lgpd-anonymization

lgpd-anonymization is a Privacy & regulatory compliance skill for AI agents, published by goul4rt in the lgpd-skills repository on GitHub. Skillspace rates lgpd-anonymization C (70/100) and it passed the safety scan; the rating is based on static analysis only.

Apply anonymization (Art. 5, XI) and pseudonymization (Art. 13, §4) techniques to take data out of LGPD scope or reduce risk in analytics pipelines. Use when user asks 'anonimizar', 'pseudonimizar', 'k-anonymity', 'differential privacy', 'tokenização', 'analytics LGPD', or designing analytics/ML pipelines.

Grade
C
Quality
70 / 100
Safety
Passed
GitHub stars
36

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubgoul4rt/lgpd-skills

Our review

Architecture
100 / 100
Maturity
Structured: ships references or scripts
Security scan
Low-risk patterns
License
MIT
Adoption
Adopted by 0 other repositories, in 0 collections

What the scanner flagged

  • mentions secrets in text

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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