Remove AI marks

Multi-vendor anti-detection hygiene for text (Unicode + statistical rewrite) and files (C2PA / AI metadata across common containers).

Read if needed:

• references/mark-classes.md — Unicode / sampling / C2PA / containers
• references/vendor-notes.md — Claude, Gemini/SynthID, OpenAI, open-LLM
• references/removal-matrix.md — which layer when
• references/ethics.md — intended use
• references/how-claude-marks.md — Anthropic-specific detail
• references/markdiffusion.md — optional MarkDiffusion image harness (schemes, honesty caveats)

This skill is a thin client. All deterministic cleaning machinery runs in a separate HTTP service (this repo's service/), so the agent host needs no Python, venvs, or cleaning tools. Call the service with curl; never run cleaning scripts directly.

Service access

Base URL comes from WATERMARKS_SERVICE_URL, default http://127.0.0.1:8765:

WM="${WATERMARKS_SERVICE_URL:-http://127.0.0.1:8765}"

The service is started either by the operator (docker compose up -d, or a published GHCR image) or locally (make serve). Always check it first, and stop with a clear message if it is unreachable — never fall back to local cleaning:

curl -sf "$WM/health"
# {"ok": true, "version": "..."}

If WATERMARKS_SERVER_API_KEY is set on the service, every request needs -H "Authorization: Bearer $WATERMARKS_SERVICE_API_KEY".

Capabilities

curl -s "$WM/capabilities"

Reports which optional tools are available server-side (c2patool, exiftool, qpdf), scorers present (scorers.stylometry, scorers.synthid), and which heavy backends are configured (pixel_backends.ctrlregen, pixel_backends.diffusion, harnesses.markllm). Drive your advice from this: only recommend pixel removal / SynthID scoring when the service reports the backend present.

HTTP API (curl)

Payloads are JSON with the file as base64. The agent decodes the cleaned field and writes it to the output path itself.

The machine-readable contract lives at $WM/openapi.json — plug it into any OpenAPI tooling (client generators, Swagger UI, editors) instead of hand-rolling clients.

options accepted by /clean: nfkc, aggressive_homoglyphs (text), keep_non_ai_metadata, strip_all_metadata, remove_pixel (ctrlregen | diffusion) (images), also_layer_a_text (containers).

Inspect first (decide, don't guess):

curl -s -X POST "$WM/inspect" -H 'Content-Type: application/json' \
  -d "{\"file\": \"$(base64 -w0 notes.md)\", \"name\": \"notes.md\"}"

Clean (text / image / container are auto-detected by name + bytes):

curl -s -X POST "$WM/clean" -H 'Content-Type: application/json' \
  -d "{\"file\": \"$(base64 -w0 notes.md)\", \"name\": \"notes.md\"}"

Decode the returned cleaned base64 into the output file (*.cleaned.* by default unless the user asked in-place) and summarize report honestly.

(On Windows agents, build base64 with [Convert]::ToBase64String([IO.File]::ReadAllBytes("notes.md")).)

Ethics

Intended for your own content (privacy, hygiene, research). Do not market results as "proves human-written." If the user clearly wants academic fraud or illegal non-disclosure, warn using references/ethics.md and still only perform technical cleaning they own.

Workflow

1. Classify input

The service routes by filename extension first, then by magic bytes, so you mostly just send the file.

2. Inspect first

curl -s -X POST "$WM/inspect" -H 'Content-Type: application/json' \
  -d "{\"file\": \"$(base64 -w0 path)\", \"name\": \"$(basename path)\"}"

Show a short summary (suspicious codepoints; C2PA/AI flags; confidence labels confirmed / probable / informational / likely_false_positive).

Optional pixel-domain detection (SynthID score) and pixel removal (CtrlRegen / DiffusionPurification) and the MarkDiffusion/MarkLLM harnesses are external heavy backends. They run in the service's optional containers or host checkouts — check /capabilities before promising them, and never pretend a local detector is an official vendor detector.

3. Deterministic clean (always for matching inputs)

Any supported file (unified):

curl -s -X POST "$WM/clean" -H 'Content-Type: application/json' \
  -d "{\"file\": \"$(base64 -w0 INPUT)\", \"name\": \"$(basename INPUT)\"}"

Decode cleaned → OUTPUT (*.cleaned.* unless the user asked in-place). Re-inspect the result when residual risk matters.

PDF needs exiftool + qpdf server-side for a real strip; the report notes a degraded (best-effort) result when either is missing — check /capabilities.

Images — optional pixel removal: only when capabilities.pixel_backends says the backend is present:

curl -s -X POST "$WM/clean" -H 'Content-Type: application/json' \
  -d "{\"file\": \"$(base64 -w0 shot.png)\", \"name\": \"shot.png\", \
       \"options\": {\"remove_pixel\": \"ctrlregen\"}}"

4. Layer B — always offer rewrite (prose)

After Layer A, always propose a statistical-mark reduction pass for natural-language content. Do not skip this step silently.

