generate-image
by K-Dense-AIgenerate-image is a skill for generating or editing images with AI models like FLUX.2 Pro and Gemini 3.1 Flash Image Preview through OpenRouter. Use it for photos, illustrations, concept art, visual assets, and image edits when you want a repeatable workflow instead of a one-off prompt. For diagrams, flowcharts, and schematics, use scientific-schematics instead.
This skill scores 78/100, which means it is a solid listing candidate for directory users: it has a clear trigger, a concrete execution path, and enough workflow detail to install with reasonable confidence, though it is not yet fully self-contained. The score suggests users can expect practical image-generation leverage, but should still verify environment and model availability before adopting broadly.
- Clear use-case boundaries for image generation vs. scientific schematics, which reduces trigger ambiguity.
- Concrete quick-start with command examples showing both generation and edit flows.
- Substantial skill body with multiple workflow sections and repository/file references, indicating more than a placeholder guide.
- No install command or support files are included, so setup and integration may require manual effort.
- Compatibility depends on an OpenRouter API key, which may limit immediate use for some users.
Overview of generate-image skill
What generate-image is for
The generate-image skill is a practical way to generate or edit images with AI models such as FLUX.2 Pro and Gemini 3.1 Flash Image Preview through OpenRouter. It is best for users who need photos, illustrations, concept art, visual assets, or straightforward image edits, not technical diagrams.
Who should install it
Install the generate-image skill if you want a repeatable image workflow instead of writing a one-off prompt each time. It is a good fit for content creators, product teams, researchers making presentation visuals, and anyone who needs consistent image generation with less guesswork.
What it does better than a generic prompt
The main value of the generate-image skill is workflow clarity: it tells you when to use it, what to avoid, and how to drive the model with a usable input. The repo also points users toward the right file to start with and separates general image generation from scientific-schematics for diagram-like output.
How to Use generate-image skill
Install generate-image skill
Install with:
npx skills add K-Dense-AI/claude-scientific-skills --skill generate-image
This generate-image install assumes an OpenRouter API key is available. If the key is missing, the skill will not be useful until that account and environment setup is in place.
Start from the right files
Read SKILL.md first, then inspect the supporting paths the repository points to: README.md, AGENTS.md, metadata.json, and any rules/, resources/, references/, or scripts/ folders. In this repo, the visible implementation is centered in scientific-skills/generate-image/SKILL.md, and the quick-start script path mentioned there is scripts/generate_image.py.
Turn a rough idea into a usable prompt
Strong generate-image usage is specific about subject, style, and edits. Instead of “make an image of a startup,” use something like: “Create a clean product illustration of a SaaS dashboard on a laptop screen, dark background, blue accent lighting, realistic but polished, no text overlays.” For edits, say exactly what changes and what must stay unchanged: “Keep the subject and framing, but replace the sky with purple dusk light and warm the foreground colors.”
Practical workflow for better output
Use the repo’s quick-start pattern to separate generation from editing:
python scripts/generate_image.py "A beautiful sunset over mountains"
python scripts/generate_image.py "Make the sky purple" --input photo.jpg
That workflow matters because the skill is designed for image creation or modification, not abstract planning. If your target is a flowchart, circuit, or system diagram, switch to scientific-schematics instead of forcing generate-image to do the wrong job.
generate-image skill FAQ
Is generate-image good for technical diagrams?
No. The repository explicitly steers diagrams, schematics, pathways, and flowcharts to scientific-schematics. Use generate-image for visual content that benefits from aesthetics or general composition, not precise technical notation.
Do I need special experience to use generate-image?
No, but better inputs produce better results. Beginners can start with a short prompt and a simple edit instruction, while experienced users will get more value by specifying style, camera feel, composition, and what should remain fixed.
Is generate-image better than a normal prompt?
Usually yes when you want a repeatable install-and-run workflow, clearer model selection, and fewer decisions about how to structure the request. A generic prompt can work once; the generate-image skill is more useful when you want the same process to be reusable.
When should I not use generate-image?
Do not use it when the output must be exact, data-driven, or diagrammatic. If the image needs labels, precise relationships, or technical correctness, a schematic-oriented skill is the safer choice.
How to Improve generate-image skill
Give the model the details that change the image
The biggest quality gains usually come from subject, medium, composition, and constraints. A weak prompt says “make it modern”; a stronger one says “create a minimal editorial illustration of a city bike on a white background, side view, muted palette, no people, no labels.” That kind of input improves generate-image results because it removes ambiguity.
Separate creative direction from edit instructions
For the generate-image skill, edits work best when you say what to preserve and what to change. For example: “Keep the original subject and crop, soften the lighting, replace the background with a studio gradient, and remove any text.” This reduces accidental drift in identity, framing, or layout.
Watch for the common failure modes
The most common problems are overbroad prompts, missing style guidance, and asking the skill to do schematic work. If output quality is off, tighten the prompt before changing models: specify the visual goal, exclude unwanted elements, and decide whether the task is generation or editing.