convert-workout-journal

To convert raw gym notes into structured workout logs in Obsidian, this Skill uses Python scripts to parse journal entries and generate Dataview-compatible fitness files. It extracts exercises, sets, reps, and weights into a deterministic JSON structure to populate your vault automatically.

Yes, you can automate importing fitness data into an Obsidian Dataview dashboard by running the parse and generate Python scripts. This creates an aggregated dashboard alongside individual workout and exercise notes rooted under the Area/Fitness directory.

The best way to structure exercise notes for Dataview queries is using a canonical exercises.json registry. This ensures repeatable and idempotent imports by standardizing extracted exercise data into a deterministic JSON format before generating Obsidian-ready files.

Yes, you need Python to parse workout journals and generate Obsidian files because the workflow relies on two specific scripts, parse_journal.py and generate_workout.py. These scripts process raw journal text and output the structured markdown files for your vault.

Parsing raw journal notes for fitness tracking works by extracting sets, reps, weights, durations, and notes from unstructured text. The parsed data is fed into a generation script that creates per-exercise notes and an aggregated dashboard under the Area/Fitness folder.

A limitation when converting unstructured gym notes into a Dataview-compatible format is the dependency on a canonical exercises.json registry for accurate matching. If raw journal entries deviate significantly from expected naming conventions, parsing accuracy may decrease.