portaljs-check-data-quality

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