identifying

Analyze source code for PII exposure and LINDDUN category I identifiability threats.

12|1|Updated Feb 9, 2026

Search Tags:#data security #privacy #PII #anonymization #identifiability #LINDDUN

Ready to run

npx skills add https://github.com/florianbuetow/claude-code --skill identifying

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: identifying
Source: https://github.com/florianbuetow/claude-code/tree/main/plugins/appsec/skills/identifying
Command: npx skills add https://github.com/florianbuetow/claude-code --skill identifying

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill safeguards user privacy by detecting and mitigating risks of identifying individuals from supposedly anonymous data, preventing PII exposure and re-identification attacks.

Core Features & Use Cases

PII Detection: Identifies direct identifiers (names, emails, SSNs) and quasi-identifiers (zip codes, birth dates, gender) in code.
Anonymization Audit: Verifies the effectiveness of anonymization logic and flags insufficient data masking.
Use Case: Ensure that user data exported for analytics or shared externally does not inadvertently reveal individual identities by checking for combined quasi-identifiers or direct PII in logs and API responses.

Quick Start

Analyze the current code changes for any identifiability issues.

Frequently Asked Questions about identifying

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect PII and re-identification risks in source code?▼

To detect PII and re-identification risks, this Skill analyzes source code to identify direct identifiers and quasi-identifier combinations, mapping threats to LINDDUN category I. It traces data flows across exports, APIs, logs, and databases to flag exposure.

What are quasi-identifiers and how do they cause re-identification?▼

Quasi-identifiers are data attributes like zip codes, birth dates, and gender that, when combined, can uniquely identify individuals. This Skill detects these combinations in your code to prevent re-identification attacks on supposedly anonymous data.

How can I audit my anonymization logic for insufficient data masking?▼

You can audit anonymization logic by analyzing data exports and API responses for insufficient data masking. This Skill verifies the effectiveness of your masking and flags code paths that inadvertently reveal individual identities.

Can I scan my codebase for LINDDUN identifiability threats?▼

Yes, you can scan your codebase for LINDDUN identifiability threats. The Skill supports various analysis depths, ranging from quick grep pattern matching to deep data flow tracing and re-identification risk modeling.

Does this tool analyze data exports and API logs for privacy compliance?▼

Yes, it analyzes data exports, APIs, and logs for privacy compliance by detecting direct PII exposure and combined quasi-identifiers. This ensures user data shared externally for analytics does not inadvertently reveal individual identities.

What is the best way to check my code changes for identifiability issues?▼

The best way to check code changes for identifiability issues is to run a targeted analysis on the current modifications. This quickly flags any newly introduced direct identifiers, quasi-identifier combinations, or insufficient anonymization logic.