# goldencheck

> Use this tool when you need to automatically discover data validation rules from your existing data, solving problems of data inconsistency and quality issues. It takes in datasets as input and outputs validated data with discovered rules, providing a robust interface for data quality control. Ideal for use cases where data accuracy and reliability are crucial, such as data integration, migration, or analytics projects.

Canonical page: https://skillsregistry.net/skills/benzsevern-goldencheck  
JSON: https://api.skillsregistry.net/v1/skills/benzsevern-goldencheck

## Description

Data validation that discovers rules from your data. Python + TypeScript. DQBench Score: 88.40.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-02

## Source

- **Source listing:** [GitHub](https://github.com/benzsevern/goldencheck)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "benzsevern-goldencheck"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/benzsevern-goldencheck` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/benzsevern-goldencheck/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt