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Victor KuznetsovandClaude Opus 5
2bc37738d9
`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>
171 lines
8.1 KiB
Markdown
171 lines
8.1 KiB
Markdown
# Agent skill
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The published Agent Skill lives in
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[`skills/remove-ai-watermarks/`](../skills/remove-ai-watermarks/). It teaches
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coding agents to drive the `remove-ai-watermarks` CLI on content the user
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generated or edited. It is not a fifth release surface: it ships with this
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repository and updates when the skill files change.
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## Install
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Any agent that speaks the [Agent Skills](https://agentskills.io/specification)
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format:
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```bash
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npx skills add wiltodelta/remove-ai-watermarks
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```
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Claude Code, from this repository as a marketplace:
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```text
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/plugin marketplace add wiltodelta/remove-ai-watermarks
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/plugin install remove-ai-watermarks@remove-ai-watermarks
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```
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SkillsMP indexes public GitHub `SKILL.md` files and does not need a separate
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upload. ClawHub and the Claude community marketplace are explicit publishes;
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see [Release and distribution](release-and-distribution.md#agent-skill).
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## Where the skill can actually be listed
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Most skill catalogs are install registries, not discovery engines. A listing
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without search traffic does not bring people to the CLI or to
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[raiw.cc](https://raiw.cc). Prefer channels that a stranger can find, and keep
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the intended-use boundary in every public description. Official review may
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still reject a watermark-removal listing; that is a risk, not a reason to
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widen the scope.
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Do these in order after the skill is on the default branch.
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1. **GitHub default branch.** Unlocks `npx skills add wiltodelta/remove-ai-watermarks`.
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Cursor, OpenCode, Pi, Codex, and Copilot have no separate skill store; they
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consume this repo or a copied `SKILL.md`.
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2. **SkillsMP.** Aggregates public GitHub `SKILL.md` files. No submit form. A
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search for `remove-ai-watermarks` was empty before the first public push.
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Neighboring `watermark` hits are PDF-add and detection skills, not removal.
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3. **skills.sh.** No submit form. A row appears on the leaderboard only after
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people run `npx skills add`. The README install line is the seed, not a
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registration.
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4. **Awesome-list PRs.** Hand-curated GitHub lists are how people browse skills
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today. One accurate PR per list, with the scope sentence intact. Candidates:
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`ComposioHQ/awesome-claude-skills`, `VoltAgent/awesome-agent-skills`,
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`VoltAgent/awesome-openclaw-skills`.
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5. **Claude community marketplace.** Form at
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[platform.claude.com/plugins/submit](https://platform.claude.com/plugins/submit)
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(individuals) or the claude.ai directory form (Team/Enterprise). Validate
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with `claude plugin validate` first. The official Anthropic marketplace is
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curated and has no application.
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6. **ClawHub.** `clawhub login` then `clawhub skill publish` from
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`skills/remove-ai-watermarks/`. The public catalog already has an add-watermark
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skill; this would be the removal counterpart. Frame it as the user's own
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content. ClawHub disallows deception, impersonation, and fake-engagement
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install loops.
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7. **OpenAI ChatGPT / Codex plugin directory.** Skills-only plugins are a
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supported submission type at
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[platform.openai.com/plugins](https://platform.openai.com/plugins). This is
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the largest consumer surface that actually reviews listings. It needs a
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verified Platform identity, public website / support / privacy / terms URLs,
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starter prompts, and five positive plus three negative test cases. Claude
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marketplace approval does not transfer. Convert with
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[Submit your Claude Code plugin to OpenAI](https://developers.openai.com/plugins/guides/submit-claude-plugin).
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Do not treat Smithery or other MCP directories as skill catalogs. Do not open
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a second near-identical skill to farm installs. The conversion path that
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matters is: agent cannot run CUDA locally, so it sends the user to raiw.cc.
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## When to edit the skill
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Update `SKILL.md` and `references/` in the same change as any CLI edit that
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alters:
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- command names or routing;
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- extras a command needs;
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- no-signal or exit-code behavior;
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- registered visible mark keys;
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- the CUDA-only invisible-image rule;
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- the intended-use boundary.
