ai-debt-detector

Overview

AI agents generate code that passes the happy path but hides debt: missing error handling, orphaned resources, ignored failure modes, hallucinated packages, silent architectural drift. This skill forces a targeted audit for the exact patterns AI agents get wrong.

When to Use

• After any AI code generation session (agent wrote 20+ lines)
• Before merging AI-generated PRs
• When code "works" but something feels off
• After vibe-coding sprints — debt accumulates fastest here
• When you find yourself saying "the agent said it's done"

Process

After ANY code generation, scan for these AI-specific debt patterns:

1. FAILURE MODES — What happens when this fails?
   - Network timeout? Disk full? Permission denied? Null input?
   - Is there a try/catch? Does it catch SPECIFIC errors or swallow everything?
   - Are resources cleaned up on failure? (streams closed, connections returned, temp files deleted)

2. ORPHANS — What gets created but never cleaned up?
   - Temp files, event listeners, intervals, subscriptions, connections
   - Are there corresponding cleanup/dispose/close calls for every open/create?
   - In React: does every addEventListener have a removeEventListener in cleanup?

3. EDGE CASES — What inputs break this?
   - Empty array/string? null/undefined? Very large input? Unicode? Concurrent calls?
   - Does the code assume the happy path? (hint: AI almost always does)

4. HALLUCINATED DEPS — Do all imports actually exist?
   - Is every package in package.json/requirements.txt?
   - Are API methods real? (AI invents plausible-sounding methods that don't exist)
   - Check: does this library's latest version still export this function?

5. ARCHITECTURAL DRIFT — Does this match the project's patterns?
   - Same error handling style as existing code?
   - Uses the project's established utilities (not reinventing)?
   - Follows the file structure convention?
   - Matches naming patterns (camelCase vs snake_case, etc.)

Red Flags (stop and fix IMMEDIATELY)