portaljs-check-data-quality

1# PortalJS — Check Data Quality
2 
3## Overview
4 
5Run a read-only quality audit of one CSV or TSV file, local or remote, and return a
6structured JSON report. The audit profiles every column — null/blank counts, inferred
7value types, numeric ranges, likely year/date fields — and flags duplicate rows,
8duplicate values in identifier-like columns, ambiguous overlapping year columns (e.g.
9`calendar year` vs `fiscal year`), and mixed-type columns. It never edits the source
10file, `datasets.json`, or any other project file; it only reads the target file (a
11remote URL is downloaded to a temp file that is deleted before the run ends) and
12prints a report. Use it before publishing a dataset with `portaljs-add-dataset`, or to
13diagnose why a showcase renders wrong.
14 
15## Prerequisites
16 
17- `python3` on `PATH` — the audit logic runs as an embedded Python script; nothing is
18  installed.
19- One CSV or TSV file, given as a local path or an `http`/`https` URL. Only one file
20  per run.
21 
22## Instructions
23 
24The canonical, full step-by-step workflow is
25[`.claude/commands/portaljs-check-data-quality.md`](https://github.com/datopian/portaljs/blob/main/.claude/commands/portaljs-check-data-quality.md) —
26the single source of truth. Read and follow it when executing. Summary:
27 
281. Gather input — the file path or URL to audit. If missing, ask for it; never dead-end.
292. Resolve the source: if it's an `http`/`https` URL, download it to a temp file first;
30   otherwise use the local path as given.
31…