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name desensitizer
description Desensitize, anonymize, and mask sensitive data in documents, code, configs, and datasets. Use when someone asks to "remove PII", "anonymize data", "mask sensitive info", "desensitize a document", or needs to prepare data for sharing, demos, or testing without exposing real information.
tags
privacy
anonymization
pii
masking
data-protection
version 1.0.0
common-skills-used
quality-checklist
agents-tested
claude-code
kiro

Desensitizer

Purpose

Remove or mask sensitive information from any content while preserving its structure and usefulness for the intended purpose (sharing, demos, testing, compliance).

When to Use

  • User needs to remove PII from documents or data
  • User wants to create anonymized versions of real data
  • User needs to prepare content for external sharing
  • User asks to mask credentials, API keys, or secrets in configs/code

When NOT to Use

  • User asks for legal/privacy policy interpretation without providing masking work
  • User requests irreversible deletion from live systems (this skill handles content transformation)

Inputs

Required:

  • Content to desensitize

Optional:

  • What to mask (specific fields, all PII, secrets only)
  • Replacement strategy (fake data, redaction, hashing, placeholders)
  • Compliance context (GDPR, HIPAA, etc.)

Process

Step 1: Identify Sensitive Data

Scan for: names, emails, phone numbers, addresses, SSNs, credit cards, API keys, passwords, IP addresses, and domain-specific sensitive fields.

Step 2: Choose Masking Strategy

Based on context: replace with realistic fake data (for demos), redact with [REDACTED] (for compliance), or hash (for data analysis).

Step 3: Apply Masking

Replace all identified sensitive data consistently — same real name maps to same fake name throughout the document.

Step 4: Verify

Review the output to ensure no sensitive data leaked through and the document remains usable.

Output Format

Desensitized version of the input in the same format.

Quality Checks

  • All obvious direct identifiers (names, emails, phone numbers, IDs) are handled
  • Secret patterns (keys/tokens/passwords) are masked consistently
  • Replacement strategy preserves structure needed for downstream use
  • Same source entity maps to the same replacement value throughout

Common Skills Used

  • common-skills/quality-checklist.md — Verify completeness of desensitization

Edge Cases

  • Partially structured data: Preserve delimiters/schema while masking cell values
  • High re-identification risk context: Prefer [REDACTED] over realistic fake replacements
  • Binary or encoded payloads: Flag unsupported formats and request decoded text form first