Visual asset lifecycle management — analysis, generation, modification, and review. Three sub-skills cover the full creative pipeline with provider abstraction. Use when: generating, editing, analyzing, or reviewing an image — routes to the appropriate sub-skill (create / alter / review).
Resources
4Install
npx skillscat add mikeparcewski/wicked-garden/wicked-garden-product-imagery Install via the SkillsCat registry.
Imagery: Visual Asset Lifecycle
Skill-local scripts (scripts/provider.py, scripts/*.sh) are stdlib-only and run base-directory-relative with python3 (py -3 on Windows).
Manage the full lifecycle of visual assets through three specialized sub-skills. Each sub-skill handles a distinct phase of creative work, from analysis through generation to iterative refinement.
Sub-Skills
| Sub-Skill | Purpose | Provider Required |
|---|---|---|
| **review** | Image analysis and quality review | None (reads image files directly) |
| **create** | Text-to-image generation | Any provider (see below) |
| **alter** | Image modification (img2img, inpainting) | Any provider (see below) |
When To Use Each
- "What is in this image?" — review
- "Generate a new image of..." — create
- "Change this image to..." — alter
- "Is this image ready for production?" — review (quality gates)
- "Make the sky more dramatic" — alter (img2img or inpainting)
The Creative Loop: Analyze-Execute-Review-Refine
Every visual task follows this iterative cycle until the output matches requirements:
┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐
│ ANALYZE │────>│ EXECUTE │────>│ REVIEW │────>│ REFINE │
│ (review)│ │(create/ │ │(review) │ │ (alter) │
│ │ │ alter) │ │ │ │ │
└─────────┘ └─────────┘ └─────────┘ └────┬────┘
^ │
└────────────────────────────────────────────────┘- Analyze — Understand the visual context, reference images, or requirements (review sub-skill)
- Execute — Generate new assets (create) or modify existing ones (alter)
- Review — Run quality gates: brand, accessibility, production, sensitivity (review sub-skill)
- Refine — Adjust prompts, parameters, or masks based on review findings (alter sub-skill)
Provider Status
The imagery skill uses a provider abstraction layer. Review works without any provider. Create and alter require at least one:
# Check available providers
python3 scripts/provider.py detect| Provider | How to Enable |
|---|---|
| cstudio | Install CLI binary, set GOOGLE_CLOUD_PROJECT |
| vertex-curl | gcloud auth login, set GOOGLE_CLOUD_PROJECT |
| openai | Set OPENAI_API_KEY |
| stability | Set STABILITY_API_KEY |
| replicate | Set REPLICATE_API_TOKEN |
Quick Start
Analyze an existing image
Read the image file → apply review sub-skill analysis lensesGenerate a new image
Describe requirements → create sub-skill generates → review sub-skill validatesModify an existing image
Review identifies issues → alter sub-skill applies changes → review validatesReference Map
Review References
review/refs/analysis-lenses.md— Four analysis lenses (general, technical, style, layout)review/refs/quality-gates.md— Four quality gates (brand, a11y, production, sensitivity)
Create References
create/refs/provider-reference.md— Provider APIs and configurationcreate/refs/prompt-engineering.md— Crafting effective prompts
Alter References
alter/refs/editing-reference.md— Modification patterns, provider commands, refinement strategies