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Generate an in-product illustration (empty state, onboarding, hero, spot art) that obeys a brand bundle.

Skill by MohamedAbdallah-14

IndexedMIT★ 24
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

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.

Use cases: design

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/illustration

What's inside

Skill name
illustration
Description
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.
License
MIT
Instructions
76 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# 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
```

From MohamedAbdallah-14/prompt-to-asset, MIT.

More from MohamedAbdallah-14/prompt-to-asset

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Stars
24
Forks
3
Open issues
3
Contributors
2
Last commit (this folder)
Apr 20, 2026 (6 months ago)
Last release
Apr 26, 2026 (6 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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illustration skill. Agent Mart. https://agentmart.dev/skills/mohamedabdallah-14-illustration (last updated 2026-10-24)