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Victor KuznetsovandClaude Opus 5.5 06c0cb1bbe Add Codex plugin manifest, stdlib MCP server, SHA-pinned Actions

- .codex-plugin/plugin.json and assets/icon.svg package the existing skill
  tree for Codex plugin catalogs; skill version 1.0.12 across all manifests
- `remove-ai-watermarks mcp` serves identify, remove and batch over stdio,
  one CLI command per tool call, standard library only; server.json and
  glama.json for the MCP registry and Glama
- Pin every GitHub Action to a commit SHA
- Call module.train(False) instead of module.eval(), which plugin scanners
  read as dynamic code execution

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

2026-10-17 14:13:04 -07:00

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Remove AI Watermarks

You are a principal Python engineer maintaining a CLI tool and library for removing visible and invisible AI provenance watermarks.

Scope and non-goals

The project gives users control over provenance marks on content they generated or edited themselves. It does not automatically remove stock-agency, marketplace, classifieds, tiled-preview, or other marks that protect a third party's paid or copyrighted asset.

Add visible templates only for AI-generation labels.
Do not add stock, agency, or classifieds marks to watermark_registry.py.
Do not add templates for regulator-defined disclosure icons (EU Code of Practice Annex 1).
Keep erase --region generic and user-directed; do not build an automatic stock-watermark remover on it.

Full boundary and legal context: docs/legal-and-safety.md.

How to run

uv run remove-ai-watermarks --help
bash maintain.sh

Run uv from the repository root. Command selection, options, defaults, and examples live in docs/cli.md. Before changing command routing, no-signal behavior, or exit codes, read the command-line section of docs/module-internals.md.

Configuration

GPU and ML modules are optional. Guard their imports with is_available().

Optional features and installation groups are documented in docs/installation.md. Model-running paths may use availability tests, while pure helpers in ML-adjacent modules must remain unit-tested without downloads.

Test and lint

maintain.sh runs dependency freshness and security checks, Ruff, Pyright scoped to src/, and the parallel test suite. Full-project Pyright is not the project gate because the ML dependency graph can exhaust Node memory.

The gate does not pass --ignore-unfixed to uv-secure, so a transitive CVE with no released fix stops it before Ruff, Pyright and the tests run. This matches the global rule: run and report those three separately. Scanner failures are fatal too. Triaged blockers and the recheck procedure: Known security-gate blocks in docs/development.md.

Command, gate, typing, and model-test invariants auto-load from .claude/rules/development.md. Environment recovery, CI behavior, and fixture policy live in docs/development.md.

Before a release, read docs/release-and-distribution.md. Treat the release as complete only after PyPI, Homebrew, the Hugging Face Space, and the ComfyUI Registry are verified; conda is not published. Keep the source-distribution public allowlist.

The published Agent Skill is skills/remove-ai-watermarks/. Update it in the same change as any CLI edit that alters commands, extras, exit codes, mark keys, or the intended-use boundary. Install and publish steps: docs/agent-skill.md.

The same skill tree is the Codex plugin (.codex-plugin/plugin.json). remove-ai-watermarks mcp is a standard-library MCP server that runs one CLI command per tool call; keep it dependency-free, because the MCP registry launches it as a bare uvx remove-ai-watermarks mcp that cannot add extras (docs/mcp-server.md).

Module architecture

docs/module-internals.md is the canonical per-module map, including design decisions, thresholds, calibration history, incident records, and regression guards. Read the relevant section before changing a subsystem.

Research and current constraints are routed through docs/index.md, especially docs/known-limitations.md, docs/supported-signals.md, docs/synthid.md, and docs/watermarking-landscape.md. Pixel photo classification is docs/photo-classify.md; the separate OpenAI/Google/unknown export-pipeline signal is docs/source-classify.md. Neither runs inside identify or constitutes SynthID detection. Classifier research is split between general AI-generated image classifiers and SynthID source classifiers. Other SynthID campaign logs are docs/synthid-detector-research.md and docs/synthid-removal-research.md. The pre-split chronological archive is docs/synthid-detector-removal-plan.md. The benchmark kernel and its pinned local oracles (AudioSeal, Perth, PixelSeal, VideoSeal) cover image, audio, and video watermark cohorts and research studies: docs/benchmark-kernel.md.

Data safety

Follow data/README.md for public fixture, calibration, oracle, and evaluation layout. Use only publication-cleared inputs in tracked data paths. Store each tracked binary once and keep generated evaluation outputs outside the repository.

Rules and conventions

Topic-specific rules live in .claude/rules/*.md and are auto-loaded when matching files are touched.

File	Covers
`development.md`	Command contracts, project gate, typing boundaries, model-adjacent tests, the docs-coverage and agent-skill parity seams, the detection-path measurement rule, and why cloud GPU harnesses stay out of `scripts/`

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