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image-gen

Skill0 starsChanged 3 months agoMITView on GitHub

Image generation routing and prompt discipline — which provider for which job (gpt-image for text rendering and design fidelity, grok for fast iteration and photoreal people), how to structure prompts that survive the model (subject, composition, style, lighting, negatives), and the honest constraints of each provider installed on this machine. Use before generating any image so the first generation is closer to the brief.

49 other projects use a version of this.

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Projects with it also use spotify, trello and 10 more.

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The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 35 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Check what's installed before promising: the harness's generate_image tool for gpt-image.

    line 26

“Common” is measured: kept by at least 6 in 10 of the 35 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Image Gen

The first generation should be close. Provider choice and prompt structure decide that, not luck.

Provider routing

JobProviderWhy
Text in image (posters, UI mockups, signage)gpt-image (gpt-image-2)Best-in-class verbatim text rendering
Design fidelity, brand-adjacent workgpt-imageFollows structured art direction
Fast iteration, photoreal peoplegrok (via groken cloud)Speed, identity realism
Textures, tiles, spritesgpt-image low quality first, then highCheap drafts before the final render

Check what's installed before promising: the harness's generate_image tool for gpt-image; the groken bridge for grok. Never fake a provider that isn't there — say what's missing.

Prompt structure (the part people skip)

  1. Subject — one clear subject, named concretely ("a ceramic pour-over coffee dripper" not "coffee thing").
  2. Composition — where the subject sits, camera angle, framing ("centered, slight low angle, product shot on seamless").
  3. Style — the visual language ("editorial product photography", "flat vector illustration", "35mm film grain").
  4. Lighting — the single biggest quality lever ("soft key from upper left, subtle rim light", "overcast window light").
  5. Negatives — what to avoid ("no text, no watermark, no hands").
  6. Verbatim text — if text appears, put it in quotes exactly (the words "SALE ENDS FRIDAY") and keep it short.

Iteration discipline

Draft cheap (low quality), pick a direction, then render the final at high quality. Read the model's revised_prompt when available — it tells you what it actually heard. One strong revision beats five blind regenerations.

Pairs with

design (the taste standard the image is judged against), higgsfield-generate (heavier video/3D/audio generation when installed).

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