Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ShadowAqueduct/watermark-remover --skill remove-ai-marksgit clone --depth 1 https://github.com/ShadowAqueduct/watermark-removerWrote 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/shadowaqueduct/watermark-remover/remove-ai-marks)<a href="https://agentmods.dev/skills/shadowaqueduct/watermark-remover/remove-ai-marks"><img src="https://agentmods.dev/badge/skills/shadowaqueduct/watermark-remover/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/shadowaqueduct/watermark-remover/remove-ai-marks"><img src="https://agentmods.dev/badge/skills/shadowaqueduct/watermark-remover/remove-ai-marks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 8 findings, up to medium
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 →
- medium Data Exfiltration · line 75 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 112 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 82 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 134 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 145 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 125 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium MCP Rug Pull · line 234 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- low Tool Misuse · line 234 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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 | $0.00135 | $0.03468 |
| Opus 5 | $0.00068 | $0.01734 |
| Sonnet 5 | $0.00027 | $0.00694 |
| Haiku 4.5 | $0.00014 | $0.00347 |
Grade A, and why
remove-ai-marks scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
This skill is a **thin HTTP client**. All deterministic cleaning lives in this repo’s `service/`. The agent host does not need Python, venvs, or cleaner binaries. Call the service with `curl`. Never run cleaning scripts How it starts
The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remove AI marks
Hygiene for text (Unicode + statistical rewrite) and files (C2PA / AI metadata on common containers).
Read when needed:
references/mark-classes.md— Unicode / sampling / C2PA / containersreferences/vendor-notes.md— Claude, Gemini/SynthID, OpenAI, open-LLMreferences/removal-matrix.md— which layer whenreferences/ethics.md— intended usereferences/how-claude-marks.md— Anthropic-specific detailreferences/markdiffusion.md— optional MarkDiffusion image harness
This skill is a thin HTTP client. All deterministic cleaning lives in this repo’s service/. The agent host does not need Python, venvs, or cleaner binaries. Call the service with curl. Never run cleaning scripts on the host.
Reach the service
Base URL: WATERMARKS_SERVICE_URL, default http://127.0.0.1:8765.
WM="${WATERMARKS_SERVICE_URL:-http://127.0.0.1:8765}"
The operator starts it (docker compose up -d, a published image, or make serve). Probe it first. If it is down, stop with a clear message — do not 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"
Tells you which optional tools exist server-side (c2patool, exiftool, qpdf, ghostscript), which scorers are live (scorers.stylometry, scorers.synthid, scorers.synthid_http), which text detectors are wired (text_detectors.markllm, text_detectors.claude-text, text_detectors.gumbel), and which heavy backends are configured (pixel_backends.ctrlregen, pixel_backends.diffusion, harnesses.markllm). Only recommend pixel removal / SynthID scoring / vendor detection when capabilities say the backend is present.
HTTP API
Payloads are JSON; the file is base64. Decode the cleaned field yourself and write the output path.
What ships with it
6 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.
- 10d ago First seen · 268 lines · 135 tokens per session scan A f3cb9ca7b145
remove-ai-marks is a skill published in the GitHub repository ShadowAqueduct/watermark-remover (835 stars, last pushed 17d ago), licensed MIT. It adds 135 tokens to every session and 3,468 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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