Text Analysis Tools & Converters
Analyze readability, convert case formats, and perform text diffs natively in your browser. Ensure absolute privacy with offline-capable text utilities.
Why analyse text offline?
Readability analysis and text transformation tools are routinely used on draft content that may not be ready for public distribution, such as articles, legal documents, and internal reports. Sending that content to a third-party SaaS tool means it passes through external servers, gets logged, and potentially contributes to training datasets. Running the same analysis locally means your draft stays private.
The tools in this category implement standard readability formulas (Flesch-Kincaid Grade Level, Gunning Fog Index, SMOG) and common string transformations (camelCase, snake_case, kebab-case) entirely in the browser. There is no round-trip: you type, the result appears instantly, and nothing is stored.
Offline availability is particularly useful for writers working on long-form content in environments with unreliable connectivity, such as planes, conference venues, and rural locations. Load the tools once over a reliable connection and they remain available for the rest of your session.
No. CapyToolkit doesn't store, upload, or log any text you enter. It lives in your browser's memory while you use the tool and is gone when you close or refresh the tab.
Yes. All processing happens locally once the page loads. No network calls are made while you type or analyse.
No hard limit is enforced. Very long documents may slow down scoring on older hardware, but there is no cutoff.
No. CapyToolkit doesn't require registration of any kind. Nothing to sign up for.
Yes. The text never leaves your browser, so these tools are safe for internal drafts, legal documents, or anything you would not want uploaded to a third-party server.
Chrome, Firefox, Edge, and Safari on current stable versions. Mobile browsers are supported too.
Request a tool or share feedback
Have an idea for a new tool? Leave a message about what you'd like to see added.