Indexé dans Registry

remove-ai-marks

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 Credential

Examiner la source Voir sur GitHub

Prix non confirmé★ 66 Stars GitHubRegistre mis à jour · 9 sept. 2026agent-skill

Vue d’ensemble

Lire la documentation complète

Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

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

Voir la source ↗

Métadonnées du fichier

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.

Voir le texte original

---
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

Examiner la source

Prix et coûts d’utilisation

Obtenir le skill: Prix non confirmé
L’utiliser: Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
Licence: MIT
Prix non confirmé: Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.

Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →

Source du skill enregistrée

Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.

Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
L’approbation de revue IA est absente
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

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

RépertoriéContrôle statique

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: anshaneja5/markscrub
Licence: MIT
Version: Unknown
Dernier push GitHub: 2 sept. 2026
Registre mis à jour: 9 sept. 2026
Chemin des instructions: skills/remove-ai-marks/SKILL.md @ 224160bea0d4

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

57/100

Prometteur

Confiance

58/100

Do not auto-install

Audit

70/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
L’approbation de revue IA est absente
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

Verified installs: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

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Plus de détails

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Créateur: anshaneja5
Source: anshaneja5/markscrub
Indexé par: Index communautaire OpenAgentSkill

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/anshaneja5-remove-ai-marks?metric=listed&label=Listed)](https://www.openagentskill.com/skills/anshaneja5-remove-ai-marks?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/anshaneja5-remove-ai-marks?metric=trust&label=Trust)](https://www.openagentskill.com/skills/anshaneja5-remove-ai-marks?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/anshaneja5-remove-ai-marks?metric=audit&label=Audit)](https://www.openagentskill.com/skills/anshaneja5-remove-ai-marks/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/anshaneja5-remove-ai-marks?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/anshaneja5-remove-ai-marks?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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