Agent skill

Prompt To Design

by hassancs91 in hassancs91/claude-image-generation

Generate a polished PNG graphic from a text prompt and an aspect ratio.

MITAuto-check passedMedia & Creative

Install Prompt To Design

skills CLI
$ npx skills add hassancs91/claude-image-generation --skill prompt-to-design -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hassancs91/claude-image-generation prompt-to-design --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hassancs91/claude-image-generation.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/level-1-image-generator .claude/skills/prompt-to-design && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
prompt-to-design
GitHub stars
102
Token cost
~3.2k tokens
SKILL.md length
1,357 words
Files
57
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

Generate a polished PNG graphic from a text prompt and an aspect ratio.

  • Works in 2 steps: Prompt — free text. Extract:… → Aspect ratio — a preset or WxH. If…
  • The user wants to create / generate / make an image
  • SKILL.md covers What it is (and is not), Inputs, The pipeline (follow in order) and Environment & how to run, plus 8 more sections
  • Calls pip and python3

What it does

Prompt To Design is an agent skill from hassancs91/claude-image-generation. Generate a polished PNG graphic from a text prompt and an aspect ratio. This is a CODE-BASED design generator (not a diffusion/photo model): it builds images from gradients, mesh fields, glow, grain, geometric shapes, and real typography. Use it whenever the user wants to create / generate / make an "image", poster, cover, wallpaper, banner, thumbnail, album art, story or reel cover, quote card, or any graphic from a description — optionally with words to render and a size or ratio (e.g. 1:1, 4:5, 9:16, 16:9…

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 57 other files (for example `README.md`).

It sits in Media & Creative, covering Image generation, Logo and visual identity and Excel spreadsheets. The repository describes itself as: Connect Claude to image generation with Agent Skills. Three levels: a zero-cost code-based design engine, a Three.js 3D renderer, and a real diffusion model on Cloudflare. Plus… The licence is MIT.

When your agent uses it

  • The user wants to create / generate / make an image
  • Any graphic from a description — optionally with words to render and a size

Example prompts

  • “/prompt-to-design”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Prompt — free text. Extract: subject/scene, mood, any color cues, and
  2. Aspect ratio — a preset or WxH. If unspecified, default 1:1.

What it can do on your machine

Read from SKILL.md and the folder at commit f533831. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Prompt To Design loads about 3.2k tokens when it runs. Until then it costs about 213 tokens; SKILL.md has 1,357 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~213
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hassancs91/claude-image-generation at commit f533831, republished under its MIT licence (© hassancs91). 1,357 words, ~3,208 tokens.

Download SKILL.mdSave it as .claude/skills/prompt-to-design/SKILL.md (or your agent's skills folder). This skill also uses 56 other files; get the full folder from GitHub.
name
prompt-to-design
description
Generate a polished PNG graphic from a text prompt and an aspect ratio. This is a CODE-BASED design generator (not a diffusion/photo model): it builds images from gradients, mesh fields, glow, grain, geometric shapes, and real typography. Use it whenever the user wants to create / generate / make an "image", poster, cover, wallpaper, banner, thumbnail, album art, story or reel cover, quote card, or any graphic from a description — optionally with words to render and a size or ratio (e.g. 1:1, 4:5, 9:16, 16:9, 2:3, 1:2, or WxH). It excels at designed, typographic, abstract, gradient, and geometric/Bauhaus/Swiss graphics and soft product-style visuals. It CANNOT produce photorealistic photos, real named people, brand logos, or copyrighted characters — for those, translate intent into a strong designed/stylized take instead.

prompt-to-design

Turn a free-form prompt + an aspect ratio into a finished PNG, built entirely from code. The house style is the level of the four pieces in reference/: posters, typographic covers, mesh/gradient atmospheres, geometric compositions, soft product abstractions. Frictionless like an image generator, but bounded to designed graphics.

What it is (and is not)

Everything is drawn with math and type — no image model. That means:

  • Great at: posters & album art, quote / story / reel / carousel covers, wallpapers, gradient & mesh backgrounds, neon / synthwave, Bauhaus / Swiss geometric art, minimalist logos-as-shapes, patterns, soft "product" orbs, anything typographic. Crisp text, perfect alignment, editable, no AI artifacts.
  • Cannot do: photorealism, a specific real person's face, detailed representational illustration (a recognizable animal, a fantasy castle), brand logos, or copyrighted characters.

Accept any prompt and always output a designed PNG. Silently route the prompt to the rendering approach that fits (see Style routing). Only if a prompt truly demands a photo (e.g. "photorealistic portrait of my friend") say so in one sentence and deliver the strongest stylized/typographic interpretation anyway — never refuse, never return a blob that ignores the prompt.

Inputs

  1. Prompt — free text. Extract: subject/scene, mood, any color cues, and any literal words to render (quotes, titles, handles), plus implied style.
  2. Aspect ratio — a preset or WxH. If unspecified, default 1:1.

