Portaljs Check Data Quality
Audit a local or remote tabular file (CSV/TSV) for common data quality issues, schema...
Install
npx skills add datopian/portaljs --skill portaljs-check-data-quality -g -a claude-code -y- Repository
- datopian/portaljs2.4k
- Category
- Data and ML
- Updated
- 7d ago
- License
- MIT
- Context cost
- Lightabout 1,290 tokens
- Runs scripts
- No, instructions only
What it does
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.
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
python3onPATH, 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/httpsURL. Only one file per run.
Instructions
The canonical, full step-by-step workflow is
.claude/commands/portaljs-check-data-quality.md, the single source of truth. Read and follow it when executing. Summary:
