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 agentmods add skills/m-binimran/design-pack/image-generatenpx skills add m-binimran/design-pack --skill image-generategit clone --depth 1 https://github.com/m-binimran/design-packWrote 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/m-binimran/design-pack/image-generate)<a href="https://agentmods.dev/skills/m-binimran/design-pack/image-generate"><img src="https://agentmods.dev/badge/skills/m-binimran/design-pack/image-generate.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.00367 |
| Opus 5 | $0.00026 | $0.00183 |
| Sonnet 5 | $0.00011 | $0.00073 |
| Haiku 4.5 | $0.00005 | $0.00037 |
Grade A, and why
image-generate 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 5d 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
image-generate
Pick the model, write a real prompt, generate, curate, iterate. Record the license.
Pick the connector/model
- Nano Banana (Gemini): fast, multi-reference composition (up to 14), editing, up to 4K - great default.
- Higgsfield Soul / Flux / Seedream: photoreal, character consistency, cinematic looks.
- Adobe Firefly: when you need commercially-safe training + Adobe ecosystem.
(See
mcp/CONNECTORS.md. If none is connected, say so - don't fake an image.)
Prompt framework
Subject + action + setting + style + composition + lighting + colour + mood + camera/lens + quality. State aspect ratio and any reference images. Be specific; vague prompts give generic output.
Process
- Generate a small set (2-4 variations). 2. Curate the strongest. 3. Iterate the prompt (fix hands/text/ artifacts, adjust comp/colour). 4. Upscale / set final aspect ratio. 5. Record model + license.
Output
- The chosen image(s) from the connector (confirm they returned), the final prompt, and the model + license note.
Guardrails
- Don't describe an image as generated unless the connector returned it (truth-telling +
review-gate). - Record AI source + usage rights (the
license-guardhook). Curate and fix - don't ship raw first output.
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.
- 5d ago First seen · 30 lines · 53 tokens per session scan A 8089aa6b3081
image-generate is a skill published in the GitHub repository m-binimran/design-pack (3 stars, last pushed 2mo ago), licensed MIT. It adds 53 tokens to every session and 367 once invoked, about $0.0003 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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