defense-in-depth

Enforce multi-layer data validation across API entry points, business logic, environment guards, and debug instrumentation.

3|Updated Oct 18, 2025
One-click install
npx skills add https://github.com/DYAI2025/Stoppclock-page --skill defense-in-depth-dyai2025
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: defense-in-depth
Source: https://github.com/DYAI2025/Stoppclock-page/tree/main/stoppclock_speckit/.claude/commands/SKILL (30).md
Command: npx skills add https://github.com/DYAI2025/Stoppclock-page --skill defense-in-depth-dyai2025

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents bugs caused by invalid data by enforcing validation at every layer data passes through. Instead of relying on a single check that can be bypassed, it makes bugs structurally impossible, significantly improving system robustness and security.

Core Features & Use Cases

  • Four Layers of Validation: Implement checks at entry point, business logic, environment guards, and debug instrumentation.
  • Systematic Application: Trace data flow, map checkpoints, and add validation at each layer.
  • Use Case: After fixing a critical bug caused by unexpected input, use this skill to add comprehensive validation across all relevant system layers, ensuring the bug can never recur.

Quick Start

When you find a bug:

1. Trace the data flow: Where does bad value originate? Where used?

2. Map all checkpoints: List every point data passes through.

3. Add validation at each layer: Entry, business, environment, debug.

4. Test each layer: Try to bypass layer 1, verify layer 2 catches it.

Frequently Asked Questions about defense-in-depth

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

FAQPage Schema
How do I prevent bugs caused by invalid data entering my system?

Multi-layer validation catches invalid data at every boundary—entry points, business logic, environment guards, and debug instrumentation—making bugs structurally impossible rather than relying on a single checkpoint that can be bypassed.

What are the four layers of data validation I should implement?

Entry point validation checks incoming data; business logic validation enforces rules during processing; environment guards verify system state; debug instrumentation traces data with context. Applying checks at all four layers prevents invalid data from progressing through the system.

How do I trace data flow to find validation checkpoints?

Map where bad data originates, trace its path through entry points and business logic, identify environment assumptions, and note debug touchpoints. Add explicit validation and error handling at each checkpoint to catch violations early and produce traceable context.

Can I use this approach after finding a critical bug?

Yes. Once you identify a bug caused by unexpected input, use this method to add comprehensive validation across all layers—entry, business, environment, and debug—ensuring that specific bug class cannot recur through any data path.

How do I test that validation works at each layer?

Test bypass scenarios: attempt to inject invalid data past layer 1 and verify layer 2 catches it. Repeat for each boundary. This confirms no single validation point is the only defense and that failures produce explicit errors with context.

What's the difference between single-point validation and multi-layer validation?

Single-point validation relies on one check that can fail silently or be overlooked. Multi-layer validation creates structural redundancy—if one layer misses invalid data, the next catches it—turning data quality from a best-effort practice into a system guarantee.

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