portaljs-check-data-quality
Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates. Read-only. Use when a dataset needs a quality check before publishing, or a showcase renders wrong (blank cells, garbled numbers, an unsortable date column) and the cause needs isolating.
npx skills add datopian/portaljs --skill portaljs-check-data-quality --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 trut
- Overview
- Prerequisites
- Instructions
- Output
- Error Handling
- Examples
- Example 1 — Audit a local CSV before publishing
- Example 2 — Audit a remote CSV over HTTPS
- Example 3 — Audit a TSV and save the report for review
- Example 4 — Read a critical status report
- Resources
bash scripts/check-data-quality.sh ./data/emissions.tsv > /tmp/emissions-quality.json
What does the portaljs-check-data-quality skill do?
Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates. Read-only. Use when a dataset needs a quality check before publishing, or a showcase renders wrong (blank cells, garbled numbers, an unsortable date column) and the cause needs isolating.
How do I install it?
Run `npx skills add datopian/portaljs --skill portaljs-check-data-quality --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From datopian/portaljs, a repository with 2,337 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.