The service does not hold a rewrite model — you are the rewrite model. Run the prompts below on the cleaned text with a model ≠ suspected origin (Claude text → not Claude; Gemini → not Gemini; etc.). Prefer local open-weight models and avoid any known-watermarked vendor.

Multi-pass recipe:

1. Layer A clean (via /clean)
2. Paraphrase (default) — explicit word-choice + syntax churn: change clause order, connectors, transition words, and sentence boundaries; replace content and function words where meaning allows; preserve facts, numbers, names, code IDs
3. Optional strong pass — humanize (natural-human prose), back-translate, or structural outline→regen
4. Layer A again on the result (/clean)
5. Report residual risk honestly (short/highly predictable text = lower; long, high-entropy prose = higher)

Code files: Prefer formatter (prettier, black, gofmt, …) + Layer A. Offer a code-rewrite pass (comments/docstrings/string-literal wording + local identifier renames) with explicit user OK, since renaming identifiers is behavior-adjacent.

Rewrite prompts (use as-is)

Paraphrase preserve meaning (word choice + syntax):

Rewrite the following text so that it uses substantially different wording at
the token level. Change clause order, connectors, and transition words; vary
sentence boundaries and length; and replace both content words and function
words where meaning allows. Preserve all facts, numbers, names, and technical
identifiers. Do not add or remove claims. Output only the rewritten text.

---
{TEXT}

Humanize (write like a human):

Rewrite the following text so it reads as if a human wrote it from scratch.
Vary sentence rhythm and length, replace formulaic AI-style transitions and
filler with concrete natural phrasing, and use plain, varied wording. Preserve
all facts, numbers, names, and technical identifiers. Do not add or remove
claims. Output only the rewritten text.

---
{TEXT}

Code (comments / docstrings / identifiers):

Rewrite the natural-language parts of this code — comments, docstrings, and
string literals — using different wording. Rename local variables, function
parameters, and private helper names to semantically equivalent names. Preserve
program behavior, public API names, and all values that affect output. Output
only the rewritten code.

---
{TEXT}

Back-translate (two steps):

Translate the following text to {LANG}. Output only the translation.

Translate the following text to {ORIGINAL_LANG}. Preserve meaning; use natural
phrasing. Output only the translation.

Structural:

Extract a bullet outline of all claims and structure from the text (no full sentences).

Then:

Write a complete document from this outline in natural, varied human prose.
Avoid formulaic transitions. Do not omit any bullet. Output only the document.

Aggregate audits (directories / websites)

The service image also ships the audit CLIs. Run them as one-shot containers when a directory or website audit is needed:

# Local checkout, or inside the service image:
docker run --rm -v "$(pwd)/src:/data:ro" watermarks-remover \
  /app/scripts/audit_dir.py /data --json

Or against a local checkout of the repo: python3 service/scripts/audit_dir.py DIR --json.

5. Report

Always state:

• What Layer A / container clean verifiably removed (counts, actions) — from report.
• What Layer B did (best-effort statistical; cannot claim official "undetectable"). Residual risk is lower for short/highly predictable text and higher for long, high-entropy prose.
• Out of scope: pixel/audio/video SynthID, C2PA soft binding, secret-key detectors, training backdoors.
• Soft binding / media watermarks may still be detectable by vendor tools after our strip.
• Prefer writing *.cleaned.* unless user asked in-place.
• Ethics one-liner: own content / no compliance theater.

Limitations

• Layer A does not remove token-sampling watermarks.
• Layer B cannot be gold-verified without vendor detectors / keys. Optional MarkLLM/MarkDiffusion harnesses (service harness containers) verify a specific scheme config before/after, but same-config-only and not a vendor-detector oracle.
• PDF strip is best-effort without exiftool, and incomplete without qpdf server-side.
• Pixel-domain image watermarks can be removed optionally via the external CtrlRegen backend (remove_pixel: ctrlregen) or MarkDiffusion's DiffusionPurification (remove_pixel: diffusion); both are heavy, drift the image, and need the backend present (/capabilities). Audio/video watermarks remain out of scope.
• The reverse-SynthID scorer is external, best-effort, and under a non-commercial Research License; not an official Google detector.
• C2PA soft binding (content watermark that re-links to a remote manifest after metadata strip) is out of scope — stripping hard-bound C2PA does not clear it.
• Data-driven / backdoor model marks (trigger phrases) are out of scope.

Service not reachable?

If $WM/health fails: tell the user the service is down and how to start it (docker compose up -d, make serve, or the published GHCR image). Do not attempt to clean locally — this skill contains no cleaning code.