Claude-skill-registry create-image

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/create-image" ~/.claude/skills/majiayu000-claude-skill-registry-create-image && rm -rf "$T"

manifest: skills/data/create-image/SKILL.md

create-image

Generate images using FREE AI image generation backends.

Features

Ollama (local) - Z-Image Turbo or FLUX2-Klein via Ollama (FREE, no internet)
FLUX.1-schnell - AI-generated images via HuggingFace (FREE remote)
Mermaid diagrams - Flowcharts and architecture diagrams (FREE)
Placeholder images - Random grayscale from picsum.photos (FREE)
Solid color - Gray box with text label (always works)
Size control - Specify dimensions for PDF embedding (auto-resized)

Quick Start

cd .pi/skills/create-image

# Generate an AI image (uses FLUX.1-schnell)
uv run --script generate.py "hardware verification flowchart for microprocessor" \
  --output test_figure.png \
  --size 400x600

# Generate with specific backend
uv run --script generate.py "network security architecture" \
  --output security_arch.png \
  --size 800x600 \
  --backend flux

# Use placeholder fallback
uv run --script generate.py "placeholder" \
  --output placeholder.png \
  --size 400x300 \
  --backend placeholder

Commands

generate

- Create an image

uv run --script generate.py "<prompt>" [options]

Arguments:

Argument	Description
`prompt`	Description of the image to generate

Options:

Option	Short	Description	Default
`--output`	`-o`	Output file path	`fixture_image.png`
`--size`	`-s`	Image dimensions (WxH)	`512x512`
`--backend`	`-b`	Generation backend	`auto`

Backends

Backend	Description	Requires	Cost
`ollama`	Z-Image/FLUX2 local generation	Ollama + model	FREE (local)
`flux`	FLUX.1-schnell AI generation	`HF_TOKEN`	FREE (remote)
`mermaid`	Flowchart/diagram generation	`mmdc` CLI	FREE
`placeholder`	picsum.photos (grayscale)	Nothing	FREE
`solid`	Gray box with text label	Pillow	FREE
`auto`	Try backends in order	Any available	-

Setup

Option 1: Ollama (macOS only - MLX framework)

Note: Ollama image generation currently only works on macOS (Apple Silicon). Linux/NVIDIA support is "coming soon" per Ollama docs.

# macOS only
ollama pull x/z-image-turbo
# or
ollama pull x/flux2-klein

Option 2: HuggingFace Token (FREE Remote)

Get a FREE HuggingFace token from huggingface.co/settings/tokens:

export HF_TOKEN="hf_your_token_here"

Option 3: Mermaid (for Diagrams)

npm install -g @mermaid-js/mermaid-cli

Example Prompts

Security Documents

"APT attack kill chain diagram with reconnaissance, weaponization, delivery, exploitation phases"
"network intrusion detection system architecture"
"malware analysis workflow flowchart"

Engineering Documents

"hardware verification flow for microprocessor with RTL, synthesis, and timing analysis"
"FPGA design pipeline from HDL to bitstream"
"embedded systems boot sequence diagram"

Scientific Documents

"machine learning pipeline with data preprocessing, training, and inference stages"
"experimental methodology flowchart"
"system architecture diagram with numbered components"

Cached Images (Reuse Before Generating)

Pre-generated images are available in

cached_images/

- use these first to avoid unnecessary API calls:

File	Description	Size
`decorative.png`	Abstract cover/decorative illustration	512x512
`flowchart.png`	Technical workflow/process diagram	512x512
`network_arch.png`	Network/system architecture diagram	512x512

# Copy cached image instead of generating
cp cached_images/flowchart.png /path/to/output.png

Integration with PDF Generation

After generating images, embed them in PDFs:

import fitz  # PyMuPDF

doc = fitz.open()
page = doc.new_page()

# Insert generated image
img_rect = fitz.Rect(50, 200, 450, 500)  # x0, y0, x1, y1
page.insert_image(img_rect, filename="test_figure.png")

doc.save("fixture_with_figure.pdf")

Dependencies

dependencies = [
    "huggingface_hub>=0.26.0",
    "httpx",
    "typer",
    "pillow",
]