nano-banana-2

inference-sh/skills · updated Apr 8, 2026

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$npx skills add https://github.com/inference-sh/skills --skill nano-banana-2
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summary

Generate images with Google Gemini 3.1 Flash Image Preview via inference.sh CLI.

skill.md

Nano Banana 2 - Gemini 3.1 Flash Image Preview

Generate images with Google Gemini 3.1 Flash Image Preview via inference.sh CLI.

Quick Start

Requires inference.sh CLI (infsh). Install instructions

infsh login

infsh app run google/gemini-3-1-flash-image-preview --input '{"prompt": "a banana in space, photorealistic"}'

Examples

Basic Text-to-Image

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "A futuristic cityscape at sunset with flying cars"
}'

Multiple Images

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "Minimalist logo design for a coffee shop",
  "num_images": 4
}'

Custom Aspect Ratio

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "Panoramic mountain landscape with northern lights",
  "aspect_ratio": "16:9"
}'

Image Editing (with input images)

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "Add a rainbow in the sky",
  "images": ["https://example.com/landscape.jpg"]
}'

High Resolution (4K)

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "Detailed illustration of a medieval castle",
  "resolution": "4K"
}'

With Google Search Grounding

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "Current weather in Tokyo visualized as an artistic scene",
  "enable_google_search": true
}'

Input Options

Parameter Type Description
prompt string Required. What to generate or change
images array Input images for editing (up to 14). Supported: JPEG, PNG, WebP
num_images integer Number of images to generate
aspect_ratio string Output ratio: "1:1", "16:9", "9:16", "4:3", "3:4", "auto"
resolution string "1K", "2K", "4K" (default: 1K)
output_format string Output format for images
enable_google_search boolean Enable real-time info grounding (weather, news, etc.)

Output

Field Type Description
images array The generated or edited images
description string Text description or response from the model
output_meta object Metadata about inputs/outputs for pricing

Prompt Tips

Styles: photorealistic, illustration, watercolor, oil painting, digital art, anime, 3D render

Composition: close-up, wide shot, aerial view, macro, portrait, landscape

Lighting: natural light, studio lighting, golden hour, dramatic shadows, neon

Details: add specific details about textures, colors, mood, atmosphere

Sample Workflow

# 1. Generate sample input to see all options
infsh app sample google/gemini-3-1-flash-image-preview --save input.json

# 2. Edit the prompt
# 3. Run
infsh app run google/gemini-3-1-flash-image-preview --input input.json

Python SDK

from inferencesh import inference

client = inference()

# Basic generation
result = client.run({
    "app": "google/gemini-3-1-flash-image-preview@0c7ma1ex",
    "input": {
        "prompt": "A banana in space, photorealistic"
    }
})
print(result["output"])

# Stream live updates
for update in client.run({
    "app": "google/gemini-3-1-flash-image-preview@0c7ma1ex",
    "input": {
        "prompt": "A futuristic cityscape at sunset"
    }
}, stream=True):
    if update.get("progress"):
        print(f"progress: {update['progress']}%")
    if update.get("output"):
        print(f"output: {update['output']}")

Related Skills

# Original Nano Banana (Gemini 3 Pro Image, Gemini 2.5 Flash Image)
npx skills add inference-sh/skills@nano-banana

# Full platform skill (all 150+ apps)
npx skills add inference-sh/skills@infsh-cli

# All image generation models
npx skills add inference-sh/skills@ai-image-generation

Browse all image apps: infsh app list --category image

Documentation

how to use nano-banana-2

How to use nano-banana-2 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 nano-banana-2
2

Execute installation command

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

$npx skills add https://github.com/inference-sh/skills --skill nano-banana-2

The skills CLI fetches nano-banana-2 from GitHub repository inference-sh/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/nano-banana-2

Reload or restart Cursor to activate nano-banana-2. Access the skill through slash commands (e.g., /nano-banana-2) 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

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

4.837 reviews
  • Evelyn Agarwal· Dec 28, 2024

    nano-banana-2 is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Pratham Ware· Dec 24, 2024

    nano-banana-2 fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yusuf Wang· Dec 20, 2024

    nano-banana-2 fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Chaitanya Patil· Dec 16, 2024

    Keeps context tight: nano-banana-2 is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kiara Iyer· Dec 12, 2024

    Keeps context tight: nano-banana-2 is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Michael Sanchez· Nov 19, 2024

    Solid pick for teams standardizing on skills: nano-banana-2 is focused, and the summary matches what you get after install.

  • Kabir Torres· Nov 11, 2024

    Keeps context tight: nano-banana-2 is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Piyush G· Nov 7, 2024

    nano-banana-2 has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Diya Torres· Nov 3, 2024

    nano-banana-2 has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Shikha Mishra· Oct 26, 2024

    Solid pick for teams standardizing on skills: nano-banana-2 is focused, and the summary matches what you get after install.

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