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.
git clone --depth 1 https://github.com/Cleveland-RD/claude-image-genWrote 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/commands/cleveland-rd/claude-image-gen/image-gpt)<a href="https://agentmods.dev/commands/cleveland-rd/claude-image-gen/image-gpt"><img src="https://agentmods.dev/badge/commands/cleveland-rd/claude-image-gen/image-gpt/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/commands/cleveland-rd/claude-image-gen/image-gpt"><img src="https://agentmods.dev/badge/commands/cleveland-rd/claude-image-gen/image-gpt.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 | $0.00018 | $0.00099 |
| Opus 5 | $0.00009 | $0.00049 |
| Sonnet 5 | $0.00004 | $0.00020 |
| Haiku 4.5 | $0.00002 | $0.00010 |
Grade A, and why
image-gpt 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 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.
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
The user has chosen GPT (gpt-image-2) as the image model. Follow the
image-gen skill end-to-end with model: "gpt" on every call to
generate_image. Do not ask the user which model to use — they've
already chosen by invoking /image-gpt.
The user's request: $ARGUMENTS
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 · 11 lines · 18 tokens per session scan A b59896dab942
image-gpt is a command published in the GitHub repository Cleveland-RD/claude-image-gen (1 stars, last pushed 3mo ago), licensed MIT. It adds 18 tokens to every session and 99 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.
Other commands, from other repositories
rotate-creative
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edit-meta-creative
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negative-keyword-sweep
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launch-meta-campaign
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launch-pmax
Launch a Google Ads Performance Max campaign with a complete asset group, audience signals, and (for ecom) listing groups. Built paused for review. Use when the user wants a new PMax campaign.