jorgealves/agent_skills

pii-sanitizer

Detects and redacts Personally Identifiable Information (PII) like emails, phone numbers, and credit cards. Use when cleaning logs, datasets, or communications to comply with GDPR/CCPA privacy standards.

First seen Jan 30, 2026

Installation

$ npx skills add jorgealves/agent_skills --skill pii-sanitizer

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 2
License license-compliance-auditor
Default branch main
Open issues 1
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,729 B
  • docs SUMMARY.md 224 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 251 installs

SKILL.md

PII Sanitizer

Purpose and Intent

The pii-sanitizer is a data protection tool designed to identify and mask Personally Identifiable Information (PII) from datasets, logs, or communications to comply with privacy regulations like GDPR and CCPA.

When to Use

  • Log Scrubbing: Clean application logs before sending them to centralized logging platforms (e.g., ELK, Datadog).
  • Dataset Preparation: Sanitize production data before using it in staging or training environments.
  • Customer Support: Mask sensitive info in support tickets before sharing them with engineering teams.

When NOT to Use

  • Encryption: This is a redaction tool, not an encryption tool. It is for removing data, not securing it for later retrieval.
  • Structured Database Migration: While it handles some structure, specialized ETL tools are better for massive DB sanitization.

Error Conditions and Edge Cases

  • False Positives: Strings that resemble PII (like internal serial numbers) might be accidentally redacted.
  • Ambiguous Context: "Rose" could be a name (PII) or a flower; the tool may err on the side of caution.
  • Encoding Issues: Ensure input text is UTF-8 to avoid detection failures on special characters.

Security and Data-Handling Considerations

  • Zero Retention: Input data must never be saved to disk.
  • Local Processing: Highly recommended to run this within a secure perimeter so sensitive raw data never leaves the local environment.