Strip multi-vendor AI provenance marks from text and files using the guillaumemeyer/watermarks-remover Python toolkit: invisible Unicode/space homoglyphs (Layer A, deterministic), statistical token-sampling watermarks via agent-guided rewrite (Layer B, best-effort), and C2PA/EXIF/XMP/doc-props metadata on PNG, JPEG, SVG, PDF, DOCX, ODT, HTML, and Markdown. Covers Claude, Gemini/SynthID-Text, OpenAI provenance surfaces, and open-LLM Kirchenbauer-style marks, plus optional external backends for SynthID pixel scoring and CtrlRegen pixel-domain removal. Use when the user wants to strip AI watermarks, remove C2PA / Content Credentials, clean AI metadata from a file, remove invisible Unicode / zero-width characters from AI-generated text, or audit a directory/website for AI provenance signals. Triggers on: "remove watermark", "strip C2PA", "remove AI metadata", "clean invisible unicode", "remove-ai-marks", "SynthID removal", "audit_dir.py", "Layer A / Layer B watermark removal".
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Strip multi-vendor AI provenance marks from text and files using the guillaumemeyer/watermarks-remover Python toolkit: invisible Unicode/space homoglyphs (Layer A, deterministic), statistical token-sampling watermarks via agent-guided rewrite (Layer B, best-effort), and C2PA/EXIF/XMP/doc-props metadata on PNG, JPEG, SVG, PDF, DOCX, ODT, HTML, and Markdown. Covers Claude, Gemini/SynthID-Text, OpenAI provenance surfaces, and open-LLM Kirchenbauer-style marks, plus optional external backends for SynthID pixel scoring and CtrlRegen pixel-domain removal. Use when the user wants to strip AI watermarks, remove C2PA / Content Credentials, clean AI metadata from a file, remove invisible Unicode / zero-width characters from AI-generated text, or audit a directory/website for AI provenance signals. Triggers on: "remove watermark", "strip C2PA", "remove AI metadata", "clean invisible unicode", "remove-ai-marks", "SynthID removal", "audit_dir.py", "Layer A / Layer B watermark removal".
allowed-tools
Bash Read Write Edit Glob Grep
compatibility
Python 3.10+ stdlib only for the core inspect/clean scripts (no pip install required beyond the clone). Optional system tools `c2patool` and `exiftool` improve C2PA/PDF handling when present. Optional heavy external backends (SynthID pixel scoring via `aloshdenny/reverse-SynthID`, CtrlRegen pixel removal via `mertizci/noai-watermark`) need their own venvs, ~10 GB of model downloads, and a GPU is strongly recommended for CtrlRegen. MIT license.
Agent skill wrapping guillaumemeyer/watermarks-remover, a set of stdlib
Python scripts that remove multi-vendor AI provenance marks from text
(invisible Unicode, statistical sampling watermarks) and files (C2PA /
EXIF / XMP / document properties). It is for privacy and hygiene on content
you own or are authorized to process — not for academic fraud or
falsely claiming "human-written" origin.
When to use this skill
Stripping invisible Unicode, bidi controls, tag characters, or exotic
space homoglyphs that AI tools silently inject into generated text
Removing C2PA manifests, EXIF/XMP AI-provenance fields, or generator
metadata from PNG, JPEG, SVG, PDF, DOCX, ODT, HTML, or Markdown files
Reducing a statistical (token-sampling) watermark's signal in AI-written
prose via a guided rewrite pass, while preserving facts/names/numbers
Auditing a whole directory tree or a live website (via its sitemap) for
AI provenance signals and getting a confidence-classified report
Optionally scoring or removing a pixel-domain SynthID-class watermark on
an image, using the project's external bootstrap scripts
When not to use this skill
The user wants a guarantee that a vendor's official detector will fail —
this toolkit only reports verifiable removals plus best-effort rewrites;
no local tool can certify that outcome
Removing audio/video watermarks or defeating training-data backdoor
triggers — explicitly out of scope upstream
Clearing C2PA soft binding (a watermark that re-links a stripped file
to a remote Content Credentials manifest) — stripping hard-bound C2PA
metadata does not clear this channel
The user's stated goal is academic fraud, plagiarism, or a false
"human-written" claim about content they don't own — warn per
skills/remove-ai-marks/references/ethics.md upstream and only perform
technical cleaning on content they are authorized to process
Instructions
Step 1: Clone the repo and resolve the scripts directory
The toolkit is not published as a pip package; it is consumed as a
standalone script directory (Python 3.10+ stdlib only). c2patool and
exiftool are auto-detected and used when present, especially for PDF.
