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"
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Added October 18, 2026
ai-agentspythongobashapici/cd
Works with
cli
api
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---
name: wicked-garden-product-imagery-create
description: |
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"
metadata:
role: module
phases: "clarify,design,review"
archetypes: "*"
---
# Image Creation
AI-powered image generation from text prompts. Supports multiple providers through a unified abstraction layer.
## Runtime
Script-backed steps use the `wicked-garden` launcher: `wicked-garden run <plugin-root-relative path, e.g. scripts/…> [args]`. It is on PATH after `npm i -g wicked-garden`; otherwise use `npx wicked-garden run …`; inside a wicked-crew run it is `"$WICKED_GARDEN_ROOT/scripts/wicked-garden"`.
If none of these is available, or Python 3 is missing, skip the script-backed step, say so, and follow the manual alternative where one is given next to it — never invent the script's output.
Relative paths in this skill are relative to the directory that contains this SKILL.md.
## 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
```bash
# Check which providers are available
wicked-garden run skills/product/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](refs/prompt-engineering.md).
### Step 3: Generate
```bash
# Basic generation (auto-detects best available provider)
wicked-garden run skills/product/imagery/scripts/provider.py generate \
--prompt "Your crafted prompt" \
--output ./output/v1.png
# Use a specific provider
wicked-garden run skills/product/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](refs/provider-reference.md)
- **Prompt engineering techniques**: [refs/prompt-engineering.md](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