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 stevenke1981/coart --skill coart-image-editgit clone --depth 1 https://github.com/stevenke1981/coartWrote 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/stevenke1981/coart/coart-image-edit)<a href="https://agentmods.dev/skills/stevenke1981/coart/coart-image-edit"><img src="https://agentmods.dev/badge/skills/stevenke1981/coart/coart-image-edit/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/stevenke1981/coart/coart-image-edit"><img src="https://agentmods.dev/badge/skills/stevenke1981/coart/coart-image-edit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00022 | $0.00293 |
| Opus 5 | $0.00011 | $0.00147 |
| Sonnet 5 | $0.00004 | $0.00059 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade A, and why
coart-image-edit 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 9d 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.
What it actually says
Coart Annotation Edit
- If the standalone editor was used, call
get_coart_pending_requestfirst when a pending request exists; do not ask the user to paste a prompt. If the user refers to an image just edited in Coart, callget_coart_latest_image; otherwise use the named annotation screenshot path inside the active project'scanvas/assets/directory. - Treat arrows, circles, freehand marks, and nearby text as revision instructions.
- Generate a clean bitmap without annotation marks, blue selection boxes, toolbars, or editor chrome.
- Preserve the requested subject, composition, and aspect ratio unless the annotations explicitly change them.
- If the user asks to update the existing image, call
update_coart_imagewith the exact generated image path and the selected image shape id. This preserves the shape's position, size, and id while keeping the previous asset protected. - For annotation-led revisions where the original must remain unchanged, call
insert_coart_imagewith the active project and place the result beside the selected source. Do not setreplaceHolder: trueunless the anchor is an actualai-imageholder. - After a queued standalone request is handled successfully, call
clear_coart_pending_requestwith its request id.
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.
- 9d ago First seen · 15 lines · 22 tokens per session scan A 9adbb489ef69
coart-image-edit is a skill published in the GitHub repository stevenke1981/coart (1 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 293 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
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