GoldenCheck

Scan a data file (CSV, Parquet, Excel) for data quality issues. Returns findings with severity, confidence, affected rows, and sample values. No configuration needed — rules are discovered from the data.

Validate a data file against pinned rules in goldencheck.yml. Returns validation findings (existence, required, unique, enum, range checks).

Profile a data file and return column-level statistics: type, null%, unique%, min/max, top values, detected formats. Also returns a health score (A-F) based on finding severity.

Get the health score (A-F, 0-100) for a data file. Quick summary of overall data quality.

List all available profiler checks and what they detect. No arguments needed.

Get detailed profile and findings for a specific column.

List all available domain packs (healthcare, finance, ecommerce, etc.). Domain packs provide specialized semantic type definitions for specific data domains.

Get detailed info about a specific domain pack — lists all semantic types, their name hints, and suppression rules.

Download a community domain pack from the goldencheck-types repository and save it for use in future scans.

Analyze a data file to detect its domain, profile columns, and recommend a scanning strategy. Returns domain detection, column count, row count, strategy decisions, and alternative approaches.

Scan a data file, triage findings by confidence, and generate goldencheck.yml content from the pinned findings. Optionally accepts constraints to filter or adjust the generated config.

Explain a single finding in natural language. Requires the finding as a JSON dict and the file_path to load a profile for context.

Get a natural-language health narrative for a specific column. Scans the file, profiles the column, and explains all findings.

List all pending review items for a given job. Returns items that need human decision (medium-confidence findings).

Approve (pin) or reject (dismiss) a review queue item. Decision must be 'pin' or 'dismiss'.

Scan a file with every available domain pack (plus base/no-domain) and compare health scores. Recommends the best-fitting domain.

Preview fixes for a data file without applying them. Shows what would change (columns, fix types, rows affected, before/after samples).

Generate a structured quality attestation JSON for a data file. Includes health score, findings summary, pinned rules, and attestation status (PASS, PASS_WITH_WARNINGS, REVIEW_REQUIRED, FAIL).

Get review queue statistics for a job — counts of pending, pinned, and dismissed items.