Generate illustration images for blog posts, documentation, and technical articles using Google Gemini's Nano Banana 2 (gemini-3.1-flash-image-preview) model.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionimage-generationExecute the skills CLI command in your project's root directory to begin installation:
Fetches image-generation from zc277584121/marketing-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate image-generation. Access via /image-generation in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Generate illustration images for blog posts, documentation, and technical articles using Google Gemini's Nano Banana 2 (gemini-3.1-flash-image-preview) model.
Prerequisite: GEMINI_API_KEY must be set in environment variables.
Before generating, clarify:
The script automatically prepends this style prefix:
Use a clean, modern color palette with soft tones. Minimalist flat illustration style with clear visual hierarchy. Professional and polished look suitable for technical blog articles. No photorealistic rendering. No excessive gradients or shadows.
Architecture diagram:
A system architecture diagram showing: User sends query to an API Gateway,
which routes to a Vector Database (labeled "Milvus") and an LLM service.
The Vector Database returns relevant documents, which are combined with the
original query and sent to the LLM for final response generation.
Arrows show data flow direction. Each component is a rounded rectangle with
an icon and label.
Concept illustration:
A visual comparison of keyword search vs semantic search. Left side shows
keyword search with exact word matching (highlighted matching words).
Right side shows semantic search with a brain icon understanding meaning
and connecting related concepts with dotted lines. A dividing line separates
the two approaches.
Run the script:
python ${CLAUDE_SKILL_ROOT}/scripts/generate_image.py \
--prompt "your prompt here" \
--output "/path/to/save/image.png"
| Parameter | Default | Notes |
|---|---|---|
| Model | gemini-3.1-flash-image-preview (Nano Banana 2) |
Fastest with good quality |
| Aspect ratio | 3:2 |
Landscape (width:height = 3:2), ideal for article illustrations |
| Image size | 1K |
Good balance of quality and cost |
| Style | Minimal, clean, soft tones | Auto-prepended to prompt |
| Language | English | Prompt and in-image text |
--model Model ID (gemini-3.1-flash-image-preview, gemini-3-pro-image-preview, gemini-2.5-flash-image)
--aspect-ratio 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9, etc.
--image-size 512, 1K, 2K, 4K
--style-prefix Custom style prefix (replaces the default)
--no-style Skip default style prefix entirely
| Scenario | Change |
|---|---|
| User wants higher quality | --image-size 2K |
| User wants best quality | --model gemini-3-pro-image-preview --image-size 2K |
| Social media banner | --aspect-ratio 16:9 |
| Portrait/vertical image | --aspect-ratio 3:4 or --aspect-ratio 9:16 |
| Square image (icon, avatar) | --aspect-ratio 1:1 |
| User has their own style | --style-prefix "your style" or --no-style |
| Non-English content | Write prompt in target language, no parameter change needed |
Follow this priority order to decide where to save the generated image:
If the user is working on a specific markdown file or article:
, save to the same images/ directory.
If no specific article context but working within a project:
images/, assets/, static/, img/, figures/images/ directory at the project root or under the relevant content directoryIf no clear project context:
concept-name-illustration.pngvector-search-architecture.pngrag-pipeline-overview.png over image1.pngAfter generating:
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
image-generation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend image-generation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: image-generation is focused, and the summary matches what you get after install.
Registry listing for image-generation matched our evaluation — installs cleanly and behaves as described in the markdown.
image-generation reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in image-generation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
image-generation has been reliable in day-to-day use. Documentation quality is above average for community skills.
image-generation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: image-generation is the kind of skill you can hand to a new teammate without a long onboarding doc.
image-generation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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