Agent skill · Data & Analytics

configuring-privacy-policies

Select and customize OpenMed's seven bundled privacy policy profiles for de-identification, and build custom surrogate generators. Use when the user asks which policy fits HIPAA Safe Harbor vs Expert Determination vs GDPR vs PIPEDA vs a research limited dataset vs strict no-leak, wants to pass policy= to deidentify(), needs to keep quasi-identifiers for research, or must register a custom MRN/name/address surrogate provider. Covers the profile-to-use-case map, AnonymizerConfig/Anonymizer for fine control, and register_clinical_provider / register_label_generator. Pairs with OpenMed deidentifyi

maziyarpanahigithub.com/maziyarpanahiGitHub ↗
claude-codeApache-2.0
Install
npx skills add maziyarpanahi/openmed --skill configuring-privacy-policies --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 8 KB
Bundled scripts: none
Version: 1.0
Path: skills/configuring-privacy-policies/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 4,851
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Configuring privacy policies A **policy profile** is a named bundle of de-identification decisions: which action (mask/redact/replace/keep) applies to each label, how aggressively detectors arbitrate, whether the mandatory safety sweep runs, and whether a reversible mapping is produced. OpenMed ships seven profiles. Pass one by name to `deidentify(policy=...)` and you get a compliance-aligned default without hand-wiring 50+ per-label actions. Everything runs **on-device**. ## When to use this skill Use it to pick the right `policy=` for a regulatory context, to understand what a profile actually changes, or to go beyond the bundle — keeping quasi- identifiers for research, or registering a custom surrogate generator (e.g. your own MRN format). ## Quick start ```python import openmed note = "Jane Roe, DOB 1979-04-11, lives in Cambridge MA 02139. SSN 123-45-6789." # HIPAA Safe Harbor: mask every identifier class. safe = openmed.deidentify(note, policy="hipaa_safe_harbor") # GDPR pseudonymization: replace with fakes AND keep a reversible mapping. gdpr = openmed.deidentify(note, policy="gdpr_pseudonymization") mapping = gdpr.mapping # present because the profile sets keep_mapping=Tru

What's inside
Steps it walks through
  1. When to use this skill
  2. Quick start
  3. The seven bundled profiles
  4. Choosing: map regulation → profile
  5. Customizing beyond the bundle
  6. Hand-off to / from OpenMed
  7. Edge cases & gotchas
  8. Standards & references
More from openmed
All skills →
About this skill
What does the configuring-privacy-policies skill do?

Select and customize OpenMed's seven bundled privacy policy profiles for de-identification, and build custom surrogate generators. Use when the user asks which policy fits HIPAA Safe Harbor vs Expert Determination vs GDPR vs PIPEDA vs a research limited dataset vs strict no-leak, wants to pass policy= to deidentify(), needs to keep quasi-identifiers for research, or must register a custom MRN/name/address surrogate provider. Covers the profile-to-use-case map, AnonymizerConfig/Anonymizer for fine control, and register_clinical_provider / register_label_generator. Pairs with OpenMed deidentifyi

How do I install it?

Run `npx skills add maziyarpanahi/openmed --skill configuring-privacy-policies --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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