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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.

  1. 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.
  2. SkillsMP. Aggregates public GitHub SKILL.md files. No submit form. A search for remove-ai-watermarks now returns this skill. Neighboring watermark hits are PDF-add and detection skills, not removal.
  3. skills.sh. No submit form. A row appears on the leaderboard only after people run npx skills add. The listing is live and has recorded real installs; the README install line remains the seed, not a registration.
  4. Awesome-list PRs. Hand-curated GitHub lists are how people browse skills today. One accurate PR per list, with the scope sentence intact.
    • ComposioHQ/awesome-claude-skills: submitted 2026-09-05 as PR 1841 (Creative & Media); no maturity bar in its contribution rules. Rechecked 2026-09-11: still open, CI green, no review comments, waiting on the list maintainers.
    • heilcheng/awesome-agent-skills: submitted 2026-09-09 as PR 483 under Community Skills > Media. The skill's three concrete input/output examples satisfy the list's working-example requirement. Rechecked 2026-09-11: still open; the only check failure is their Vercel deploy authorization, which a contributor cannot grant.
    • VoltAgent/awesome-agent-skills: DEFERRED. Its CONTRIBUTING rejects brand-new skills ("give your skill time to mature and gain users before submitting"). The skill now has real installs and shipped releases, but adoption remains too small to make the list's maturity claim comfortably.
    • VoltAgent/awesome-openclaw-skills: DEFERRED on adoption only. Entries are indexed via clawskills.sh, and the ClawHub prerequisite in step 6 is now met. The list still requires proven real-world usage. Re-attempt once the ClawHub listing shows organic installs.
  5. 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. SUBMITTED 2026-09-07 for Claude Code only (strict validation passed; Apache 2.0; description carries the scope boundary; platform selection limited to Claude Code because Cowork was not tested). Pending review; the team may reach out by email. Rechecked 2026-09-11: platform.claude.com/plugins is 404, and claude.ai/directory is the MCP connector catalog, not plugins; a search there does not list this skill. Do not open a second submission. The official Anthropic marketplace is curated and has no application.
  6. ClawHub. clawhub login then clawhub skill publish from skills/remove-ai-watermarks/. LISTED 2026-09-09: versions 1.0.5, 1.0.6, and 1.0.8, submitted by the clawhub job in distribute.yml during the 0.39.0, 0.39.1, and 0.40.0 releases, are public. A green job proves only a submission, so read the catalog itself: clawhub search remove-ai-watermarks returns the row and https://clawhub.ai/api/v1/skills/remove-ai-watermarks reports latestVersion.version 1.0.8, while its /versions endpoint returns 1.0.5, 1.0.6, and 1.0.8. The earlier 1.0.3 and 1.0.4 submissions never became public and do not appear in that version list, so they were bypassed rather than recovered. 1.0.7 was a post-0.39.1 bump that never reached ClawHub. The issue that tracked 1.0.3 and 1.0.4, openclaw/clawhub#3643, was closed by the reporter on 2026-09-09 once 1.0.5 went through; nothing upstream was confirmed fixed, and it is the fifth report of this shape (#3624, #3466, #3351, #3284), so treat a stalled submission as recurring and file a fresh one rather than assuming the pipeline is repaired. Release-time publishing is automated (see release-and-distribution.md), and the job fires only on a published GitHub Release: a skill version bumped after a release reaches ClawHub with the next one. Do not publish by hand to close that gap. The listing records the license as MIT-0 while SKILL.md declares Apache-2.0, and that is not a listing error to repair: MIT-0 is a platform-wide constant in ClawHub's own client (dist/schema/license.js, and its version schema accepts only "MIT-0" or null), so every skill published there is offered under MIT No Attribution. Publishing to ClawHub means offering this skill on those terms in addition to the repository's Apache-2.0; do not try to edit the field, and do not read the mismatch as drift. The public catalog already has an add-watermark skill; this is the removal counterpart, framed as the user's own content. ClawHub disallows deception, impersonation, and fake-engagement install loops.
  7. 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.
  8. AgenticSkills. SUBMITTED 2026-09-09 as review issue 174 under AI/ML Development. The submission carries the user-owned-content boundary and links to the repository rather than hosting a separate copy. Rechecked 2026-09-11: the public index still does not list it (/skills/remove-ai-watermarks is 404; search for wiltodelta returns 0 of 192). Their form promises a 48-hour review; that window has passed. Issue 174 is not a public GitHub issue. Do not open a second submission. Followed up 2026-09-11 through their contact form as a listing correction.
  9. AI Skills Catalog. SUBMITTED 2026-09-09 as reference 2 under Video Media, with Codex as the primary agent and every supported harness marked compatible. Rechecked 2026-09-11: the public catalog at skillscatalog.ai shows 0 skills from 0 publishers, /api/featured returns 401, and /api/auth/session returns 500, so a stranger cannot confirm the listing. Do not open a duplicate while that initial review is pending. Once accepted, treat material skill-version updates as manual because the catalog stores compatible packages in addition to source metadata and does not promise that a GitHub change refreshes those packages. Use the update route supplied with acceptance; if none is supplied, contact the catalog or resubmit, then verify the live result.

The conditional update actions for every catalog are part of the canonical release checklist in Release and distribution.

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. Video capability is checked separately in the installed CLI's Python environment: video_stacks.pixels reports ok, missing, or unknown, and video_stacks.invisible reports installed, missing, or unknown. These checks load no weights. An unrecognized launcher leaves video guidance unverified rather than inferring readiness from image support;
  • 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.