GoldenCheck overview

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Install

pip install goldencheck

npm install goldencheck

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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Polars-free by default (3.0.0)

• goldencheck[baseline] — pulls Polars (plus scipy/numpy) for the statistical, drift, and correlation subsystems, which still run on Polars.
• goldencheck[polars] — only for the scan_dataframe(pl.DataFrame) convenience overload. scan_dataframe accepts a pyarrow.Table natively; a polars.DataFrame is converted via .to_arrow() when Polars is present.

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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

import goldencheck

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

grade, score = profile.health_score()
print(f"{grade} ({score}/100)")  # e.g. "B (78/100)"

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, pattern consistency, encoding and sequence detection, near-duplicate value detection (inconsistent categorical encodings such as California/Californa/CALIFORNIA), and freshness (future-dated values and name-gated staleness).
• Cross-column checks: temporal ordering, null correlation, numeric constraints, age-versus-DOB, composite-key discovery, exact and near-duplicate rows, strict functional dependencies, and approximate-FD violations (rows that break a near-strict zip → city).
• Denial-constraint discovery (opt-in): mines if-then / cross-tuple invariants of the form ¬(p1 ∧ … ∧ pm) — e.g. ¬(status=shipped ∧ ship_date<order_date) (“if shipped, ship_date must be ≥ order_date”) — from a single table and surfaces the violating rows. Off by default; enable with goldencheck denial-constraints data.csv or --denial on a scan.
• Baseline and drift detection: 12 check types including distribution drift, entropy drift, Benford drift, functional-dependency violation, and type drift.
• Domain packs: healthcare, finance, and ecommerce.
• 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

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