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 krusemediallc/cursor-ad-agent --skill image-ad-clonegit clone --depth 1 https://github.com/krusemediallc/cursor-ad-agentWrote 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/krusemediallc/cursor-ad-agent/image-ad-clone)<a href="https://agentmods.dev/skills/krusemediallc/cursor-ad-agent/image-ad-clone"><img src="https://agentmods.dev/badge/skills/krusemediallc/cursor-ad-agent/image-ad-clone/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/krusemediallc/cursor-ad-agent/image-ad-clone"><img src="https://agentmods.dev/badge/skills/krusemediallc/cursor-ad-agent/image-ad-clone.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 | $0.00124 | $0.02402 |
| Opus 5.5 | $0.00050 | $0.00961 |
| Sonnet 5.5 | $0.00025 | $0.00480 |
| Haiku 4.5 | $0.00012 | $0.00240 |
Grade A, and why
image-ad-clone 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.
This is a copy
100% identical to image-ad-clone — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
image-ad-clone (Arcads)
Take an existing image ad and turn it into a reusable, parameterizable prompt template that gets appended to the shared 37-template image-ad library. The template is validated by round-tripping through one of the Arcads image-ad generators — ChatGPT Image 2 (typography / UI-mimicry templates) or Nano Banana (photoreal / lifestyle / multi-reference templates).
This skill replaces the older Uni1-locked image-ad-clone (which only worked with Luma uni-1). It's backend-agnostic: at Phase 1 the agent asks you (or auto-detects from the reference) whether to validate against gpt-image-2 or Nano Banana, then routes through the matching generator script in this repo.
Read order
- This file — Arcads-specific generator paths, model-choice decision, what's locked at the per-repo layer.
- shared/skills/image-ad-clone/prompting/guide.md — the full model-agnostic 10-phase workflow (visual analysis → draft prompt → generate-with-reference → iterate → generalize → test → cross-model validate → document → save).
- shared/skills/image-ad-prompting/prompting/template-format.md — entry skeleton.
- shared/skills/image-ad-prompting/prompting/prompt-library.md — destination for the new entry. 37 validated templates already there; new entries go at T40+.
Hard rules
Inherits all 6 hard rules from the shared guide (strip platform chrome, validate by generating, test the generalized version, no brand-specific text in the final template, never silently overwrite, document model notes for both backends). Plus per-repo:
- Backend is one of: ChatGPT Image 2 OR Nano Banana on Arcads. Never uni-1. The script choice happens in Phase 1 once the user picks (or the agent auto-detects).
Picking the right backend in Phase 1
Pick by what the reference ad is showing — most templates fall into one clear bucket.
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 · 133 lines · 124 tokens per session scan A ecc353bf8ba6
image-ad-clone is a skill published in the GitHub repository krusemediallc/cursor-ad-agent (11 stars, last pushed 3mo ago), licensed MIT. It adds 124 tokens to every session and 2,402 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to image-ad-clone, differing in 4 lines, and is treated as a copy.
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