Get started with GoldenCheck in 60 seconds.

Install 

pip install goldencheck

Scan a File 

goldencheck data.csv

This launches the interactive TUI with all discovered issues.

For CLI-only output:

goldencheck data.csv --no-tui

What You’ll See

GoldenCheck runs 10 column profilers and 2 cross-column profilers, then reports findings by severity:

─── GoldenCheck Results ───
File: data.csv (10,000 rows × 12 columns)
Health: B (82)

ERROR   email        format_detection   6% malformed emails (600 rows)
ERROR   age          range_distribution  Values outside [0, 120]: -5, 999
WARN    status       pattern_consistency 3 case variants: active, Active, ACTIVE
WARN    signup_date  temporal_order      12 rows where signup_date > last_login
INFO    id           uniqueness          100% unique — likely primary key

Pin Rules → Export → Validate

In the TUI:

  1. Press Space to pin findings you want to enforce
  2. Press F2 to save to goldencheck.yml
  3. Validate in CI:
goldencheck validate data.csv
# Exit code 0 = pass, 1 = fail

With LLM Boost

Add LLM intelligence for ~$0.01 per scan:

export OPENAI_API_KEY=sk-...
goldencheck data.csv --llm-boost --llm-provider openai --no-tui

The LLM catches semantic issues profilers miss and reduces false positives.

JSON Output (CI) 

goldencheck data.csv --no-tui --json

Python API 

from goldencheck.engine.scanner import scan_file
from goldencheck.engine.confidence import apply_confidence_downgrade

findings, profile = scan_file("data.csv")
findings = apply_confidence_downgrade(findings, llm_boost=False)

for f in findings:
    print(f"{f.severity.name}: [{f.column}] {f.message}")

Jupyter / Colab 

from goldencheck.notebook import ScanResult

result = ScanResult(findings=findings, profile=profile)
result  # Rich HTML table in notebooks

Open In Colab

Next Steps