The pipeline (follow in order)

  1. Read the prompt. Identify subject, mood, palette hints, literal text, ratio.
  2. Route to a style (one of: typographic, geometric, gradient-atmospheric, soft-minimal, poster-scene) — see below. When ambiguous, pick the one that best serves the words + mood.
  3. Choose a palette (3–6 colors, deliberate). See Palette.
  4. Plan the composition in your head first: the ONE focal element, the hierarchy, where the negative space lives. Balance mass with space.
  5. Assign fonts by role (display / grotesque / geometric / serif / serif_book / mono / pixel / techno). See Typography.
  6. Write a Python script that imports the engine and composes the piece (pattern below). Prefer the library primitives; drop to numpy only for custom effects.
  7. Render to a PNG in the outputs directory.
  8. VERIFY (mandatory gate — see Verify). Fix issues, re-render.
  9. Present the PNG. (Do not show the user the internal design reasoning or any .md notes — PNG only.)

Optionally, thinking through a one-paragraph "design philosophy" for the piece before coding measurably improves results — do it as internal reasoning, never as a user-facing file.

Environment & how to run

Requires Python with Pillow and numpy (install with pip install pillow numpy --break-system-packages if missing). Fonts are bundled in fonts/ — no system fonts needed.

Write a script that puts this skill's lib/ on the path, composes, and saves:

python
import sys
sys.path.insert(0, "SKILL_DIR/lib")     # the lib/ folder next to this SKILL.md
from render import Design, NEON, NEON_CYAN, CREAM, INK, CHARCOAL, lerp_stops

d = Design("9:16")                       # preset, "WxH", or (w, h)
# ... compose with d.<method>(...) ...
d.save("/mnt/user-data/outputs/design.png", grain=6, saturation=1.1)

Replace SKILL_DIR with the absolute path to this skill. Then run with python3 your_script.py and present the saved PNG.

Engine quick reference (render.Design)

Coordinates are fractions: x in [0,1] across width, y in [0,1] down height. Sizes / radii / widths / font sizes are in final pixels; the engine supersamples internally and downsamples for crisp edges.

Construct: Design(size="1:1", supersample=None, background=(255,255,255))

Backgrounds (set the whole canvas):

  • fill(color)
  • linear_gradient(stops, angle=90) — stops=[(pos,(r,g,b)),...]; 90=top→bottom, 0=left→right
  • radial_gradient(stops, center=(0.5,0.5), radius=0.9)
  • mesh_gradient(points) — points=[(fx,fy,(r,g,b),sigma_frac),...] smooth blended color fields
  • overlay_glow(center,color,radius,strength=0.4,mode="screen"|"add"|"blend") — soft glow onto current canvas
  • vignette(strength=0.4, center=(0.5,0.5), radius=0.75, power=1.6)

Shapes (optional glow={"color":(r,g,b)} for neon):

  • disk(cx,cy,r,color,glow=) · ellipse(cx,cy,rx,ry,color) · ring(cx,cy,r,width,color)
  • pie(cx,cy,r,a0,a1,color) — filled arc/sector; angles: 0=east, CW; top half=180→360, bottom=0→180
  • arc(cx,cy,r,a0,a1,width,color) — open stroke arc
  • rect(x0,y0,x1,y1,color,radius=0) — radius>0 for rounded/pills
  • line(x0,y0,x1,y1,width,color) · polygon([(x,y),...],color)
  • intersection(shapeA, shapeB, color) — knockout the overlap; shapes are ("disk",cx,cy,r), ("pie",cx,cy,r,a0,a1), ("rect",x0,y0,x1,y1)

Soft orb: gradient_sphere(cx,cy,r, colors, light=(-0.34,-0.34), shadow=True, specular=0.16, rim=0.05, glow=None) — colors = 2–3 stops blended diagonally.

Type:

  • write(x,y,text, role=, weight="regular"|"bold", italic=False, size=, color=, gradient=None, align="left"|"center"|"right", tracking=0, glow=None, shadow=None, stroke=0, stroke_color=None, return_width=False) — one line; y is the baseline. Pass gradient=[(pos,(r,g,b)),...] for chrome/metal fills; glow={"color":...} for neon; shadow=True or a dict for legibility on busy backgrounds.
  • text_block(x,y,text, ..., max_frac=0.84, line_height=1.16) — auto-wraps & stacks.
  • fit_size(text, target_frac, role=, weight=) → largest size that fits that width.
  • wrap(text, max_frac, ...) → list of lines. measure(text, ...) → (w,h) in final px.

Custom numpy: coords()→(xx,yy) · get_rgb() · set_rgb(arr) · composite_rgba(arr).

