Why isn't a single validation check enough?

Single checks can often be bypassed by alternative code paths, unit test mocks, or future refactoring. Multi-layer validation provides redundant safeguards.

When should I use the Defense-in-Depth skill?

Use this skill when you are fixing bugs caused by invalid data that caused failures deep in execution, or when building high-stakes features where data integrity is critical.

What is Defense-in-Depth validation?

It is a strategy where data is validated at every system layer—entry, business logic, environment, and debug—to ensure bugs are structurally impossible to reproduce.

What are the four layers of validation used in this skill?

The layers include Entry Point Validation (API boundary), Business Logic Validation (operation logic), Environment Guards (context-specific safety), and Debug Instrumentation (forensic context).

Defense-in-Depth Validation

Name: Defense-in-Depth Validation
Author: franroa

byfranroa

Seguridad y Pruebas

Implements multi-layered data validation strategies to prevent deep-system failures and make software bugs structurally impossible.

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This skill equips Claude with architectural patterns for implementing robust 'Defense-in-Depth' validation across four critical system layers: entry points, business logic, environment guards, and debug instrumentation. Rather than relying on a single point of failure, this approach ensures that invalid data is caught regardless of the execution path, refactoring, or mocking. It is particularly useful for hardening complex systems where data corruption or invalid states could cause destructive actions or difficult-to-trace failures deep within the execution stack.

Características Principales

Forensic debug instrumentation
Multi-layer validation architecture (4-layer model)
0 GitHub stars
Structural bug prevention patterns
Data flow checkpoint mapping
Environment-specific safety guards

Casos de Uso

Hardening APIs against edge-case data corruption
Securing complex data pipelines against bypassed validation logic
Preventing dangerous operations (like git init) in sensitive environments

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Características Principales

Forensic debug instrumentation
Multi-layer validation architecture (4-layer model)
0 GitHub stars
Structural bug prevention patterns
Data flow checkpoint mapping
Environment-specific safety guards

Casos de Uso

Hardening APIs against edge-case data corruption
Securing complex data pipelines against bypassed validation logic
Preventing dangerous operations (like git init) in sensitive environments