generating-mock-images
Skill397 starsChanged 10 days agoApache-2.0View on GitHub
Generates mock flower and plant images for an e-commerce website, presenting them for user review. Use when generating mock product images for flower and plant stores or managing generated mock image assets.
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The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 26
When composing the Prompt argument for generate_image, integrate the scene, camera, and lighting parameters:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Generating Mock Images for Flower & Plant E-Commerce
Generates high-quality mock product photography for flower and plant e-commerce stores, displays the resulting image in chat for review, and handles user feedback (regenerate, delete, or save to workspace).
Workflow Steps
1. Synthesize Prompt & Call generate_image
CRITICAL TOOL RULE: You MUST execute the native
generate_imagetool to generate studio product images. Do NOT write Python scripts (e.g. PIL, matplotlib, byte packing), HTML/Canvas, SVG, or code to render images.
Default Pre-baked Subject Options:
- Option A (Houseplants): "Lush potted plant with healthy leaves in a minimalist ceramic pot"
- Option B (Floral Arrangements): "Elegant floral arrangement of fresh seasonal blooms in a clear glass vase filled with crystal-clear water"
Prompt Synthesis Strategy:
When composing the Prompt argument for generate_image, integrate the scene, camera, and lighting parameters:
"Professional studio product photograph of [SUBJECT]. [SCENE: smooth matte light gray surface, seamless background curve with soft infinite horizon, visible water line in vase]. [CAMERA: macro lens photography, sharp focus from front to back, f/11 aperture, crisp details, zero digital noise]. [LIGHTING: three-point soft-diffuse lighting, 48-inch octagonal softbox key light at 45 degrees, gentle fill light, backlit soft ambient glow, no harsh glare or unwanted reflections, premium e-commerce product catalog style]."
Invoke generate_image tool:
Prompt: Synthesized prompt combining Subject + Scene + Camera + Lighting specs.ImageName:mock_flower_plant(or uniquely named based on selection).AspectRatio: Default to"1:1". If the user explicitly specifies a different aspect ratio in their prompt or request (e.g.,"16:9","3:2","4:3","9:16"), use that specified aspect ratio instead.
2. Display Image in Chat
Upon receiving the output path from generate_image, render the image directly in the chat response using standard markdown image syntax:

3. Prompt User for Action (ask_question)
Invoke ask_question tool to ask the user how to handle the generated asset:
- Question: "What would you like to do with this generated mock image?"
- Options:
- "Keep"
- "Regenerate"
- "Exit"
4. Process User Choice
Action: Keep (<workspace>/public/images/)
- Ensure the destination directory exists at
<workspace>/public/images/. - Copy image file from
<GENERATED_IMAGE_PATH>to<workspace>/public/images/<image_filename>. - Display a confirmation message.
Action: Regenerate Image
- Re-run Step 1.
- Render the updated image in chat (Step 2).
- Re-prompt the user with
ask_question(Step 3).
Action: Exit
- Exit workflow without saving to workspace
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