Skaylar is a simple, web-based tool for forensic data analysis using Benford's Law to analyze and visualize first-digit distributions in a dataset. Paste the data, click Run Test, and instantly see if there are anomalies.

What is Benford's Law? In real world data that grow logarithmically and do not have imposed limits, the digit "1" appears as the first digit ~30% of the time, "2" ~18%, etc. If your data deviates too much, it can signal fraud, errors, or manipulation. Skaylar checks this for you in seconds. How to Use 1. Visit https://temi-tade.github.io/Skaylar or download the repo (offline use): `git clone https://github.com/Temi-Tade/Ska...` or click `Code > Download ZIP` and extract files on your computer. 2. Open Double-click `index.html` Works in Chrome, Edge, Firefox, and on mobile. 3. Analyze - Paste your numbers in the box. Numbers should be delimited by commas, spaces or new lines - Click `Run Test` - View the results table and graph - Click `Run Mock Data` to try it with sample data first What You Get - Results Table: Expected % vs Actual % for digits 1-9 and deviations - Graph: Visual comparison to spot deviations fast - Analytics: Max deviation and Chi square value.

would be great if you could upload a CSV directly instead of pasting data, and maybe add the option to compare multiple datasets side by side in the same chart

@todd_otterburn Absolutely, this is the first version that was shipped, I am currently working on that among other features, I just added a new feature where users can select .json datasets and run tests on them.

The comparison feature you mentioned is great too, I will start working on that once I ship the CSV file upload feature. Thank you so much 🫡

would be great if you could upload a CSV directly instead of pasting data, and maybe add the option to compare multiple datasets side by side in the same chart

The comparison feature you mentioned is great too, I will start working on that once I ship the CSV file upload feature. Thank you so much 🫡

Skaylar

Analyze and visualize first-digit distributions in datasets.

Analyze and visualize first-digit distributions in datasets.