Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/anshaneja5/markscrubnpx agentmods add skills/anshaneja5/markscrub/remove-ai-marksWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/anshaneja5/markscrub/remove-ai-marks)<a href="https://agentmods.dev/skills/anshaneja5/markscrub/remove-ai-marks"><img src="https://agentmods.dev/badge/skills/anshaneja5/markscrub/remove-ai-marks/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/anshaneja5/markscrub/remove-ai-marks"><img src="https://agentmods.dev/badge/skills/anshaneja5/markscrub/remove-ai-marks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Excessive Agency · line 62 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
- medium MCP Rug Pull · line 26 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium System Prompt Leakage · line 58 Skill contains patterns that could indirectly extract system prompts through rephrasing, translation, summarization, or side-channel techniques.Fix: Guard against indirect extraction by refusing to summarize, translate, or rephrase system instructions. Add explicit anti-extraction clauses.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00091 | $0.00689 |
| Opus 5.5 | $0.00036 | $0.00276 |
| Sonnet 5.5 | $0.00018 | $0.00138 |
| Haiku 4.5 | $0.00009 | $0.00069 |
Grade A, and why
remove-ai-marks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 actionsresidualRisk— always mention sampling marks may remain
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 98 lines · 91 tokens per session scan A ffdeb85dc8f1
remove-ai-marks is a skill published in the GitHub repository anshaneja5/markscrub (66 stars, last pushed 1mo ago), licensed MIT. It adds 91 tokens to every session and 689 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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