Implemented as Anthropic Agent Skills — versioned instruction+tool bundles; the 19 expert harmonizations act as retrievable in-context exemplars. [Agent Skills 2025]
+ +The intermediate artifact
+Between "decide" and "executed table," the harmonizer emits a + machine-readable change-mapping: one entry per dataset, with a rule for every target + variable. It is the auditable record of what maps to what, and how — decoupled from the + Python that implements it.
+Top level (7 fields):
+index, dataset_identifier, doi, archive_repositorydata_payload_files, location_metadata_files, sensor_metadata_filesharmonization_mappings — the per-variable rulesEach rule (one per target variable):
+Excluded datasets collapse to "harmonization_mappings": "EXCLUDED: <reason>".
Real rules leave-one-out fold · dataset 7 · "Bradley Meadow"
+Verbatim from an agent run that never saw dataset 7's gold. Source: open evaluation PRs (evaluations/fold-05-holdout-7/).
Scoring the mapping same fold · machine-readable metrics
+The change-mapping is compared to the expert's, field by field:
+| Field | Exact match vs gold |
|---|---|
destination_variable | 8 / 8 |
unit_conversion | 7 / 8 |
source_files | 7 / 8 |
source_pattern | 3 / 8 |
transformation (free text) | 0 / 8 |
| mapping categories covered | 8 / 8 |
The prose never matches — yet the data does.
+ The agent and expert describe transformations in different words (transformation 0/8),
+ but executing both programs on the same raw files gives:
rows recovered (P=R=1.0)
VWC & potential cells match
interval_min cells differ (ordering)
This is the whole argument for an output-equivalence endpoint: stylistic divergence in the + mapping/code is irrelevant when the harmonized table is identical. The mapping JSON gives auditable + semantics; the executed output gives the verdict.
+Figure 1