Use this skill to harmonize small metadata tables with inconsistent column names and categorical labels into one canonical TSV plus a compact JSON summary.
Installs into .claude/skills of the current project.
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# Metadata Harmonization Starter
Use this skill to harmonize small metadata tables with inconsistent column names and categorical labels into one canonical TSV plus a compact JSON summary.
## What This Skill Does
- reads one or more tabular metadata files
- applies a JSON mapping from source columns to canonical fields
- normalizes selected categorical values such as `sex` and `condition`
- writes a harmonized TSV and a machine-readable summary
## When To Use It
- when you need a starter for `metadata-harmonization`
- when multiple small test fixtures use different metadata headers
- when you want deterministic harmonized outputs before validation or format conversion
## Run
```bash
python3 skills/data-acquisition-and-dataset-handling/metadata-harmonization-starter/scripts/run_metadata_harmonization.py \
--input skills/data-acquisition-and-dataset-handling/metadata-harmonization-starter/examples/cohort_a.tsv \
--input skills/data-acquisition-and-dataset-handling/metadata-harmonization-starter/examples/cohort_b.tsv \
--mapping skills/data-acquisition-and-dataset-handling/metadata-harmonization-starter/examples/column_mapping.json \
--out-tsv scratch/metadata-harmonization/harmonized_metadata.tsv \
--summary-out scratch/metadata-harmonization/harmonized_metadata_summary.json
```
## Notes
- The starter keeps the mapping external so the same script can be reused for other tiny fixtures.
- Canonical rows are sorted by `sample_id` to keep committed outputs deterministic.
