image-generation▌
zc277584121/marketing-skills · updated Apr 8, 2026
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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.
Image Generation Skill
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.
When to Use
- User asks to generate an illustration, diagram, or concept image
- User is writing an article and needs visual explanations for concepts or workflows
- User explicitly asks for AI-generated images
Step 1: Determine the Image Requirements
Before generating, clarify:
- What to illustrate — the concept, architecture, flow, or scene
- Language — default to English for both prompt and text in image. Only use other languages if the user explicitly requests it
- Save location — see "Output Path" section below
- Style/color preferences — if user has specific needs, otherwise use defaults
Step 2: Craft the Prompt
Default Style Prefix (always prepended unless user overrides)
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.
Prompt Writing Guidelines
- Be specific and descriptive about the visual elements
- For technical concepts: describe the components, their relationships, and layout
- For architecture diagrams: list the layers/components and how they connect
- For flow diagrams: describe the steps and direction of flow
- If text labels are needed in the image, spell them out explicitly in the prompt
- Default language is English. Only write the prompt and request in-image text in another language if the user specifically asks
Example Prompts
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.
Step 3: Generate the Image
Run the script:
python ${CLAUDE_SKILL_ROOT}/scripts/generate_image.py \
--prompt "your prompt here" \
--output "/path/to/save/image.png"
Default Parameters
| 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 |
Available Options
--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
When to Change Defaults
| 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 |
Step 4: Determine Output Path
Follow this priority order to decide where to save the generated image:
Priority 1: Context from Current Conversation
If the user is working on a specific markdown file or article:
- Check where existing images in that article are stored (look for
, save to the same images/ directory.
Priority 2: Project Image Directory
If no specific article context but working within a project:
- Look for existing image directories:
images/,assets/,static/,img/,figures/ - Save in the most appropriate existing directory
- If none exists, create an
images/directory at the project root or under the relevant content directory
Priority 3: Fallback
If no clear project context:
- Save to the current working directory
- Use a descriptive filename:
concept-name-illustration.png
Filename Convention
- Use lowercase with hyphens:
vector-search-architecture.png - Be descriptive: prefer
rag-pipeline-overview.pngoverimage1.png - Match existing naming patterns in the project if any
Step 5: Verify the Result
After generating:
- Read the image file to visually verify it matches the user's request
- If the result is not satisfactory, refine the prompt and regenerate
- If the image will be inserted into a markdown file, suggest the markdown syntax:

How to use image-generation on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add image-generation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches image-generation from GitHub repository zc277584121/marketing-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate image-generation. Access the skill through slash commands (e.g., /image-generation) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★32 reviews- ★★★★★Arya Bansal· Dec 28, 2024
image-generation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Pratham Ware· Dec 16, 2024
I recommend image-generation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Lucas Liu· Dec 16, 2024
Solid pick for teams standardizing on skills: image-generation is focused, and the summary matches what you get after install.
- ★★★★★Yusuf Chen· Nov 27, 2024
Registry listing for image-generation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Aisha Khanna· Nov 19, 2024
image-generation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yash Thakker· Nov 7, 2024
Useful defaults in image-generation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Isabella Lopez· Nov 7, 2024
image-generation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Dhruvi Jain· Oct 26, 2024
image-generation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Zara Ndlovu· Oct 26, 2024
Keeps context tight: image-generation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Fatima Gupta· Oct 18, 2024
image-generation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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