SNSwatcher: Watermark -> Comment Summary Memory

Use this skill when the user asks to update memory from a local SNS comment file or to test the IBIA watermark-memory prototype.

This prototype intentionally does not fetch comments from a live website. It reads comments from a text file or a localhost HTTP test page, verifies watermarks, and appends a structured memory block to MEMORY.md.

Goal

Convert one or more verified watermarked comments into organized memory notes:

M_g = P_g || (topic_1:summary_1) || ... || (topic_n:summary_n)

where:

• P_g is the category-specific context prefix.
• topic_i is the extracted topic.
• summary_i is a short summary of the verified comment content.

Do not write comments that fail watermark verification.

Input Format

Use a local text file with one comment per line. Markdown bullet format is preferred:

- That Galaxy S26 looks slick. Did you hear about the debate between Adobe Firefly and Midjourney? I think Adobe Firefly wins because licensing is safer.

Plain one-comment-per-line text also works.

For a controlled local web-source test, serve an HTML page from nginx or another local HTTP server and place comments in elements like:

<article class="moltbook-comment">Did you hear about the topic? Summary text.</article>
<div data-comment="Did you hear about another topic? Another summary."></div>

URL input is limited to local test hosts: localhost, 127.0.0.1, ::1, and host.docker.internal.

Run

Inside OpenClaw, run:

cd /home/node/.openclaw/workspace/skills/snswatcher

python3 prototype.py advertisement /home/node/.openclaw/workspace/comments_raw.md

For a local nginx-style HTTP test page:

python3 prototype.py advertisement http://127.0.0.1:8088/fake_reddit.html

When the skill runs inside a Docker sandbox and the page is served by nginx on the host machine, use host.docker.internal instead of 127.0.0.1.

Required arguments:

• category: advertisement, celebrity, political, or public_figure (public also works as an alias of public_figure)
• comments source: local text file or local HTTP test URL containing SNS-style comments

By default, the prototype writes to /home/node/.openclaw/workspace/MEMORY.md inside OpenClaw, or ./MEMORY.md in a local demo folder. Use --memory-out only when the memory path should be different.

The script uses detect_watermark.py in this same skill folder by default.

What the Script Does

1. Read comments from the input text file or local HTTP test page.
2. Normalize them into - comment lines for the detector.
3. Run:

python3 detect_watermark.py <normalized_comments.md> --csv detection.csv

4. Keep only rows where watermarked=True.
5. Extract the topic and comment summary from hooks such as:

Did you hear about the debate between A and B? <comment summary>
Did you hear about <topic>? <comment summary>
Did you hear that <topic>? <comment summary>

6. Append structured memory to MEMORY.md.

If no comment passes watermark detection, the script leaves MEMORY.md unchanged and prints watermarked=0.

Notes

• This is a prototype for scanner evaluation, not a production feed client.
• Keep detect_watermark.py as the watermark verification engine.
• Keep the local-file input path for reproducible baseline testing.
• Use local HTTP input only for controlled web-source tests. Do not use this prototype as a live website client.