---
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LLMs use patterns from training data that may be outdated.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

| Severity | Impact | Fix Timeline |
|----------|--------|-------------|
| **Critical** | Security vulnerability, data loss risk | Immediately |
| **High** | Production failures under load or edge cases | This sprint |
| **Medium** | Maintainability issues, duplication | Next sprint |
| **Low** | Style, naming, minor abstractions | Backlog |

## 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)