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resciencelab avatar

Banner Creator

resciencelab/opc-skills

This handles the full loop of creating banners with AI image generation: you discuss what you need, it generates 20 variations at once using the nanobanana skill, shows you an HTML preview, then iterates based on which ones you like. Once you pick a winner, it crops to your target platform ratio (GitHub README 2:1, Twitter header 3:1, etc). The workflow is thorough, maybe even a bit prescriptive, but that's helpful when you're staring at a blank canvas. It saves everything to a dated archive folder and knows common banner sizes off the top of its head. Good for when you need a header image and don't want to fire up Figma.

installs
1.8K
GitHub stars
1.9K
last updated
Aug 21, 2026
View on GitHubSave

Install

$ npx -y skills add resciencelab/opc-skills --skill banner-creator --agent claude-code

Installs into .claude/skills of the current project.

Files

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:

  1. Purpose - Where will the banner be used?

    • GitHub README
    • Twitter/X header
    • LinkedIn banner
    • Website hero
    • YouTube channel art
  2. Target ratio/size - See references/formats.md:

    • 2:1 (1280x640) - GitHub README
    • 3:1 (1500x500) - Twitter header
    • 16:9 (1920x1080) - Website hero
  3. Style preference:

    • Match existing logo/brand?
    • Pixel art / 8-bit retro
    • Minimalist / flat design
    • Gradient / modern
    • Illustrated / artistic
  4. Content elements:

    • Brand name / project name?
    • Tagline / slogan?
    • Logo character to include?
  5. 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 21:9 ratio (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:

  1. Generate 10-20 more variations of favorite styles
  2. Use naming: banner-{original}-v{n}.png (e.g., banner-03-v1.png)
  3. Update HTML preview
  4. 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 - Wide
  • 3:2 - Standard wide

References

  • references/formats.md - Common banner sizes by platform
  • examples/opc-banner-creation.md - Full example conversation
SKILL.md on GitHub

Sponsors

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Details

Repository
github.com/resciencelab/opc-skills
Categories
Git & Pull Requests, Documentation, Code Review & Quality

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