mikeparcewski/wicked-garden-product-imagery-create
skills/product/imagery/create/SKILL.md
AI-powered image generation from text prompts using multiple providers. Supports 5 providers: cstudio, vertex-curl, OpenAI, Stability AI, Replicate. Use when: "generate image", "create image", "text to image", "new visual"
$ install --global
skillsauthnpx skillsauth add mikeparcewski/wicked-garden wicked-garden-product-imagery-createInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- wicked-garden-product-imagery-create
- description:
- |
- Supports 5 providers:
- cstudio, vertex-curl, OpenAI, Stability AI, Replicate.
- Use when:
- generate image", "create image", "text to image", "new visual
- phase_relevance:
- ["clarify", "design", "review"]
- archetype_relevance:
- ["*"]
Image Creation
AI-powered image generation from text prompts. Supports multiple providers through a unified abstraction layer.
When To Use This Skill
- Generating new images from text descriptions
- Creating visual assets for marketing, UI, presentations
- Producing multiple variations of a concept
- Building visual prototypes from written specifications
Supported Providers
| Provider | Interface | Auth | Best For |
|----------|-----------|------|----------|
| cstudio | CLI binary | GOOGLE_CLOUD_PROJECT | Interactive use, rapid iteration |
| vertex-curl | gcloud + curl | gcloud auth | CI/CD, scripted pipelines |
| openai | REST API | OPENAI_API_KEY | Existing OpenAI subscription |
| stability | REST API | STABILITY_API_KEY | Stable Diffusion 3.5 |
| replicate | REST API | REPLICATE_API_TOKEN | Flux models, pay-per-use |
Provider Detection
# Check which providers are available
sh "${CLAUDE_PLUGIN_ROOT}/scripts/_python.sh" "${CLAUDE_PLUGIN_ROOT}/skills/imagery/scripts/provider.py" detect
The provider abstraction selects the best available provider automatically. A specific provider can be forced when needed.
Generation Workflow
1. UNDERSTAND — Clarify the visual intent and requirements
2. CRAFT — Build a high-quality prompt with style and constraints
3. GENERATE — Execute via the provider abstraction
4. REVIEW — Use the review sub-skill to validate output
5. REFINE — Iterate on prompt, parameters, or provider settings
Step 1: Understand Requirements
Before generating, gather:
- Subject: What is the image of?
- Style: Photorealistic, illustration, abstract, etc.
- Dimensions: Aspect ratio (16:9, 1:1, 9:16, etc.)
- Context: Where will this image be used?
- Constraints: Brand colors, no text, specific mood
Step 2: Craft the Prompt
Effective prompts follow this structure:
[Subject] + [Style/Medium] + [Lighting/Mood] + [Composition] + [Details]
Example: "A minimalist office workspace with a single monitor, soft natural side lighting, centered composition, matte finish, neutral color palette"
For detailed prompt engineering techniques, see refs/prompt-engineering.md.
Step 3: Generate
# Basic generation (auto-detects best available provider)
sh "${CLAUDE_PLUGIN_ROOT}/scripts/_python.sh" "${CLAUDE_PLUGIN_ROOT}/skills/imagery/scripts/provider.py" generate \
--prompt "Your crafted prompt" \
--output ./output/v1.png
# Use a specific provider
sh "${CLAUDE_PLUGIN_ROOT}/scripts/_python.sh" "${CLAUDE_PLUGIN_ROOT}/skills/imagery/scripts/provider.py" generate \
--prompt "Your crafted prompt" \
--provider openai \
--output ./output/v1.png
Key Parameters
| Parameter | Purpose | Example |
|-----------|---------|---------|
| --prompt | Text description | "A gothic garden at dusk" |
| --aspect-ratio | Output dimensions | "16:9", "1:1", "9:16" |
| --negative-prompt | Elements to exclude | "text, watermarks, blur" |
| --sample-count | Number of variations | 1-4 |
| --seed | Reproducible results | Any integer |
| --guidance-scale | Prompt adherence | Higher = more literal |
| --model | Model selection | "imagen-3.0-generate-001" |
Detailed References
- Provider APIs and configuration: refs/provider-reference.md
- Prompt engineering techniques: refs/prompt-engineering.md
Integration With Other Sub-Skills
- Use review after generation to validate quality and run gates
- If modifications are needed, hand off to alter for targeted edits
- Generation output feeds into the Analyze-Execute-Review-Refine loop
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