deidentify

Detect and classify PHI columns using locale-aware patterns and column-name heuristics.

243|60|Updated Apr 6, 2026
One-click install
npx skills add https://github.com/Aperivue/medsci-skills --skill deidentify
Or copy as Structured Prompt for Agent▼
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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openpyxl, and includes references (resource) components.

What problem does it solve?

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.

Core Features & Use Cases

  • Locale-aware PHI detection: uses country-specific patterns and column-name heuristics to classify fields as PHI.
  • Interactive review and anonymization: researchers approve pseudonymization, date shifting, and suppression in a guided terminal flow.
  • Audit trail and mapping: produces a de-identified dataset plus a secure mapping file and an audit log for IRB compliance.

Quick Start

Run the deidentify tool on your dataset to produce a de-identified copy with an audit trail.

Frequently Asked Questions about deidentify

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I de-identify clinical data without sending PHI to external models?▼

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.

What is the best way to generate an audit log for HIPAA compliance when anonymizing research data?▼

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.

Can I interactively review and approve date shifting and pseudonymization for clinical data?▼

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.

Does locale-aware PHI detection work with country-specific patterns in clinical datasets?▼

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.

Why does de-identification require storing the mapping file separately from the dataset?▼

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.