| name | detecting-ai-code |
| description | Use when auditing code for AI authorship, reviewing acquisitions/contractors, verifying academic integrity, or during code review - provides systematic tiered framework for detecting fully AI-generated AND AI-assisted code patterns with confidence scoring |
| metadata | {"author":"galihcitta"} |
Detecting AI-Generated Code
Overview
Systematic framework for detecting AI-generated and AI-assisted code. Uses tiered signal detection with confidence scoring.
Core principle: Check metadata FIRST (highest confidence), then comments, then code patterns. Multiple signals compound confidence.
Detection Framework (Check in Order)
Tier 1: Metadata Signals (Highest Confidence)
ALWAYS CHECK THESE FIRST - they're definitive.
| Signal | What to Look For | Confidence |
|---|
| Git headers | Co-Authored-By: Claude, Co-Authored-By: GitHub Copilot | Definitive |
| Commit patterns | Uniform "Add X functionality" pattern across all commits | High |
| Generated footers | "Generated with [Claude Code]", "Created by Copilot" | Definitive |
| Config files | .cursorrules, CLAUDE.md in repo | High |
| README template | Exact Claude/GPT README structure (see below) | High |
AI README Template Pattern:
# Project Name
## Overview
[Formal description]
## Features
- Feature 1
- Feature 2
## Installation
[Perfect instructions]
## API Endpoints (table)
## Environment Variables (table)
## Contributing
## License
If README follows this EXACT structure with perfect tables and no personality → High AI probability.
AI Commit Message Patterns:
Add user authentication with JWT tokens
Add password recovery functionality
Add comprehensive documentation ← "comprehensive" is strong AI tell
Implement feature X
Update component Y
Fix bug in Z
Mechanical uniformity + "Add/Implement/Update/Fix" pattern → AI signal.
Human commit patterns (for contrast):
wip: payments
fix typo in readme
initial payment setup
refactor auth - cleanup
oops forgot the tests
Tier 2: Comment Patterns (High Confidence)
| Signal | Example | Confidence |
|---|
| Narrating comments | "First, we...", "Here we...", "Now we..." | High |
| Restating code | // Initialize array above const arr = [] | High |
| Tutorial style | "This function will...", "Let me explain..." | High |
| Perfect grammar | No typos, formal English in all comments | Medium |
| Over-documentation | 20-line JSDoc for 5-line function | High |
Verbatim AI Phrases:
- "This file contains..."
- "This function handles..."
- "Here we define..."
- "Now we check..."
- "Finally, we return..."
Tier 3: Code Structure (Medium Confidence)
| Signal | Example | Confidence |
|---|
| Over-engineering | Comprehensive error handling for impossible cases | Medium |
| Template repetition | Same pattern copied across files exactly | Medium |
| Perfect consistency | Identical formatting across entire codebase | Low-Medium |
| Defensive coding | Null checks on values that can't be null | Medium |
Important: Clean code alone is NOT evidence of AI. Experienced developers also write clean code.
Tier 4: Anti-Signals (What Humans Do, AI Doesn't)
These DECREASE AI probability:
| Human Signal | Why |
|---|
| TODO/FIXME comments | AI rarely leaves work incomplete |
| Debug code left in | console.log, commented code |
| Shortcuts/abbreviations | amt, curr, intl instead of full words |
| Domain jargon | Industry-specific terms, internal naming |
| Stack Overflow attribution | "grabbed from SO", "copied from [link]" |
| Inconsistent formatting | Mixed styles within file |
| Pragmatic compromises | "prod will have proper checks" |
Confidence Scoring
| Signals Found | Confidence Level | Recommendation |
|---|
| 1 Tier-1 signal | High | Flag for review |
| 2+ Tier-2 signals | High | Likely AI-generated |
| Tier-2 + Tier-3 combined | Medium-High | Probably AI-assisted |
| Only Tier-3 signals | Low-Medium | Investigate more |
| Anti-signals present | Reduces confidence | Human likely involved |
Compound scoring: Multiple signals from different tiers = higher confidence than multiple signals from same tier.
AI-Assisted Detection (Partial AI Use)
Look for:
- Style jumps - Casual code with formal JSDoc blocks
- Git history jumps - "Add comprehensive documentation" as separate commit
- Quality inconsistency - Some files over-documented, others bare
- Misplaced docs - JSDoc on library calls, not wrapper functions
- Parameter mismatches - Docs describe different params than code has
This pattern: Human wrote code → Asked AI to add documentation.
Common Mistakes
| Mistake | Reality |
|---|
| "Clean code = AI" | Experienced developers write clean code too |
| Only checking code | Metadata is highest-confidence signal |
| Missing git history | "Add comprehensive documentation" commits reveal AI use |
| Binary thinking | AI-assisted is different from fully AI-generated |
| Single signal certainty | Compound multiple signals before concluding |
Quick Reference Workflow
- Check git log - Look for Co-Authored-By, uniform commit patterns
- Check README - Template structure? Perfect tables? No personality?
- Check file headers - Generated footers? "This file contains..." comments?
- Check code comments - Narrating style? Over-documentation?
- Check for anti-signals - TODOs? Shortcuts? Debug code?
- Score confidence - Compound signals from multiple tiers
- Report with evidence - List specific signals found
When NOT to Flag
- Clean, well-written code (without other signals)
- Standard patterns for solved problems (EventEmitter, validators)
- Good documentation (if style matches rest of codebase)
- Experienced developer output
Clean code without Tier-1 or Tier-2 signals = insufficient evidence.
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