Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sanky369/vibe-building-skillsnpx agentmods add skills/sanky369/vibe-building-skills/image-generationWrote 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/sanky369/vibe-building-skills/image-generation)<a href="https://agentmods.dev/skills/sanky369/vibe-building-skills/image-generation"><img src="https://agentmods.dev/badge/skills/sanky369/vibe-building-skills/image-generation/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/sanky369/vibe-building-skills/image-generation"><img src="https://agentmods.dev/badge/skills/sanky369/vibe-building-skills/image-generation.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 | $0.00162 | $0.01853 |
| Opus 5 | $0.00081 | $0.00927 |
| Sonnet 5 | $0.00032 | $0.00371 |
| Haiku 4.5 | $0.00016 | $0.00185 |
Grade A, and why
image-generation 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation
Convert a fuzzy image request into prompt specs precise enough that any competent
image model — and specifically fal-ai/nano-banana-pro on FAL.ai, the one model
this repo's automation uses — renders what the user actually pictured. The prompt
spec is the deliverable; generated files are the bonus when the automation is
configured. Prime rule: never generate from a vague prompt. A weak prompt
costs a generation credit and teaches you nothing; a sharp spec is reusable
forever.
When to use / when not to
Use for general-purpose images: hero images, illustrations, thumbnails, infographics, textures, concept art, any one-off visual. Hand off instead when the request is really:
- Product shots for commerce/marketing →
skills/creative/product-photography - Platform-sized social content →
skills/creative/social-graphics - Logos, icons, patterns, brand marks →
skills/creative/brand-asset - No visual direction exists yet and consistency matters → run
skills/creative/creative-strategistfirst and consume its style block - A whole multi-asset campaign →
skills/creative/orchestratorroutes it
Intake
Ask in one batch, only what's missing:
- Subject — what exactly is in the image? (object, scene, people, text?)
- Use — where does it go? (this decides aspect ratio and resolution)
- Style anchor — existing style guide / creative-strategist style block, reference images, or 3 adjectives for the mood?
- Automation — is
FAL_API_KEYset so you can generate, or deliver specs only?
Infer, don't ask: aspect ratio from the stated destination (thumbnail → 16:9,
story → 9:16, feed → 1:1 or 4:5); resolution from stakes (2K default,
4K hero/print, 1K drafts). Don't stall: if subject and use are clear,
state your assumptions for the rest and proceed.
Workflow
- Check for a style block. If the user has run
skills/creative/creative-strategist, reuse its style block verbatim in every prompt. If not, and this is a one-off, derive a minimal one (style + palette + mood) from context and label it an assumption. - Draft the prompt with the 5-part formula. Every prompt contains, in order: subject (the main thing, specific: "a luxury leather watch with gold accents", never "a watch") → descriptive detail (materials, colors, text to render) → style ("professional product photography", "flat modern illustration", "photorealistic") → technical treatment (lighting, composition, focus, "4K, highly detailed") → mood ("warm and inviting"). Add negative constraints as plain prohibitions ("no text overlay", "no watermark", "no busy background").
- Decide variant strategy. If it's a throwaway draft → 1 image at
1K. If it's a keeper asset → propose 3–4 genuinely distinct prompt specs (different composition or style angle, not synonym swaps), generate 1 of each ornum_images3–4 of the winner, and have the user pick. Never present one option for a hero asset. - Generate or deliver. If
FAL_API_KEY(orFAL_KEY) is set:
What ships with it
1 file 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.
- 12d ago First seen · 135 lines · 162 tokens per session scan A 3a940ae957b4
image-generation is a skill published in the GitHub repository sanky369/vibe-building-skills (30 stars, last pushed 2mo ago), licensed MIT. It adds 162 tokens to every session and 1,853 once invoked, about $0.0008 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 2026-08-30.
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