claude

aidrin

Assess and improve dataset AI-readiness across six dimensions: data quality, impact of data on AI, fairness and bias, data governance, understandability and usability, and data structure.

20 on GitHubby The AIDRIN authors (IDT Lab, The Ohio State University; LBNL; ANL)v2026.10.1BSD-3-Clauseupdated yesterday
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Plugin aidrin from marketplace idtlab in idtlab/AIDRIN. Commands read from the repo's manifest — yesterday.

This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.

What it installs

Skills (1)

  • aidrin

    Use when the user asks "is my data AI ready", "is my dataset ready", "what is the quality of my data", whether data is good enough to train or publish, to validate file paths stored in a dataset, to check a dataset for bias, fairness, privacy, PII risk, HIPAA compliance, protected health information

MCP servers (1)

  • aidrin

    runs: aidrin-mcp

View source on GitHub aidrin.readthedocs.io

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