text-watermark-fountain

To embed a hidden text watermark, this Skill uses Fountain Code encoding to modify sentence length patterns, allowing you to insert recoverable ownership markers into long-form text without altering readability.

Text watermark extraction works by detecting synchronization frames and applying decoding algorithms to recover embedded identifiers from sentence length modifications, even after the text undergoes partial edits or insertions.

Yes, you can recover a text watermark after partial modifications because the Skill applies Fountain Code redundancy and synchronization markers, ensuring resilience against text deletions or inserted content.

Yes, you need Python3 installed in your environment, as the Skill relies on Python-based scripts for deterministic watermark generation and recovery operations.

The best way to add ownership markers to generated text artifacts is by encoding watermark strings through sentence length patterns, providing a robust method for document attribution and later verification.