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Validate LLM training data, detect anomalies, and auto-fix quality issues.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "DataCheck" yet — see the docs or source repo.
Run multidimensional quality validation on this LLM training dataset. Check duplicates, missing values, formatting errors, label anomalies, and distribution imbalance, then return a summary of issues and fix recommendations.
A data quality report listing major anomaly types, affected scope, statistics, and recommended fixes.
Execute the auto-fix pipeline for detected training data issues: deduplicate records, normalize fields, correct obvious formatting errors, and generate a before-and-after change summary.
The cleaned dataset, a fix log, and a summary of how each issue type was handled.
Analyze this training corpus for statistical anomalies, including abnormal length, score deviation, and unusual class concentration, then rank findings by risk level.
A ranked list of anomalous samples with risk levels for prioritized review.
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Validate shell commands before execution to avoid invalid or failing runs.