Spotting GitHub Copilot Artifacts: The Ultimate Guide to AI Written Code Detection
# Spotting GitHub Copilot Artifacts: The Ultimate Guide to AI Written Code Detection
GitHub Copilot and Cursor inline auto-completions have revolutionized daily developer workflows. However, accepting inline completions uncritically leads to subtle codebase degradation.
---
## Common GitHub Copilot Code Patterns
- **Hallucinated Utility Helpers:** Copilot frequently invents slight variations of existing internal helper functions.
- **Copy-Paste Pattern Duplication:** Repeated multi-line conditionals across contiguous files.
- **Missing Accessibility Attributes:** Missing `aria-label` or `alt` tags on auto-completed UI elements.
---
## Measuring AI Code Quality with Vibe Score
VibeFix provides a unified numerical metric—the **Vibe Score (0-100)**—to evaluate code quality:
- **80–100 (Excellent):** Clean, intentional, human-quality code.
- **60–79 (Good):** Minor AI artifacts present; quick refactors suggested.
- **40–59 (Needs Work):** Significant synthetic debt present.
- **0–39 (Critical):** Heavy AI slop throughout; refactor required.
Audit your code today at [vibefix.app/vibe-check](https://vibefix.app/vibe-check).
Scan your Repo and URL
See what AI broke in 30 seconds — with a full Neural DNA breakdown and fix roadmap.
