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Victor KuznetsovandClaude Opus 5 2bc37738d9 Publish the agent skill that drives this CLI 
`skills/remove-ai-watermarks/` teaches a coding agent to drive the CLI on
content the user generated, and refuses stock-agency and other third-party
paid-asset marks. It ships with this repository rather than as a fifth release
surface, and installs through `npx skills add` in any harness or as a Claude
plugin from `.claude-plugin/marketplace.json`.

`scripts/probe.py` measures capability rather than presence: a found binary said
nothing about whether the build can run a visible-mark command, so it writes a
blank PNG with zlib and struct and runs the installed `visible` and `invisible`
on it. Exit 2 is a state only a working stack reaches, and `invisible` checks
its extra before detection, so a signal-free image answers in 0.23s with no
model download. That distinction matters: without CUDA, `all` writes nothing
with the invisible extra installed and writes a useful visible+metadata result
without it, and both exit 1.

The skill also carries what a weak model gets wrong. It must report only what a
command in the session actually did, because a run on a machine with no pixel
stack produced a finished-looking removal report, complete with a size table for
a file nobody wrote. It must not read exit 2 as a verdict, because Click's usage
errors share that code. And it must get explicit consent before
`verify-openai-synthid`, which uploads the user's image to a third party.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-05 11:27:17 -07:00

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Documentation

This documentation is split by purpose. Start with the user guides if you want to run the tool. Use the maintainer references only when changing the code.

User guides

Page Use it when
Installation You need the CLI, an optional model backend, or a development environment.
CLI guide You want a command for an image, video metadata or visible marks, a directory, or a specific watermark type.
Python API You want to call the package from Python.
Supported signals You need to know which visible marks, metadata formats, and invisible signals are covered.
Known limitations You need the quality, device, format, or verification boundaries.
Photo pixel classification You want classify / classify_pixels: AI versus camera, optional provider.
Photo classifier training You need the retrain pack, public sources, and what is not published.
Scope, safety, and legal notes You need the intended use and legal context.

Maintainer references

Page Purpose
Module internals Current architecture, invariants, and regression guards by module.
Development Environment setup, dependency recovery, CI behavior, and fixture policy.
Code provenance Required notices for licensed derivative work.
Verification plan Verification methods, completed measurements, and remaining validation gaps.
Release and distribution PyPI, Homebrew, Hugging Face Space, photo-classify Hub model, ComfyUI Registry, and the release workflow.
Agent skill Install and maintain the published Agent Skill and Claude plugin.
Watermarking landscape Vendor signals and detection approaches.
SynthID technical reference Mechanism, provenance, robustness, regeneration.

Research archive

These pages record experiments and the evidence behind past decisions. They are not command references and may describe prototypes that were later removed. The current behavior is defined by the code, tests, README, and user guides.

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