name + Telegram location
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But location share alone is still useful enough to support the workflow.
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## Current project status
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Created:
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- `projects/vehicle-data/vehicle_data.db`
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- `projects/vehicle-data/import_vehicle_csv.py`
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- `projects/vehicle-data/PROJECT-STATUS.md`
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The project is still in pre-skill/prototype mode.
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No dedicated skill has been created yet.
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## Current aim
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Short term:
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- add assistant/manual append capability for new fuel entries
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- prove the Telegram-driven entry workflow
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- refine provenance/source fields and place naming
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Medium term:
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- formalise into a reusable workspace skill, likely `vehicle-data`
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## Notes to remember
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- Treat CSV as import/export only.
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- Treat SQLite as the living datastore.
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- Keep manual entry format minimal and phone-friendly.
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- Add skill packaging only after the next few workflow tweaks are proven.
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