Profile clinical CSV/Excel datasets and generate reproducible cleaning scripts.
npx skills add https://github.com/Aperivue/medsci-skills --skill clean-data
Please help me install this Agent Skill. Skill: clean-data Source: https://github.com/Aperivue/medsci-skills/tree/main/skills/clean-data Command: npx skills add https://github.com/Aperivue/medsci-skills --skill clean-data
Profile clinical datasets to identify quality gaps and generate clean-data workflows.
Upload your dataset and run the profiling tool to produce a data profile, review flagged issues, and generate cleaning scripts for approved actions.
High-intent search queries and answers about installing and using this skill.
You can profile clinical datasets by uploading CSV or Excel research files to generate a detailed data profile. The automated workflow identifies missing values, outliers, duplicates, and type mismatches, outputting a profiling report and flag table for your review.
To clean clinical research data while maintaining reproducibility, use an automated code-generation workflow that produces cleaning scripts instead of applying changes directly. This approach ensures a complete audit log and reproducible code through researcher approval gates at each stage.
Yes, this data cleaning workflow includes de-identification guidance and templates for clinical registries. It supports privacy-preserving data processing during the profiling and cleaning stages of your research datasets.
Yes, you can review and approve flagged data quality issues before generating scripts. The workflow uses a three-stage process—profile, flag, and code-generation—with explicit researcher approval gates at each step to ensure full control over all cleaning actions.
When profiling clinical registries, the workflow handles missing values, outliers, duplicates, and type mismatches. It identifies these specific data quality gaps and compiles them into a flag table for your review before generating any reproducible cleaning scripts.
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