tech-debt-detector

Technical Debt Detector (AI-Generated Code)

Research shows a 2500% increase in code defects linked to AI-generated code (BCG 2026). This skill identifies debt patterns specific to LLM outputs.

When to Use

• After generating code with AI (review before merging)
• During code review of AI-assisted PRs
• When refactoring AI-generated modules
• When investigating production issues in AI-written code
• Periodic tech debt audits

AI-Specific Debt Patterns

Pattern 1: Shallow Implementation

LLMs produce code that works for the happy path but fails on edges.

Signals:

• No error handling beyond generic try/catch
• No input validation at system boundaries
• No null/undefined checks on external data
• No timeout on network calls
• Functions that work for sample data but fail at scale

Check: For each function, ask: "What happens with empty input? Null? Very large input? Concurrent access? Network failure?"

Pattern 2: Over-Abstraction

LLMs love creating abstractions even when unnecessary.

Signals:

• Wrapper classes with no added logic
• Factory patterns for single implementations
• Interface with exactly one implementation
• Helper functions called only once
• Generic framework for a specific problem

Check: "Can I delete this abstraction and use the concrete implementation directly?"

Pattern 3: Stale Patterns

LLMs use patterns from training data that may be outdated.

Signals:

• Class components in React (should be hooks)
• Callbacks instead of async/await
• var instead of const/let
• jQuery patterns in modern codebase
• Deprecated API usage

Check: "Is this the current recommended pattern for this framework version?"

Pattern 4: Copy-Paste Drift

LLMs generate similar code for similar tasks without deduplication.

Signals:

• 3+ functions with >70% similar logic
• Same validation logic repeated in multiple places
• Similar error messages with slight variations
• Duplicate type definitions

Check: "Are there 3+ places doing essentially the same thing?"