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Keep `SKILL.md` under 500 lines. Put flag lists in `references/`. Do not copy
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calibration numbers or retired pipeline names into the skill.
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Write for every harness and a weaker model, not only Claude Opus:
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- third-person description, keywords first, so a truncated listing still matches;
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- a numbered checklist in `SKILL.md`;
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- `scripts/probe.py` for CUDA, ffmpeg, CLI, and installer detection. It measures
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CAPABILITY, not presence: it writes a blank PNG with `zlib` and `struct` and
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runs the installed `visible` on it, because exit `2` is a state only a working
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pixel stack reaches. Reporting a found binary as ready is what sent an agent
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into a missing-cv2 crash on the Homebrew build, which carries no extras. It
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also compares the installed release against `MIN_CLI_VERSION`, so a stale CLI
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is named instead of being reported as a broken flag;
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- uv, pipx, and pip as installer fallbacks;
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- forward slashes only;
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- no `allowed-tools` (that field is experimental and host-specific).
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Raise `MIN_CLI_VERSION` in `scripts/probe.py` in the same change as any reference
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that names a command, flag or value the previous release lacked. Left behind, it tells an agent its
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CLI is current right before it types an option that build rejects.
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Bump `metadata.version` in `SKILL.md` plus `version` in
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`skills/.claude-plugin/plugin.json` and `.claude-plugin/marketplace.json` when
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the skill instructions change. That version is independent of the PyPI package
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version.
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## Behavior evals
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The deterministic suite proves the skill's prose matches the CLI. It cannot prove
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the only thing a skill exists for: that a model reaches for it on a real request
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and then obeys it. `scripts/skill_eval.py` measures that.
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Each case builds a throwaway project with the skill installed and a recording
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stand-in for the CLI first on `PATH`, runs a headless agent on one user prompt,
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and grades the recorded command trace. Grading is mechanical wherever it can be:
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"did it run `all` on a machine the probe said writes nothing" is a fact in the
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trace, and "did it name an output file that is not on disk" is how a fabricated
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result is caught without asking a model to judge itself.
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```bash
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python3 scripts/skill_eval.py --model haiku --repeat 3
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python3 scripts/skill_eval.py --case visible_ru --model sonnet --out /tmp/eval.json
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```
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Cases live in `data/evaluations/skill/cases.json`; each carries the prompt, the
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scenario the stand-in replays, and the expectations. Run outputs stay outside the
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repository, per `data/README.md`. This never runs in `maintain.sh`: it spends model
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tokens and needs the `claude` CLI.
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Agents are nondeterministic, so the report is a pass RATE per check over `--repeat`
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runs, never one verdict. Weak models are the point: run haiku before opus.
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Four things the first runs found, all on haiku and none visible to the
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deterministic suite:
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- a skill that never triggered on "скажи, видно ли, что она сгенерирована ИИ",
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because the description sold removal and not the question;
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- a finished-looking removal report, with a size table for a file nobody wrote,
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on a machine whose pixel stack was missing;
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- `--mark gemini-sparkle`, a key that does not exist, guessed instead of read;
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- a removal command run to answer a question that only needed `identify`.
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`tests/test_skill_evals.py` keeps the harness honest: the stand-in's exit codes are
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compared against the real CLI where the state is reproducible locally, the grader is
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driven over hand-built traces, and every case is checked for internal consistency. A
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scenario where `identify` names a visible mark that `visible` then cannot find sends
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any agent into a retry loop and measures the case, not the skill.
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## Validate
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```bash
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claude plugin validate .
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claude plugin validate --strict .
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claude plugin validate ./skills
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```
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The suite also checks frontmatter and manifest agreement in
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`tests/test_agent_skill.py`, plus two things a catalog cannot see: that the
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skill's prose still matches the live click application (mark keys, flag choices,
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batch modes, exit codes, extras) and that a machine without the pixel stack gets
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an install hint from the CLI, a caveat from `identify`, and `pixel_stack:
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missing` from the probe.
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