Agent skill

The published Agent Skill lives in skills/remove-ai-watermarks/. It teaches coding agents to drive the remove-ai-watermarks CLI on content the user generated or edited. It is not a fifth release surface: it ships with this repository and updates when the skill files change.

Install

Any agent that speaks the Agent Skills format:

npx skills add wiltodelta/remove-ai-watermarks

Claude Code, from this repository as a marketplace:

/plugin marketplace add wiltodelta/remove-ai-watermarks
/plugin install remove-ai-watermarks@remove-ai-watermarks

SkillsMP indexes public GitHub SKILL.md files and does not need a separate upload. ClawHub and the Claude community marketplace are explicit publishes; see Release and distribution.

Where the skill can actually be listed

Most skill catalogs are install registries, not discovery engines. A listing without search traffic does not bring people to the CLI or to raiw.cc. Prefer channels that a stranger can find, and keep the intended-use boundary in every public description. Official review may still reject a watermark-removal listing; that is a risk, not a reason to widen the scope.

Do these in order after the skill is on the default branch.

GitHub default branch. Unlocks npx skills add wiltodelta/remove-ai-watermarks. Cursor, OpenCode, Pi, Codex, and Copilot have no separate skill store; they consume this repo or a copied SKILL.md.
SkillsMP. Aggregates public GitHub SKILL.md files. No submit form. A search for remove-ai-watermarks was empty before the first public push. Neighboring watermark hits are PDF-add and detection skills, not removal.
skills.sh. No submit form. A row appears on the leaderboard only after people run npx skills add. The README install line is the seed, not a registration.
Awesome-list PRs. Hand-curated GitHub lists are how people browse skills today. One accurate PR per list, with the scope sentence intact. Candidates: ComposioHQ/awesome-claude-skills, VoltAgent/awesome-agent-skills, VoltAgent/awesome-openclaw-skills.
Claude community marketplace. Form at platform.claude.com/plugins/submit (individuals) or the claude.ai directory form (Team/Enterprise). Validate with claude plugin validate first. The official Anthropic marketplace is curated and has no application.
ClawHub. clawhub login then clawhub skill publish from skills/remove-ai-watermarks/. The public catalog already has an add-watermark skill; this would be the removal counterpart. Frame it as the user's own content. ClawHub disallows deception, impersonation, and fake-engagement install loops.
OpenAI ChatGPT / Codex plugin directory. Skills-only plugins are a supported submission type at platform.openai.com/plugins. This is the largest consumer surface that actually reviews listings. It needs Apps Management write access, a verified Platform identity, public website / support / privacy / terms URLs, starter prompts, and five positive plus three negative test cases. Claude marketplace approval does not transfer. Contact your OpenAI partner before submitting: this skill's core value requires local execution, arbitrary file access, and optional hardware access, which the official guide routes through that product-specific review. Convert with Submit your Claude Code plugin to OpenAI.

Do not treat Smithery or other MCP directories as skill catalogs. Do not open a second near-identical skill to farm installs. The conversion path that matters is: agent cannot run CUDA locally, so it sends the user to raiw.cc.

When to edit the skill

Update SKILL.md and references/ in the same change as any CLI edit that alters:

command names or routing;
extras a command needs;
no-signal or exit-code behavior;
registered visible mark keys;
the CUDA-only invisible-image rule;
the intended-use boundary.

Keep SKILL.md under 500 lines. Put flag lists in references/. Do not copy calibration numbers or retired pipeline names into the skill.

Write for every harness and a weaker model, not only Claude Opus:

third-person description, keywords first, so a truncated listing still matches;
a numbered checklist in SKILL.md;
scripts/probe.py for CUDA, ffmpeg, CLI, and installer detection. It measures CAPABILITY, not presence: it writes a blank PNG with zlib and struct and runs the installed visible on it, because exit 2 is a state only a working pixel stack reaches. Reporting a found binary as ready is what sent an agent into a missing-cv2 crash on the Homebrew build, which carries no extras. It also compares the installed release against MIN_CLI_VERSION, so a stale CLI is named instead of being reported as a broken flag;
uv, pipx, and pip as installer fallbacks;
forward slashes only;
no allowed-tools (that field is experimental and host-specific).

Raise MIN_CLI_VERSION in scripts/probe.py in the same change as any reference that names a command, flag or value the previous release lacked. Left behind, it tells an agent its CLI is current right before it types an option that build rejects.

Bump metadata.version in SKILL.md plus version in skills/.claude-plugin/plugin.json and .claude-plugin/marketplace.json when the skill instructions change. That version is independent of the PyPI package version.

Behavior evals

The deterministic suite proves the skill's prose matches the CLI. It cannot prove the only thing a skill exists for: that a model reaches for it on a real request and then obeys it. scripts/skill_eval.py measures that.

Each case builds a throwaway project with the skill installed and a recording stand-in for the CLI first on PATH, runs a headless agent on one user prompt, and grades the recorded command trace. Grading is mechanical wherever it can be: "did it run all on a machine the probe said writes nothing" is a fact in the trace, and "did it name an output file that is not on disk" is how a fabricated result is caught without asking a model to judge itself.

python3 scripts/skill_eval.py --model haiku --repeat 3
python3 scripts/skill_eval.py --case visible_ru --model sonnet --out /tmp/eval.json

Cases live in data/evaluations/skill/cases.json; each carries the prompt, the scenario the stand-in replays, and the expectations. Run outputs stay outside the repository, per data/README.md. This never runs in maintain.sh: it spends model tokens and needs the claude CLI.

Agents are nondeterministic, so the report is a pass RATE per check over --repeat runs, never one verdict. Weak models are the point: run haiku before opus.

Four things the first runs found, all on haiku and none visible to the deterministic suite:

a skill that never triggered on "скажи, видно ли, что она сгенерирована ИИ", because the description sold removal and not the question;
a finished-looking removal report, with a size table for a file nobody wrote, on a machine whose pixel stack was missing;
--mark gemini-sparkle, a key that does not exist, guessed instead of read;
a removal command run to answer a question that only needed identify.

tests/test_skill_evals.py keeps the harness honest: the stand-in's exit codes are compared against the real CLI where the state is reproducible locally, the grader is driven over hand-built traces, and every case is checked for internal consistency. A scenario where identify names a visible mark that visible then cannot find sends any agent into a retry loop and measures the case, not the skill.

Validate

claude plugin validate .
claude plugin validate --strict .
claude plugin validate ./skills

The suite also checks frontmatter and manifest agreement in tests/test_agent_skill.py, plus two things a catalog cannot see: that the skill's prose still matches the live click application (mark keys, flag choices, batch modes, exit codes, extras) and that a machine without the pixel stack gets an install hint from the CLI, a caveat from identify, and pixel_stack: missing from the probe.

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