GoldenCheck
Auto-discover validation rules from data — scan, profile, health-score.
Community: Submitted by a user or imported; check the owner before granting accessOnlineNo sign-inGlobalFreeCan modify data
What it can do
- Scan: 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
- Validate: Validate a data file against pinned rules in goldencheck.yml. Returns validation findings (existence, required, unique, enum, range checks).
- Profile: 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.
What data it sees
Do you need an account
No: the server works without sign-in
Auto-discover validation rules from data — scan, profile, health-score. No rules to write.
Server tool list (19)
Raw names from tools/list. Only developers need these.
| scan | 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 | Validate a data file against pinned rules in goldencheck.yml. Returns validation findings (existence, required, unique, enum, range checks). |
| profile | 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. |
| health_score | Get the health score (A-F, 0-100) for a data file. Quick summary of overall data quality. |
| list_checks | List all available profiler checks and what they detect. No arguments needed. |
| get_column_detail | Get detailed profile and findings for a specific column. |
| list_domains | List all available domain packs (healthcare, finance, ecommerce, etc.). Domain packs provide specialized semantic type definitions for specific data domains. |
| get_domain_info | Get detailed info about a specific domain pack — lists all semantic types, their name hints, and suppression rules. |
| install_domain | Download a community domain pack from the goldencheck-types repository and save it for use in future scans. |
| analyze_data | 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. |
| auto_configure | 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_finding | Explain a single finding in natural language. Requires the finding as a JSON dict and the file_path to load a profile for context. |
| explain_column | Get a natural-language health narrative for a specific column. Scans the file, profiles the column, and explains all findings. |
| review_queue | List all pending review items for a given job. Returns items that need human decision (medium-confidence findings). |
| approve_reject | Approve (pin) or reject (dismiss) a review queue item. Decision must be 'pin' or 'dismiss'. |
| compare_domains | Scan a file with every available domain pack (plus base/no-domain) and compare health scores. Recommends the best-fitting domain. |
| suggest_fix | Preview fixes for a data file without applying them. Shows what would change (columns, fix types, rows affected, before/after samples). |
| pipeline_handoff | 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). |
| review_stats | Get review queue statistics for a job — counts of pending, pinned, and dismissed items. |