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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.
Connect, query, profile, and monitor data sources with an AI copilot.
Validate JSON, emails, URLs, and API responses to catch quality issues fast.
Check financial statements for footing and tie-out consistency issues quickly.
Explore CSV datasets with summaries, cleaning, correlations, and statistical tests.
Use nine deterministic tools to verify LLM answers before trusting them.
Validate AI-generated code against real codebases before bugs reach runtime.