Finish: save(path, grain=0, chroma=0, scanlines=0, saturation=1.0, contrast=1.0, brightness=1.0) — post effects are applied after downsample (correct place for grain). Typical: light designs grain=2; rich/dark designs grain=5–6, chroma=2; add scanlines=0.03 only for CRT/retro looks.

Style routing

  • typographic — prompt centers on words/a quote/a title, or asks for a "quote card", "cover", "poster with text". Huge fitted type is the hero over a gradient or mesh. Emphasize one word (color or serif italic). (ref: quote_card.py)
  • geometric — "Bauhaus", "Swiss", "geometric", "shapes", "minimal poster", abstract mark. Flat color fields, circles/arcs/lines on a grid, knockouts, generous whitespace. (ref: geometric_bauhaus.py)
  • gradient-atmospheric — "gradient", "mesh", "aurora", "synthwave", "vaporwave", "neon", "dreamy", "abstract background", wallpaper. Mesh/linear fields + glow + grain; add shapes/type as needed. (ref: synthwave_poster.py)
  • soft-minimal — "soft", "Apple", "clean", "pastel", "premium", "carousel slide", "product". Light airy ground, a soft gradient_sphere or rounded card, refined type, lots of space. (ref: soft_carousel.py)
  • poster-scene — a simple scene ("sunset over mountains", "ocean horizon", "desert dunes"). Render as flat/geometric layers: gradient sky, a pie/disk sun, polygon mountains, layered bands — a stylized designed landscape, not a photo. Combine gradient + shapes.

Most prompts blend two (e.g. a synthwave poster is gradient-atmospheric + typographic). Compose accordingly.

Show full SKILL.md (528 more words)Show less

Palette

Pick 3–6 colors that carry a clear mood; restraint reads as premium.

  • Warm/energetic: vermilion, coral, gold, magenta.
  • Cool/calm: indigo, cobalt, teal, periwinkle.
  • Neon: hot magenta + cyan on near-black.
  • Soft/premium: cream/paper ground, muted pastels, one gentle accent.
  • Bauhaus: cream + primary red/blue/yellow + ink (+ optional teal). Use a light ground for airy/editorial; a dark or saturated ground for punchy/neon. Ensure text contrast (see Verify). Grain/dither prevents banding in smooth fields.

Typography

  • Match the face to the mood: display/display_chunky for huge poster words; grotesque/geometric for clean modern/Apple; serif/serif_display/serif_chic for editorial/elegant; serif_book italic for a graceful accent word; mono for kickers/handles/labels; pixel for retro-arcade; techno for sci-fi.
  • Make hero type genuinely large — use fit_size so the longest line fills the column. Break a phrase into short lines for tall formats.
  • Mix sparingly: one hero face + one label face; emphasize at most one word.
  • On busy/gradient grounds, give type a soft shadow or glow for legibility.
  • Add a small letter-spaced kicker and/or a small footer to sell "designed piece" — but keep them quiet.

Verify (mandatory before presenting)

After rendering, always check the result. Prefer viewing the PNG. If the image viewer is unavailable or returns nothing, fall back to measuring pixels (this is reliable and catches real bugs):

  • Legibility: sample the background luminance behind the text vs the text color — ensure strong contrast. Light text needs a dark-enough ground (and/or a shadow/glow); dark text needs a light ground.
  • Presence & placement: scan for the key colors (e.g. count pixels near the accent/headline color) and print their bounding box to confirm elements landed where intended and nothing was accidentally covered or knocked out.
  • Composition: downsample to a ~44-wide grid and print a coarse "map" (nearest-palette letters, or a luminance ramp) to see balance, coverage, and whitespace at a glance.
  • Banding: ensure grain/chroma is on for smooth gradients.

Example checks:

python
from PIL import Image; import numpy as np
a = np.array(Image.open(path).convert("RGB")).astype(int)
lum = 0.299*a[...,0]+0.587*a[...,1]+0.114*a[...,2]
band = lum[int(.3*a.shape[0]):int(.7*a.shape[0])]          # text band
print("bg lum behind text:", round(band[band<200].mean()))  # compare to text lum
mask = (((a-np.array(ACCENT))**2).sum(2) < 1500)            # find an element
ys,xs = np.where(mask); print("accent bbox:", xs.min(),xs.max(),ys.min(),ys.max())

Common bugs to look for (all seen in practice): a knockout intersection that swallows a whole shape because one shape sits fully inside the other; text/caption placed on same-color area so it's invisible; a hero element too small; a gradient that's so dark/muddy the colors don't read (lift it / boost saturation). Fix and re-render until it's clean.

Aspect ratios

PresetPixelsTypical use
1:11500×1500square post (default)
4:51080×1350IG portrait
9:161080×1920story / reel / phone wallpaper
16:91920×1080slide / desktop / YouTube
2:31200×1800poster portrait
3:21800×1200poster landscape
1:21080×2160tall poster
3:4, 4:3, 5:4, 2:1—also available

Or pass WxH (e.g. "1600x1000"), clamped to a 2560 long edge for speed.

