generate-image▌
davila7/claude-code-templates · updated Apr 8, 2026
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Generate and edit high-quality images using FLUX and Gemini AI models via OpenRouter.
- ›Supports both image generation from text prompts and editing of existing images with instruction-based modifications
- ›Two primary models available: Gemini 3 Pro (high quality, supports both generation and editing) and FLUX.2 Pro (fast, high quality, supports both modes); FLUX.2 Flex available for generation-only use cases
- ›Requires OpenRouter API key configured via .env file or environment variable; s
Generate Image
Generate and edit high-quality images using OpenRouter's image generation models including FLUX.2 Pro and Gemini 3 Pro.
When to Use This Skill
Use generate-image for:
- Photos and photorealistic images
- Artistic illustrations and artwork
- Concept art and visual concepts
- Visual assets for presentations or documents
- Image editing and modifications
- Any general-purpose image generation needs
Use scientific-schematics instead for:
- Flowcharts and process diagrams
- Circuit diagrams and electrical schematics
- Biological pathways and signaling cascades
- System architecture diagrams
- CONSORT diagrams and methodology flowcharts
- Any technical/schematic diagrams
Quick Start
Use the scripts/generate_image.py script to generate or edit images:
# Generate a new image
python scripts/generate_image.py "A beautiful sunset over mountains"
# Edit an existing image
python scripts/generate_image.py "Make the sky purple" --input photo.jpg
This generates/edits an image and saves it as generated_image.png in the current directory.
API Key Setup
CRITICAL: The script requires an OpenRouter API key. Before running, check if the user has configured their API key:
- Look for a
.envfile in the project directory or parent directories - Check for
OPENROUTER_API_KEY=<key>in the.envfile - If not found, inform the user they need to:
- Create a
.envfile withOPENROUTER_API_KEY=your-api-key-here - Or set the environment variable:
export OPENROUTER_API_KEY=your-api-key-here - Get an API key from: https://openrouter.ai/keys
- Create a
The script will automatically detect the .env file and provide clear error messages if the API key is missing.
Model Selection
Default model: google/gemini-3-pro-image-preview (high quality, recommended)
Available models for generation and editing:
google/gemini-3-pro-image-preview- High quality, supports generation + editingblack-forest-labs/flux.2-pro- Fast, high quality, supports generation + editing
Generation only:
black-forest-labs/flux.2-flex- Fast and cheap, but not as high quality as pro
Select based on:
- Quality: Use gemini-3-pro or flux.2-pro
- Editing: Use gemini-3-pro or flux.2-pro (both support image editing)
- Cost: Use flux.2-flex for generation only
Common Usage Patterns
Basic generation
python scripts/generate_image.py "Your prompt here"
Specify model
python scripts/generate_image.py "A cat in space" --model "black-forest-labs/flux.2-pro"
Custom output path
python scripts/generate_image.py "Abstract art" --output artwork.png
Edit an existing image
python scripts/generate_image.py "Make the background blue" --input photo.jpg
Edit with a specific model
python scripts/generate_image.py "Add sunglasses to the person" --input portrait.png --model "black-forest-labs/flux.2-pro"
Edit with custom output
python scripts/generate_image.py "Remove the text from the image" --input screenshot.png --output cleaned.png
Multiple images
Run the script multiple times with different prompts or output paths:
python scripts/generate_image.py "Image 1 description" --output image1.png
python scripts/generate_image.py "Image 2 description" --output image2.png
Script Parameters
prompt(required): Text description of the image to generate, or editing instructions--inputor-i: Input image path for editing (enables edit mode)--modelor-m: OpenRouter model ID (default: google/gemini-3-pro-image-preview)--outputor-o: Output file path (default: generated_image.png)--api-key: OpenRouter API key (overrides .env file)
Example Use Cases
For Scientific Documents
# Generate a conceptual illustration for a paper
python scripts/generate_image.py "Microscopic view of cancer cells being attacked by immunotherapy agents, scientific illustration style" --output figures/immunotherapy_concept.png
# Create a visual for a presentation
python scripts/generate_image.py "DNA double helix structure with highlighted mutation site, modern scientific visualization" --output slides/dna_mutation.png
For Presentations and Posters
# Title slide background
python scripts/generate_image.py "Abstract blue and white background with subtle molecular patterns, professional presentation style" --output slides/background.png
# Poster hero image
python scripts/generate_image.py "Laboratory setting with modern equipment, photorealistic, well-lit" --output poster/hero.png
For General Visual Content
# Website or documentation images
python scripts/generate_image.py "Professional team collaboration around a digital whiteboard, modern office" --output docs/team_collaboration.png
# Marketing materials
python scripts/generate_image.py "Futuristic AI brain concept with glowing neural networks" --output marketing/ai_concept.png
Error Handling
The script provides clear error messages for:
- Missing API key (with setup instructions)
- API errors (with status codes)
- Unexpected response formats
- Missing dependencies (requests library)
If the script fails, read the error message and address the issue before retrying.
Notes
- Images are returned as base64-encoded data URLs and automatically saved as PNG files
- The script supports both
imagesandcontentresponse formats from different OpenRouter models - Generation time varies by model (typically 5-30 seconds)
- For image editing, the input image is encoded as base64 and sent to the model
- Supported input image formats: PNG, JPEG, GIF, WebP
- Check OpenRouter pricing for cost information: https://openrouter.ai/models
Image Editing Tips
- Be specific about what changes you want (e.g., "change the sky to sunset colors" vs "edit the sky")
- Reference specific elements in the image when possible
- For best results, use clear and detailed editing instructions
- Both Gemini 3 Pro and FLUX.2 Pro support image editing through OpenRouter
Integration with Other Skills
- scientific-schematics: Use for technical diagrams, flowcharts, circuits, pathways
- generate-image: Use for photos, illustrations, artwork, visual concepts
- scientific-slides: Combine with generate-image for visually rich presentations
- latex-posters: Use generate-image for poster visuals and hero images
How to use generate-image 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 generate-image
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches generate-image from GitHub repository davila7/claude-code-templates 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 generate-image. Access the skill through slash commands (e.g., /generate-image) 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
4.6★★★★★34 reviews- ★★★★★Kabir Gonzalez· Dec 12, 2024
generate-image reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Arya Johnson· Dec 8, 2024
generate-image has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Isabella Kapoor· Dec 4, 2024
Registry listing for generate-image matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Daniel Huang· Nov 27, 2024
Solid pick for teams standardizing on skills: generate-image is focused, and the summary matches what you get after install.
- ★★★★★Rahul Santra· Nov 3, 2024
Useful defaults in generate-image — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Soo Harris· Nov 3, 2024
We added generate-image from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Pratham Ware· Oct 22, 2024
Registry listing for generate-image matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Omar Zhang· Oct 22, 2024
Keeps context tight: generate-image is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Amelia Huang· Oct 18, 2024
I recommend generate-image for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Sakshi Patil· Sep 25, 2024
Solid pick for teams standardizing on skills: generate-image is focused, and the summary matches what you get after install.
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