Inspecting repository signals
Scanning repository metadata, dependencies, and documentation for AI provenance signals.
Fun fact: repo scans look for AI tooling, AI service dependencies, editor traces, and documented workflow clues.
Scanning repository metadata, dependencies, and documentation for AI provenance signals.
Fun fact: repo scans look for AI tooling, AI service dependencies, editor traces, and documented workflow clues.
GitHub Repository Scan
Scanner classification based on observable evidence
This repository may use AI tools. Some indicators were found, but evidence is not conclusive.
Balanced signal mix. The scanner considers evidence from multiple independent categories.
Key telemetry from the analysis
1 signal detected
1 signal detected
Named patterns and framework traces
Implementation-level observations
| Attribute | Value |
|---|---|
| HTML payload | 0.0 KB |
| DOM structures | 0 |
| Script assets | 0 |
| Stylesheet assets | 0 |
| Token entropy | 0.00 |
| Repeated text | 0.0% |
| Visible words | 0 |
| Headings | 0 |
| Internal links | 0 |
| External links | 0 |
| Forms | 0 |
| Social links | 0 |
| Policy links | 0 |
| Contact signals | 0 |
| Trust signals | 0 |
The scanner found 1 signal commonly associated with AI/no-code construction, but also identified 1 signal associated with manual engineering and 0 trust markers. Because evidence exists on both sides and the signal counts are balanced, the system currently classifies this site as uncertain rather than strongly attributing it to either category.
This result does not prove who built the website or whether AI was used during development. The scanner evaluates observable technical and structural signals. A human-built website can exhibit builder-like fingerprints, and an AI-assisted website can contain extensive human engineering.
The scanner evaluates observable technical fingerprints including framework and platform clues, generated or builder-style structures, identifier patterns, metadata quality, structured data, asset characteristics, engineering signals, and trust markers.
No single fingerprint proves AI authorship. The final classification combines multiple independent observations, weighting them against each other to produce an explainable verdict with a confidence score.