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Standardise inconsistent country names in a dataset (e.g. "USA", "U.S.A.", "United States of America" → single canonical form). Use when a country column contains multiple spellings/aliases for the same country and the user wants them normalised.
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Audit and standardise a dataset's header row against a naming convention (snake_case, camelCase, Title Case, kebab-case) and verify consistency with an existing or forthcoming data dictionary. Use when preparing a dataset for SQL loading, publishing, or when header inconsistency (mixed casing, spaces, punctuation, abbreviation drift) is blocking downstream work.
Produce localised versions of a dataset and/or its data dictionary with translated column headers (and optionally translated dictionary descriptions) so the same underlying data can be analysed by speakers of different languages. Use when the user wants frictionless multi-language packaging — e.g. English canonical plus Hebrew, Arabic, French, Spanish variants — without forking the underlying data.
Audit numeric columns for inconsistent decimal precision (e.g. some values at 4 dp, others at 2 dp) and round all values to a user-chosen precision. Use before SQL load or publishing when mixed precision would otherwise produce awkward `NUMERIC(x, y)` choices or misleading implied precision.
Generate a polished PDF document from a dataset using Typst, with layout chosen to match the data shape (wide tables → landscape multi-page reference, narrow tables → portrait report, per-record → one-record-per-page card/profile layout, grouped → sectioned report). Supports user-selected field subsets, custom column labels, optional filtering/sorting, cover page, summary stats, and branded templates. Use when the user wants the dataset (or a slice of it) rendered as a shareable/printable document rather than a spreadsheet export.
Reconcile a canonical upstream data file with a downstream project that has diverged — the downstream has added enrichments, renamed columns, changed types, or restructured the data, so fresh upstream rows can't be loaded incrementally without transformation. Builds a mapping between upstream and downstream representations and generates an idempotent incremental sync script that ingests only new/changed rows from upstream, applies the transformation, and appends/upserts them into the downstream dataset. Use when a project's working data has evolved beyond the source and new source data needs to flow in without clobbering the divergence.
Convert tabular geodata (CSV / Excel / Parquet) into GeoJSON (or GeoJSON Seq / newline-delimited GeoJSON) — inferring geometry from lat/lon columns, WKT/WKB columns, or address columns via geocoding. Handles CRS reprojection (default WGS84 / EPSG:4326), feature property selection, and large-file streaming. Use when the user has location data in flat form and needs it as GeoJSON for mapping, GIS, or geospatial analysis tools.
| name | standardise-country-names |
| description | Standardise inconsistent country names in a dataset (e.g. "USA", "U.S.A.", "United States of America" → single canonical form). Use when a country column contains multiple spellings/aliases for the same country and the user wants them normalised. |
Normalise a country column so each country has a single canonical spelling.
add-iso3166 — standardising first improves lookup hit rate.pycountry:
add-iso3166 skill for the baseline list).country_standardisation_status column)._standardised suffix.pip install pandas pycountry