Text-to-image and image editing powered by Google's Gemini 3 Pro Image model.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionnanobananaExecute the skills CLI command in your project's root directory to begin installation:
Fetches nanobanana from resciencelab/opc-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate nanobanana. Access via /nanobanana in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Generate and edit images using Google's Gemini 3 Pro Image model (gemini-3-pro-image-preview, nicknamed "Nano Banana Pro" 🍌).
Required:
GEMINI_API_KEY - Get from Google AI Studiogoogle-genai packageInstall dependencies:
pip install google-genai pillow
python3 <skill_dir>/scripts/generate.py "a cute robot mascot, pixel art style" -o robot.png
python3 <skill_dir>/scripts/generate.py "make the background blue" -i input.jpg -o output.png
python3 <skill_dir>/scripts/generate.py "cinematic landscape" --ratio 21:9 -o landscape.png
python3 <skill_dir>/scripts/generate.py "professional product photo" --size 4K -o product.png
scripts/generate.pyMain 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/Landscape3:4, 4:3 - Standard4:5, 5:4 - Photo9:16, 16:9 - Widescreen21:9 - Ultra-wide/Cinematicscripts/batch_generate.pyGenerate 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
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"
)
| Variable | Description | Default |
|---|---|---|
GEMINI_API_KEY |
Google Gemini API key | Required |
IMAGE_OUTPUT_DIR |
Default output directory | ./nanobanana-images |
Create images from text descriptions. The model excels at:
Transform existing images with natural language:
Enable --search for factually accurate images involving:
Good prompts include:
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"
"API key not found"
GEMINI_API_KEY environment variable--api-key option"No image in response"
"Rate limit exceeded"
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
nanobanana reduced setup friction for our internal harness; good balance of opinion and flexibility.
nanobanana is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
nanobanana reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend nanobanana for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: nanobanana is focused, and the summary matches what you get after install.
I recommend nanobanana for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in nanobanana — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
nanobanana has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in nanobanana — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
nanobanana is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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