# DataCheck

> Use this tool when you need to validate and improve the quality of your large language model (LLM) training data. DataCheck solves problems related to data inconsistencies and anomalies, providing a robust interface for inputting datasets and outputting validated and corrected data. It is ideal for use in AI model development, particularly when integrating with AI IDEs for seamless data quality management and statistical analysis.

Canonical page: https://skillsregistry.net/skills/liuxiaotong-data-check  
JSON: https://api.skillsregistry.net/v1/skills/liuxiaotong-data-check

## Description

Multi-dimensional data quality validation and statistical anomaly detection for LLM training data, with auto-fix pipeline and MCP tools for AI IDE integration.

## Trust

- **Trust score (0–1):** 0.59
- **Verification tier:** scanned
- **Last scanned:** 2026-08-31

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** devops-ci
- **Updated:** 2026-08-31

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/pw8zwg6o7n)
- **Repository:** <https://github.com/liuxiaotong/data-check>

## 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": "liuxiaotong-data-check"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/liuxiaotong-data-check` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/liuxiaotong-data-check/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