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nanobanana▌

resciencelab/opc-skills · updated Apr 8, 2026

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$npx skills add https://github.com/resciencelab/opc-skills --skill nanobanana
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summary

Text-to-image and image editing powered by Google's Gemini 3 Pro Image model.

  • ›Supports text-to-image generation, image editing with natural language prompts, and batch generation of multiple variations
  • ›Offers flexible aspect ratios (1:1, 16:9, 21:9, etc.) and high-resolution output up to 4K for enhanced detail
  • ›Includes optional Google Search grounding for factually accurate images of real people, places, and landmarks
  • ›Provides both command-line scripts and Python API for direct in
skill.md

Nano Banana - AI Image Generation

Generate and edit images using Google's Gemini 3 Pro Image model (gemini-3-pro-image-preview, nicknamed "Nano Banana Pro" 🍌).

Prerequisites

Required:

  • GEMINI_API_KEY - Get from Google AI Studio
  • Python 3.10+ with google-genai package

Install dependencies:

pip install google-genai pillow

Quick Start

Generate an image:

python3 <skill_dir>/scripts/generate.py "a cute robot mascot, pixel art style" -o robot.png

Edit an existing image:

python3 <skill_dir>/scripts/generate.py "make the background blue" -i input.jpg -o output.png

Generate with specific aspect ratio:

python3 <skill_dir>/scripts/generate.py "cinematic landscape" --ratio 21:9 -o landscape.png

Generate high-resolution 4K image:

python3 <skill_dir>/scripts/generate.py "professional product photo" --size 4K -o product.png

Script Reference

scripts/generate.py

Main image generation script.

Usage: generate.py [OPTIONS] PROMPT

Arguments:
  PROMPT              Text prompt for image generation

Options:
  -o, --output PATH   Output file path (default: auto-generated)
  -i, --input PATH    Input image for editing (optional)
  -r, --ratio RATIO   Aspect ratio (1:1, 16:9, 9:16, 21:9, etc.)
  -s, --size SIZE     Image size: 2K or 4K (default: standard)
  --search            Enable Google Search grounding for accuracy
  -v, --verbose       Show detailed output

Supported aspect ratios:

  • 1:1 - Square (default)
  • 2:3, 3:2 - Portrait/Landscape
  • 3:4, 4:3 - Standard
  • 4:5, 5:4 - Photo
  • 9:16, 16:9 - Widescreen
  • 21:9 - Ultra-wide/Cinematic

scripts/batch_generate.py

Generate multiple images with sequential naming.

Usage: batch_generate.py [OPTIONS] PROMPT

Arguments:
  PROMPT              Text prompt for image generation

Options:
  -n, --count N       Number of images to generate (default: 10)
  -d, --dir PATH      Output directory
  -p, --prefix STR    Filename prefix (default: "image")
  -r, --ratio RATIO   Aspect ratio
  -s, --size SIZE     Image size (2K/4K)
  --delay SECONDS     Delay between generations (default: 3)

Example:

python3 <skill_dir>/scripts/batch_generate.py "pixel art logo" -n 20 -d ./logos -p logo

Python API

You can also use the module directly:

from generate import generate_image, edit_image

# Generate image
result = generate_image(
    prompt="a futuristic city at night",
    output_path="city.png",
    aspect_ratio="16:9",
    image_size="4K"
)

# Edit existing image
result = edit_image(
    prompt="add flying cars to the sky",
    input_path="city.png",
    output_path="city_edited.png"
)

Environment Variables

Variable Description Default
GEMINI_API_KEY Google Gemini API key Required
IMAGE_OUTPUT_DIR Default output directory ./nanobanana-images

Features

Text-to-Image Generation

Create images from text descriptions. The model excels at:

  • Photorealistic images
  • Artistic styles (pixel art, illustration, etc.)
  • Product photography
  • Landscapes and scenes

Image Editing

Transform existing images with natural language:

  • Style transfer
  • Object addition/removal
  • Background changes
  • Color adjustments

High-Resolution Output

  • Standard: Fast generation, good quality
  • 2K: Enhanced detail (2048px)
  • 4K: Maximum quality (3840px), best for text rendering

Google Search Grounding

Enable --search for factually accurate images involving:

  • Real people, places, landmarks
  • Current events
  • Specific products or brands

Best Practices

Prompt Writing

Good prompts include:

  • Subject description
  • Style/aesthetic
  • Lighting and mood
  • Composition details
  • Color palette

Example:

"A cozy coffee shop interior, warm lighting, vintage aesthetic, 
wooden furniture, plants on shelves, morning sunlight through windows, 
soft focus background, 35mm film photography style"

Batch Generation Tips

  1. Generate 10-20 variations to explore options
  2. Use consistent prompts for style coherence
  3. Add 3-5 second delays to avoid rate limits
  4. Review results and iterate on best candidates

Rate Limits

  • Gemini API has usage quotas
  • Add delays between batch generations
  • Check your quota at Google AI Studio

Troubleshooting

"API key not found"

  • Set GEMINI_API_KEY environment variable
  • Or pass via --api-key option

"No image in response"

  • Prompt may have triggered safety filters
  • Try rephrasing to avoid sensitive content

"Rate limit exceeded"

  • Wait a few seconds and retry
  • Reduce batch size or add longer delays

References

how to use nanobanana

How to use nanobanana on Cursor

AI-first code editor with Composer

1

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 nanobanana
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/resciencelab/opc-skills --skill nanobanana

The skills CLI fetches nanobanana from GitHub repository resciencelab/opc-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/nanobanana

Reload or restart Cursor to activate nanobanana. Access the skill through slash commands (e.g., /nanobanana) 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.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.4★★★★★28 reviews
  • ★★★★★Dhruvi Jain· Dec 24, 2024

    nanobanana reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Ishan Brown· Dec 24, 2024

    nanobanana is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Benjamin Thompson· Dec 24, 2024

    nanobanana reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Oshnikdeep· Nov 15, 2024

    I recommend nanobanana for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Yuki Liu· Nov 15, 2024

    Solid pick for teams standardizing on skills: nanobanana is focused, and the summary matches what you get after install.

  • ★★★★★Camila Iyer· Nov 15, 2024

    I recommend nanobanana for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Ganesh Mohane· Oct 6, 2024

    Useful defaults in nanobanana — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Yuki Malhotra· Oct 6, 2024

    nanobanana has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Omar Choi· Oct 6, 2024

    Useful defaults in nanobanana — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Sakshi Patil· Sep 25, 2024

    nanobanana is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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