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GoldenCheck is a zero-config data-validation platform that discovers quality rules automatically from your data. It combines fast statistical profilers with optional LLM enhancement to catch type mismatches, missing values, format violations, outliers, and cross-column constraints. It runs as a CLI, a Python and TypeScript library, a dbt package, and a GitHub Action.

CLI reference

Commands, flags, and domain packs.

Integrations

dbt tests and the GitHub Action.

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Install

pip install goldencheck
Extras: 
pip install goldencheck[llm]                # LLM boost (Anthropic / OpenAI)
pip install goldencheck[baseline]           # deep profiling and drift detection
pip install goldencheck[baseline,semantic]  # + semantic type inference
pip install goldencheck[db]                  # database scanning
pip install goldencheck[mcp]                 # MCP server

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Quickstart

# Scan a file: discover issues and launch the TUI
goldencheck data.csv

# CI-friendly: no TUI, JSON output, fail on errors
goldencheck data.csv --no-tui --json
goldencheck validate data.csv
In Python: 
import goldencheck

findings = goldencheck.scan_file("data.csv")
for f in findings:
    print(f"[{f.severity}] {f.column}: {f.check} — {f.message}")

score = goldencheck.health_score("data.csv")
print(score)  # e.g. "B (78/100)"
The edge-safe TypeScript core: 
import { scanData, TabularData, Severity } from "goldencheck";

const data = new TabularData([
  { id: 1, email: "alice@example.com", age: 30, status: "active" },
  { id: 2, email: "bob@test.com", age: -5, status: "inactive" },
  { id: 3, email: "not-an-email", age: 25, status: "active" },
]);

const { findings, profile } = scanData(data);
for (const f of findings) {
  console.log(`[${f.severity === Severity.ERROR ? "ERROR" : "WARNING"}] ${f.column}: ${f.message}`);
}

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Key features

  • Profiling: type inference, nullability, uniqueness, format detection, range and distribution, cardinality, and pattern consistency.
  • Cross-column checks: temporal ordering, null correlation, numeric constraints, and age-versus-DOB.
  • Baseline and drift detection: 13 check types including distribution drift, entropy drift, Benford drift, functional-dependency violation, and type drift.
  • Domain packs: healthcare, finance, ecommerce, real estate, and people/HR.
  • Auto-fix: safe repairs such as trim, normalize case, fix encoding, and coerce types.
  • LLM boost: optional semantic understanding at roughly $0.01 per scan.
  • Confidence scoring: every finding carries a 0.0–1.0 confidence.

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Baseline and drift

Create a statistical baseline once, then check new data against it cheaply: 
import goldencheck

baseline = goldencheck.create_baseline("reference.csv")
baseline.save("goldencheck_baseline.yaml")

findings, profile = goldencheck.scan_file("production.csv", baseline="goldencheck_baseline.yaml")
drift = [f for f in findings if f.source == "baseline_drift"]

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Benchmark

GoldenCheck scores 88.40 on DQBench (versus Pandera 32.51, Great Expectations 21.68, Soda 22.36), at roughly 482K rows/sec.