banner-creator▌
resciencelab/opc-skills · updated Apr 8, 2026
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AI-powered banner creation with iterative design feedback and platform-specific cropping.
- ›Guides users through discovery phase to gather requirements (purpose, target ratio, style, content elements, colors) before generation
- ›Generates 20 banner variations at 21:9 ratio using the nanobanana skill, then crops to target formats (2:1 for GitHub, 3:1 for Twitter, 16:9 for websites)
- ›Includes HTML preview template for browsing variations and iterating based on user feedback
- ›Supports imag
Banner Creator Skill
Create professional banners through AI image generation with an iterative design process.
Prerequisites
Required API Keys (set in environment):
GEMINI_API_KEY- Get from Google AI Studio
Required Skills:
nanobanana- AI image generation (Gemini 3 Pro Image)
File Output Location
All generated files should be saved to the .skill-archive directory:
.skill-archive/banner-creator/<yyyy-mm-dd-summaryname>/
Example:
.skill-archive/banner-creator/2026-01-19-opc-banner/
banner-01.png
banner-02.png
...
banner-03-cropped.png
preview.html
Workflow
Step 1: Discovery & Requirements
Before generating, gather requirements from user:
Ask about:
-
Purpose - Where will the banner be used?
- GitHub README
- Twitter/X header
- LinkedIn banner
- Website hero
- YouTube channel art
-
Target ratio/size - See references/formats.md:
2:1(1280x640) - GitHub README3:1(1500x500) - Twitter header16:9(1920x1080) - Website hero
-
Style preference:
- Match existing logo/brand?
- Pixel art / 8-bit retro
- Minimalist / flat design
- Gradient / modern
- Illustrated / artistic
-
Content elements:
- Brand name / project name?
- Tagline / slogan?
- Logo character to include?
-
Color preferences:
- Existing brand colors?
- Let AI decide?
Wait for user confirmation before proceeding!
Step 2: Generate Banner Variations
Generate 20 banner variations using the nanobanana skill:
# Generate single banner
python3 <nanobanana_skill_dir>/scripts/generate.py "{style} banner for {brand}, {description}, {text elements}" \
--ratio 21:9 -o .skill-archive/banner-creator/<date-name>/banner-01.png
# Batch generate 20 banners
python3 <nanobanana_skill_dir>/scripts/batch_generate.py "{style} banner for {brand}, {description}, {text elements}" \
-n 20 --ratio 21:9 -d .skill-archive/banner-creator/<date-name> -p banner
Guidelines:
- Generate at
21:9ratio (widest available), crop later to target - Use batch_generate.py for multiple variations (includes auto-delay)
- Use sequential naming:
banner-01.png,banner-02.png, etc.
Image Editing (for incorporating existing logo):
python3 <nanobanana_skill_dir>/scripts/generate.py "add {logo character} to the left side of the banner" \
-i /path/to/existing-logo.png --ratio 21:9 -o banner-with-logo.png
Step 3: Create HTML Preview
Copy the preview template and open in browser:
cp <skill_dir>/templates/preview.html .skill-archive/banner-creator/<yyyy-mm-dd-summaryname>/preview.html
Then open in default browser:
open .skill-archive/banner-creator/<yyyy-mm-dd-summaryname>/preview.html
IMPORTANT: Update the HTML to include the correct number of banners generated.
Step 4: Iterate with User
Ask user which banners they prefer:
- "Which banners do you like? (e.g., #3, #7, #15)"
- "What do you like about them?"
- "Any changes you'd want?"
Based on feedback:
- Generate 10-20 more variations of favorite styles
- Use naming:
banner-{original}-v{n}.png(e.g.,banner-03-v1.png) - Update HTML preview
- Repeat until user selects final banner
Step 5: Crop to Target Ratio
Once user approves a banner, crop to target size:
python3 <skill_dir>/scripts/crop_banner.py {input.png} {output.png} --ratio 2:1 --width 1280
Common targets:
- GitHub README:
--ratio 2:1 --width 1280→ 1280x640 - Twitter header:
--ratio 3:1 --width 1500→ 1500x500 - Website hero:
--ratio 16:9 --width 1920→ 1920x1080
Step 6: Deliver Final Assets
Present final deliverables:
## Final Banner Assets
| File | Description | Size |
|------|-------------|------|
| banner-03.png | Original (21:9) | 2016x864 |
| banner-03-cropped.png | GitHub README (2:1) | 1280x640 |
All files saved to: `.skill-archive/banner-creator/<yyyy-mm-dd-summaryname>/`
Copy final banner to user's desired location.
Quick Reference
Common Prompt Patterns
With Text:
Wide banner for {brand}, {style} style, featuring "{text}" prominently displayed, {colors}, {scene/elements}
With Character:
Wide banner featuring {character description}, {style} style, {scene}, text "{brand name}" on {position}, {colors}
Abstract/Gradient:
Abstract {style} banner, {colors} gradient, geometric patterns, modern tech feel, text "{brand}" centered
Scene-based:
{Style} illustration banner, {scene description}, {character} in {action}, "{brand}" text overlay, {colors}
Supported Aspect Ratios
Generate at widest ratio, then crop:
21:9- Ultra-wide (recommended for generation)16:9- Wide3:2- Standard wide
References
- references/formats.md - Common banner sizes by platform
- examples/opc-banner-creation.md - Full example conversation
How to use banner-creator 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 banner-creator
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches banner-creator from GitHub repository resciencelab/opc-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 banner-creator. Access the skill through slash commands (e.g., /banner-creator) 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
4.7★★★★★42 reviews- ★★★★★Fatima Haddad· Dec 20, 2024
Registry listing for banner-creator matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Emma Huang· Dec 16, 2024
I recommend banner-creator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Shikha Mishra· Dec 12, 2024
Keeps context tight: banner-creator is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Luis Jain· Dec 12, 2024
Solid pick for teams standardizing on skills: banner-creator is focused, and the summary matches what you get after install.
- ★★★★★Emma Rao· Nov 11, 2024
Useful defaults in banner-creator — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Fatima Nasser· Nov 11, 2024
Keeps context tight: banner-creator is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Mateo Mensah· Nov 11, 2024
banner-creator has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Evelyn Ramirez· Nov 7, 2024
banner-creator reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chen Choi· Nov 3, 2024
We added banner-creator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Fatima Brown· Oct 26, 2024
Registry listing for banner-creator matched our evaluation — installs cleanly and behaves as described in the markdown.
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