Unified inspect_file.py / clean_file.py (auto-routes by format)
Directory
audit_dir.py for an aggregate report
Website / sitemap
audit_website.py
Text tools refuse binary input by default (magic-number + control-byte
detection) so a .docx/.pdf/image never gets mangled by decoding its
compressed bytes as text; pass --force-text to override, or use the
_file.py unified tools which route correctly on their own.
Findings are classified confirmed / probable / informational /
likely_false_positive. Summarize suspicious codepoints and C2PA/AI flags
before cleaning; do not silently overwrite the original.
Step 4: Deterministic clean (Layer A + file metadata)
python3 "$SCRIPTS/clean_text.py" INPUT -o OUTPUT --stats
python3 "$SCRIPTS/clean_file.py" INPUT -o OUTPUT # unified, any supported format
python3 "$SCRIPTS/inspect_file.py" OUTPUT # verify the result
Prefer writing *.cleaned.* outputs unless the user explicitly asked for
an in-place edit (--in-place is available but destructive).
Step 5: Always offer Layer B (statistical rewrite) for prose
After Layer A, propose — do not silently skip — a rewrite pass for
natural-language content to reduce a token-sampling watermark's signal:
print-prompt is the CI-safe default (no model call; it just emits the
rewrite prompt for the agent to run itself). Local Ollama or an
OpenAI-compatible backend can be wired via env vars:
Rules: API keys come from WATERMARKS_REWRITE_API_KEY only (never on
argv); non-loopback endpoints are refused unless --allow-remote or
WATERMARKS_REWRITE_ALLOW_REMOTE=1 is set explicitly; prefer a rewrite
model that is not the suspected origin vendor, to avoid re-stamping.
Always tell the user this step trades prose quality (voice/tone) for
hygiene — see the upstream README's "Disclaimer" section — and is
best-effort, never a certified bypass.
Only reach for these when the input is an image with a suspected
pixel-domain watermark (SynthID-class, StegaStamp, Tree-Ring,
StableSignature). Both are heavy, external, and not bundled in this repo:
Expect ~10 GB of model downloads and a strong GPU recommendation for
CtrlRegen; CPU runs are slow. clean_image.py --remove-pixel ctrlregen
also wires this into the unified image pipeline after a metadata strip.
CtrlRegen's backend ships no LICENSE file (treated as all-rights-reserved)
and is only ever loaded at runtime from the user's own checkout.
Step 7: Aggregate audits for a tree or a website
python3 "$SCRIPTS/audit_dir.py" DIR --json
python3 "$SCRIPTS/audit_website.py" --sitemap https://example.com/sitemap.xml --json
python3 "$SCRIPTS/audit_website.py" --base https://example.com --json # auto-discovers the sitemap
audit_website.py is stdlib-only and does not shell out to
c2patool/exiftool for remote URLs; download assets and run
audit_dir.py locally when that level of detail is needed.
Step 8: Use the wrapper for a read-only environment check
doctor only reports Python version and optional-tool availability
(c2patool, exiftool); it never writes to disk or installs packages.
Best practices
Inspect before you clean, and verify after — inspect_* first,
clean_* second, inspect_file.py on the output third.
Never overwrite silently — default to *.cleaned.* output paths;
only use --in-place on explicit user request.
Always offer Layer B, but be honest about its cost — statistical
watermark removal means rewording, and rewording degrades voice/tone;
surface that trade-off rather than silently rewriting production copy.
Prefer a non-origin rewrite model — rewriting Claude text with
Claude (or Gemini with Gemini) risks re-stamping the same watermark
class into the output.
Report confidence honestly — distinguish confirmed/probable
findings from informational/likely_false_positive noise, and never
claim an official vendor detector will fail.
Treat C2PA soft binding and pixel/audio/video marks as out of scope
for the deterministic path — only the optional external CtrlRegen
backend addresses pixel-domain image marks, and even then only as a
best-effort regenerating remover.
Keep heavy backends opt-in — don't run setup_synthid.sh /
setup_ctrlregen.sh unless the user actually needs pixel-domain
scoring/removal; they pull GBs of dependencies and models.
