remove-ai-marks: Data & Analytics for AI Agents | OpenAgentSkill

Overview

Scrub AI provenance marks from text and files using the markscrub CLI: invisible Unicode (Layer A), optional statistical rewrite (Layer B), and C2PA/EXIF/XMP/container metadata on PNG/JPEG/SVG/PDF/DOCX/HTML/MD. Use when the user asks to strip watermarks, remove Content Credentials, clean AI metadata, remove invisible Unicode, or run /remove-ai-marks.

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Remove AI marks (markscrub)

Privacy / provenance hygiene for content the user owns.

Do not claim results are “human-written,” “undetectable,” or that they pass a vendor detector. Separate verifiable cleans from best-effort rewrites in every summary.

Setup

Prefer the project-local CLI:

# from the markscrub repo
npm install
npx tsx src/cli.ts help
# or after build:
npm run build && node dist/cli.js help

If markscrub is on PATH, use that instead.

Workflow

1. Inspect first

markscrub inspect path/to/file --json

Summarize Unicode hits and metadata/C2PA hints briefly.

2. Deterministic clean (Layer A + files)

markscrub clean INPUT -o OUTPUT --json

Always do this for matching inputs. Re-inspect OUTPUT when useful.

PDF cleaning needs exiftool on PATH; otherwise report that PDF was left unchanged.

3. Offer rewrite (Layer B) for prose

After clean, always offer a statistical-mark reduction pass for natural-language text. Do not skip silently.

# Default: print a strong paraphrase prompt (no API key required)
markscrub rewrite INPUT --backend print-prompt

# Local presets
markscrub rewrite INPUT -o OUT.md --backend ollama --model llama3.2
markscrub rewrite INPUT -o OUT.md --backend lmstudio

# OpenAI-compatible cloud
export MARKSCRUB_REWRITE_API_KEY=...
markscrub rewrite INPUT -o OUT.md --backend openai --strength paraphrase

Model hygiene: Prefer a rewrite model different from the suspected origin model.

Also useful:

markscrub inspect DIR --recursive --fail-on-findings --sarif out.sarif
markscrub clean DIR -o OUTDIR --recursive --diff
printf 'text' | markscrub clean - -o cleaned.txt
./scripts/install-skill.sh cursor

Then run Layer A again on the rewrite result (markscrub clean).

4. Report residual risk honestly

Use the JSON report fields:

findings[].verifiable — true for Unicode/metadata actions
residualRisk — always mention sampling marks may remain

Ethics

Intended for the user’s own content (privacy, hygiene, research). If the user clearly wants academic fraud or illegal non-disclosure, warn and only perform technical cleaning on content they own.

References

references/mark-classes.md — Unicode / sampling / file provenance
references/ethics.md — intended use

View source ↗

File metadata

name: remove-ai-marks
description: >
  Scrub AI provenance marks from text and files using the markscrub CLI:
  invisible Unicode (Layer A), optional statistical rewrite (Layer B), and
  C2PA/EXIF/XMP/container metadata on PNG/JPEG/SVG/PDF/DOCX/HTML/MD.
  Use when the user asks to strip watermarks, remove Content Credentials,
  clean AI metadata, remove invisible Unicode, or run /remove-ai-marks.

View original text

---
name: remove-ai-marks
description: >
Scrub AI provenance marks from text and files using the markscrub CLI:
invisible Unicode (Layer A), optional statistical rewrite (Layer B), and
C2PA/EXIF/XMP/container metadata on PNG/JPEG/SVG/PDF/DOCX/HTML/MD.
Use when the user asks to strip watermarks, remove Content Credentials,
clean AI metadata, remove invisible Unicode, or run /remove-ai-marks.
---

# Remove AI marks (markscrub)

Privacy / provenance hygiene for **content the user owns**.

Do **not** claim results are “human-written,” “undetectable,” or that they
pass a vendor detector. Separate **verifiable** cleans from **best-effort**
rewrites in every summary.

## Setup

Prefer the project-local CLI:

```bash
# from the markscrub repo
npm install
npx tsx src/cli.ts help
# or after build:
npm run build && node dist/cli.js help
```

If `markscrub` is on PATH, use that instead.

## Workflow

### 1. Inspect first

```bash
markscrub inspect path/to/file --json
```

Summarize Unicode hits and metadata/C2PA hints briefly.

### 2. Deterministic clean (Layer A + files)

```bash
markscrub clean INPUT -o OUTPUT --json
```

Always do this for matching inputs. Re-inspect OUTPUT when useful.

PDF cleaning needs `exiftool` on PATH; otherwise report that PDF was left unchanged.

### 3. Offer rewrite (Layer B) for prose

After clean, **always offer** a statistical-mark reduction pass for natural-language text. Do not skip silently.

```bash
# Default: print a strong paraphrase prompt (no API key required)
markscrub rewrite INPUT --backend print-prompt

# Local presets
markscrub rewrite INPUT -o OUT.md --backend ollama --model llama3.2
markscrub rewrite INPUT -o OUT.md --backend lmstudio

# OpenAI-compatible cloud
export MARKSCRUB_REWRITE_API_KEY=...
markscrub rewrite INPUT -o OUT.md --backend openai --strength paraphrase
```

**Model hygiene:** Prefer a rewrite model **different from** the suspected origin model.

Also useful:

```bash
markscrub inspect DIR --recursive --fail-on-findings --sarif out.sarif
markscrub clean DIR -o OUTDIR --recursive --diff
printf 'text' | markscrub clean - -o cleaned.txt
./scripts/install-skill.sh cursor
```

Then run Layer A again on the rewrite result (`markscrub clean`).

### 4. Report residual risk honestly

Use the JSON report fields:

- `findings[].verifiable` — true for Unicode/metadata actions
- `residualRisk` — always mention sampling marks may remain

## Ethics

Intended for the user’s own content (privacy, hygiene, research). If the user clearly wants academic fraud or illegal non-disclosure, warn and only perform technical cleaning on content they own.

## References

- `references/mark-classes.md` — Unicode / sampling / file provenance
- `references/ethics.md` — intended use

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 66 GitHub stars
Stars/forks activity: 66 stars, 6 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Open full audit

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Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3

Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: anshaneja5/markscrub
License: MIT
Version: Unknown
Last GitHub push: Sep 2, 2026
Registry updated: Sep 9, 2026
Instruction path: skills/remove-ai-marks/SKILL.md @ 224160bea0d4

Version reported in registry metadata; check source releases before relying on it.

Quality

60/100

Promising

Trust

60/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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}

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Source: anshaneja5/markscrub
Indexed by: OpenAgentSkill community index

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