Summary
This handles the unglamorous but critical job of stripping PII from text before you ship logs to Datadog or hand production data to your staging environment. It catches emails, phone numbers, credit cards, and the usual suspects. Worth noting it redacts rather than encrypts, so anything it masks is gone for good. You'll want to watch for false positives on things like internal IDs that look like phone numbers, and it might be overly cautious with ambiguous cases. Best practice is running this locally so raw sensitive data never leaves your perimeter. Solid choice if you need quick GDPR compliance without building regex patterns yourself.
Install to Claude Code
npx -y skills add jorgealves/agent_skills --skill pii-sanitizer --agent claude-codeInstalls into .claude/skills of the current project.
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Files
SKILL.mdView on GitHub
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.
Featured
Plug Mailtrap into your AI workflow and let it handle the email.
Connect Mailtrap MCP →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
Try For Free →Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
