name: tech-debt-detector description: Detect and classify technical debt in AI-generated code — patterns specific to LLM outputs, shallow implementations, missing edge cases, and accumulation signals. version: "1.0.0" last-updated: "2026-04-22" model_tested: "claude-sonnet-4-6" category: eval platforms: [claude-code, codex, gemini-cli, cursor, copilot, windsurf, cline] language: en geo_relevance: [global] priority: high dependencies: mcp: [] skills: [] apis: [] data: [] update_sources:

• url: "https://newsletter.pragmaticengineer.com/p/the-impact-of-ai-on-software-engineers-2026" check_frequency: "quarterly" last_checked: "2026-04-22" license: MIT

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LLMs use patterns from training data that may be outdated.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?"

Pattern 5: Missing Observability

LLMs rarely add logging, metrics, or monitoring.

Signals:

• No logging in error paths
• No metrics on key operations
• No health check endpoints
• No structured error codes
• No request tracing

Check: "If this fails in production at 3 AM, can I diagnose the problem from logs alone?"

Pattern 6: Hardcoded Configuration

LLMs often hardcode values that should be configurable.

Signals:

• URLs, ports, timeouts in code (not config)
• Magic numbers without named constants
• Environment-specific values in source
• Feature flags as if/else in code

Check: "Can I deploy this to a different environment without changing code?"

Debt Classification

Output Format

TECH DEBT SCAN: {file/module}
Generated by: {AI tool if known}
Patterns found: {count}

[HIGH] Pattern 1 (Shallow): No error handling in fetchUserData() (line 42)
[MED]  Pattern 4 (Copy-Paste): validateInput() duplicated in 3 controllers
[LOW]  Pattern 3 (Stale): Using deprecated fetch API options (line 78)

Estimated debt: {hours to fix}
Priority: Fix HIGH items before merging.

What This Skill Does NOT Do

• Does not fix the debt (identifies and classifies)
• Does not replace SonarQube or ESLint (complements with AI-specific patterns)
• Does not measure test coverage (use coverage tools)
• Does not block PRs (advisory only)