Guardrails

No photorealistic depictions of real, named people; no reproductions of brand logos or trademarks; no copyrighted characters or existing artworks; nothing harmful. These are designed original graphics. When a prompt asks for one of these, deliver an abstract/typographic/stylized interpretation instead and say so briefly.

Reference examples

Study reference/ — each is a runnable, gold-standard build for a prompt type:

  • synthwave_poster.py — gradient sky, striped sun, neon grid, chrome title (1:2)
  • geometric_bauhaus.py — flat fields, arcs, knockouts, dot grid (1:1)
  • quote_card.py — mesh gradient + huge type + italic/gold accents (9:16)
  • soft_carousel.py — light ground, soft orb, refined type, page dots (1:1)

When a new prompt resembles one, start from its structure and adapt palette, composition, copy, and ratio.

© hassancs91, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 56 other files in .claude/skills/level-1-image-generator of hassancs91/claude-image-generation.

  • SKILL.md
  • README.md
  • fonts/BigShoulders-Bold.ttf
  • fonts/BigShoulders-OFL.txt
  • fonts/BigShoulders-Regular.ttf
  • fonts/Boldonse-OFL.txt
  • fonts/Boldonse-Regular.ttf
  • fonts/CrimsonPro-Bold.ttf
  • fonts/CrimsonPro-Italic.ttf
  • fonts/CrimsonPro-OFL.txt
  • fonts/CrimsonPro-Regular.ttf
  • fonts/DMMono-OFL.txt
  • fonts/DMMono-Regular.ttf
  • fonts/EricaOne-OFL.txt
  • fonts/EricaOne-Regular.ttf
  • fonts/Gloock-OFL.txt
  • fonts/Gloock-Regular.ttf
  • fonts/InstrumentSans-Bold.ttf
  • fonts/InstrumentSans-BoldItalic.ttf
  • fonts/InstrumentSans-Italic.ttf
  • … and 37 more

Open the folder on GitHubat commit f533831

Compare with similar skills

Prompt To Design next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Prompt To Design compared with similar skills
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Prompt To Design this skillhassancs91/claude-image-generation102—~3.2kAutomated safety check: PassMIT
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Nous Brandingmagnus919/agent-skills115—~4kAutomated safety check: PassMIT
Ideogramsocial-media-skills/skills134—~1.8kAutomated safety check: PassMIT
Anthropic Brand Stylinganthropics/skills180k30 repos~559Automated safety check: PassApache-2.0
CarouselsTheCraigHewitt/skills159—~2.3kAutomated safety check: PassMIT

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Questions about Prompt To Design

What does Prompt To Design do?

Generate a polished PNG graphic from a text prompt and an aspect ratio. Prompt To Design is an agent skill from hassancs91/claude-image-generation. Generate a polished PNG graphic from a text prompt and an aspect ratio.

When should I use Prompt To Design?

Prompt To Design fits situations like: the user wants to create / generate / make an image; any graphic from a description — optionally with words to render and a size.

How do I install Prompt To Design in Claude Code?

Run `npx skills add hassancs91/claude-image-generation --skill prompt-to-design -a claude-code`. Or copy the skill folder (.claude/skills/level-1-image-generator in hassancs91/claude-image-generation) into .claude/skills/prompt-to-design in your project. Claude Code loads it when a task matches its description.

How do I install Prompt To Design in Codex?

Run `npx skills add hassancs91/claude-image-generation --skill prompt-to-design -a codex`. Or copy the skill folder (.claude/skills/level-1-image-generator in hassancs91/claude-image-generation) into .agents/skills/prompt-to-design in your project. Codex loads it when a task matches its description.

Can I use Prompt To Design in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hassancs91/claude-image-generation --skill prompt-to-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-to-design, .gemini/skills/prompt-to-design, .github/skills/prompt-to-design and .opencode/skills/prompt-to-design in your project.

What does Prompt To Design need to run?

Going by SKILL.md and its folder, Prompt To Design needs the command-line tools its instructions call (pip and python3). Our summary lists: Python 3.

Does Prompt To Design access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Prompt To Design safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Prompt To Design use?

Prompt To Design is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prompt To Design use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Prompt To Design?

Skills that share tags, products or a category with Prompt To Design: Image Prompt Reverse (LunarXuan/image-prompt-reverse, 467 stars), Nous Branding (magnus919/agent-skills, 115 stars), Ideogram (social-media-skills/skills, 134 stars) and Anthropic Brand Styling (anthropics/skills, 180k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prompt To Design?

hassancs91 (a GitHub user) maintains it in hassancs91/claude-image-generation, which has 102 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on August 18, 2026.

Source: hassancs91/claude-image-generation on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.