mohamedabdallah-14/prompt-to-asset · Archived

illustration

Generate an in-product illustration (empty state, onboarding, hero, spot art) that obeys a brand bundle. Uses IP-Adapter / LoRA / Recraft style_id / Flux.2 brand refs for consistency across a set; validates palette ΔE2000 and style adherence.

First seen Jul 7, 2026

Installation

$ npx skills add mohamedabdallah-14/prompt-to-asset --skill illustration

Stronger alternatives

This repository is archived — consider an actively maintained alternative.

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from mohamedabdallah-14/prompt-to-asset · top by installs.

npx skills add mohamedabdallah-14/prompt-to-asset

Browse all from mohamedabdallah-14/prompt-to-asset

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 17
License LICENSE
Default branch main
Open issues 1
Status Archived

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,607 B
  • docs SUMMARY.md 263 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 6 installs

SKILL.md

Illustration generation

The consistency problem

A single illustration is easy. A set of twelve in the same style is the hard problem. Research 10 + 15: prompt words drift; reference images do not. Any illustration skill must inject a brand style reference at every call.

Routing

Model Brand lock mechanism Best for
Flux Pro / Flux.2 reference_images[] (up to 8 in Flux.2) + brand LoRA Photoreal, stylized 3D, brand illustration sets
SDXL + brand LoRA trained LoRA (6d recipe, ~5k steps on 20 images) Bespoke brand style, open-weight
Recraft V3 style_id (brand magic) Flat vector, editorial illustration
Ideogram 3 style codes Loose "same vibe" — not strict lock
Midjourney v6/v7 --sref / --cref / --mref Concept work; no API, community wrappers only
gpt-image-1 input_image[] Edit / composite flows

First illustration in a set is human-gated. Once approved, its style becomes the reference injected into all subsequent generations.

Brand bundle injection

illustration prompt =
  [SUBJECT + SCENE from brief]
+ [style anchor: "in the style of the provided reference images"]
+ [palette: exact hex list from brand]
+ [do_not list as positive anchors: "flat matte surfaces" not "no glossy plastic"]
+ [typography reminder: "no text, no labels"]
+ [technical constraints: aspect, resolution, composition]

+ reference_images[]: [style_ref_01.png, style_ref_02.png, (prior approved illustration).png]
+ LoRA handle / style_id / --sref

Prompt scaffold

An illustration of [SUBJECT: concrete noun phrase] in a [SCENE: clear action/context].
Composition: [centered | rule-of-thirds | off-center-left]. Subject occupies ~60% of frame.
Style: in the style of the provided reference images. Flat vector with soft gradients. 
Line weight consistent with references.
Palette strictly limited to: [#hex, #hex, #hex, #hex, #hex].
Materials: matte surfaces, soft ambient lighting, no rim lights, no lens flare.
No text, no labels, no UI elements.
[aspect ratio]. 2048x1280 resolution.

Drop quality modifiers on Flux (no masterpiece, 8k — hurts adherence). Keep them on SD.

Post-processing

  1. Background removal only if asset is spot art (use BiRefNet for soft edges).
  2. Palette validation: K-means 8-color in LAB, ΔE2000 against brand palette, regenerate if max ΔE > 10.
  3. Composition validation: VLM rubric check against style references.
  4. Resize to target sizes (sharp premultiplied-alpha resize).

Full-set propagation

Workflow for generating N illustrations:

  1. Generate illustration #1 with the brand bundle and the brief.
  2. Human gates: accept / regenerate / tweak.
  3. On accept: add illustration #1 to the style reference set.
  4. Subsequent illustrations pull the whole augmented reference set → style locks progressively tighter.
  5. After 3–4 accepted illustrations, train a LoRA for even tighter lock on 20+ asset sets.

Output

illustrations/
├── empty-state-projects.png
├── empty-state-tasks.png
├── onboarding-welcome.png
└── meta.json     # includes provenance + "set coherence score" across the batch