text-watermark-fountain

name	text-watermark-fountain
description	A specialized skill for embedding and extracting resilient watermarks in text by manipulating sentence lengths and using Fountain Codes. Use this when the user wants to add a hidden, robust watermark to text or verify an existing one.
license	MIT
author	github/cafe3310
depends_on_skill	[]
depends_on_binary	["python3"]

Text Watermark Fountain (Robust Sync-Frame Version)

This skill enables the Agent to embed a string watermark into a text such that it can be recovered even if the text is partially modified, segments are deleted, or new sentences are inserted. It uses a custom Luby Transform (LT) Fountain Code combined with Sync Frames to map the watermark into a sequence of target lengths.

How it works (Robustness Mechanism)

1. Sync Markers: The encoding script periodically inserts a unique length pattern [19, 4, 19] (Sync Marker) followed by a Frame ID.
2. Self-Synchronization: The decoder searches the entire text for these markers using a sliding window. Even if middle segments are removed, the decoder can resynchronize using the next Sync Marker and know exactly which symbols it is looking at.
3. Redundancy: By repeating these frames throughout a long text, the watermark becomes extremely difficult to destroy.

Workflow: Embedding a Watermark

When a user asks to embed a watermark (e.g., "name_1") into a text:

1. Generate Length Sequence:

   • Run the encoding script:

     python3 scripts/encode.py --mark "name_1" --count [TOTAL_DATA_SYMBOLS]
     

   • Note the Lengths output. It will contain periodic 19 4 19 [ID] headers.

2. Precise Text Fine-tuning (Batch Processing): To ensure 100% accuracy, you MUST use a multi-stage approach:

   • Step 2.1: Redundancy & Looping: If the original text is significantly longer than the required length sequence, the script handles the symbol indexing. Just ensure you cover all target lengths.
   • Step 2.2: Segmentation: Split the source text into segments using allowed punctuation (，。！？；：、, . ! ? ; :).
   • Step 2.3: Batch Delegation: Use a subagent to rewrite segments in batches of 5-10.
     • Prompt for Subagent: "Rewrite these segments to match EXACT character lengths: [L1, L2, ...]. Maintain meaning. NO internal punctuation allowed within a segment. Count every character (Chinese, English, digits) as 1."
   • Step 2.4: Verification: After each batch, run a Python one-liner to verify:

     python3 -c "print([len(s.strip()) for s in [SEG1, SEG2, ...]])"
     

3. Final Polishing & Formatting:

   • Step 3.1: Style Alignment: Compare the verified segments with the original text. Refine phrasing to match the original's tone, but STRICTLY maintain the verified character count.
   • Step 3.2: Paragraph Reconstruction: Re-insert original line breaks. Paragraph breaks (\n) do not affect length measurement.

4. Output:

• Provide the final watermarked text to the user.

Workflow: Extracting a Watermark

1. Run Decoder:
   • Pass the text to the decoding script:

     python3 scripts/decode.py --text "THE_TEXT_CONTENT"
     

   • The script will search for sync frames and use the LT decoder to recover the mark.

Guidelines for the Agent

• Precision is Absolute: A single character error in a segment breaks that data symbol.
• Punctuation is a Splitter: Do NOT use punctuation inside a segment unless you intend to split it.
• Sync Frames are Sacred: The 19 4 19 [ID] sequence must be embedded exactly as specified in the output of encode.py.