Detect and classify PHI columns using locale-aware patterns and column-name heuristics.
npx skills add https://github.com/Aperivue/medsci-skills --skill deidentify
Please help me install this Agent Skill. Skill: deidentify Source: https://github.com/Aperivue/medsci-skills/tree/main/skills/deidentify Command: npx skills add https://github.com/Aperivue/medsci-skills --skill deidentify
De-identify clinical research data before analysis by removing PHI using rule-based, locale-aware patterns and interactive review, without sending data to external models.
Run the deidentify tool on your dataset to produce a de-identified copy with an audit trail.
High-intent search queries and answers about installing and using this skill.
You can de-identify clinical data locally using rule-based, locale-aware patterns and interactive terminal review to mask PHI without sending data to external models. This approach generates a de-identified dataset and an audit trail for IRB compliance.
The best way to generate an audit log for HIPAA compliance is to use a guided anonymization flow that documents methodology, stores mappings separately, and produces an audit trail alongside the de-identified data file for IRB review.
Yes, you can interactively review and approve date shifting and pseudonymization through a guided terminal flow that classifies PHI columns using heuristics and allows researchers to approve suppression actions before generating the final output.
Locale-aware PHI detection works with country-specific patterns by using column-name heuristics and regional formatting rules to accurately classify sensitive fields in clinical datasets before applying masking or pseudonymization.
De-identification requires storing the mapping file separately to ensure security by isolating the pseudonym keys from the masked clinical data, preventing re-identification while maintaining an auditable link for authorized IRB compliance review.
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