nano-banana-pro

intellectronica/agent-skills · updated Jun 2, 2026

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$npx skills add https://github.com/intellectronica/agent-skills --skill nano-banana-pro
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summary

Text-to-image generation and image-to-image editing via Google's Gemini 3 Pro Image API.

  • Supports both new image generation from text prompts and editing of existing images with instruction-based modifications
  • Configurable output resolution: 1K (default), 2K, or 4K for high-resolution results
  • Automatically detects API key from --api-key argument or GEMINI_API_KEY environment variable
  • Saves PNG output to the user's current working directory with timestamped filenames
skill.md

Nano Banana Pro Image Generation & Editing

Generate new images or edit existing ones using Google's Nano Banana Pro API (Gemini 3 Pro Image).

Usage

Run the script using absolute path (do NOT cd to skill directory first):

Generate new image:

uv run ~/.claude/skills/nano-banana-pro/scripts/generate_image.py --prompt "your image description" --filename "output-name.png" [--resolution 1K|2K|4K] [--api-key KEY]

Edit existing image:

uv run ~/.claude/skills/nano-banana-pro/scripts/generate_image.py --prompt "editing instructions" --filename "output-name.png" --input-image "path/to/input.png" [--resolution 1K|2K|4K] [--api-key KEY]

Important: Always run from the user's current working directory so images are saved where the user is working, not in the skill directory.

Resolution Options

The Gemini 3 Pro Image API supports three resolutions (uppercase K required):

  • 1K (default) - ~1024px resolution
  • 2K - ~2048px resolution
  • 4K - ~4096px resolution

Map user requests to API parameters:

  • No mention of resolution → 1K
  • "low resolution", "1080", "1080p", "1K" → 1K
  • "2K", "2048", "normal", "medium resolution" → 2K
  • "high resolution", "high-res", "hi-res", "4K", "ultra" → 4K

API Key

The script checks for API key in this order:

  1. --api-key argument (use if user provided key in chat)
  2. GEMINI_API_KEY environment variable

If neither is available, the script exits with an error message.

Filename Generation

Generate filenames with the pattern: yyyy-mm-dd-hh-mm-ss-name.png

Format: {timestamp}-{descriptive-name}.png

  • Timestamp: Current date/time in format yyyy-mm-dd-hh-mm-ss (24-hour format)
  • Name: Descriptive lowercase text with hyphens
  • Keep the descriptive part concise (1-5 words typically)
  • Use context from user's prompt or conversation
  • If unclear, use random identifier (e.g., x9k2, a7b3)

Examples:

  • Prompt "A serene Japanese garden" → 2025-11-23-14-23-05-japanese-garden.png
  • Prompt "sunset over mountains" → 2025-11-23-15-30-12-sunset-mountains.png
  • Prompt "create an image of a robot" → 2025-11-23-16-45-33-robot.png
  • Unclear context → 2025-11-23-17-12-48-x9k2.png

Image Editing

When the user wants to modify an existing image:

  1. Check if they provide an image path or reference an image in the current directory
  2. Use --input-image parameter with the path to the image
  3. The prompt should contain editing instructions (e.g., "make the sky more dramatic", "remove the person", "change to cartoon style")
  4. Common editing tasks: add/remove elements, change style, adjust colors, blur background, etc.

Prompt Handling

For generation: Pass user's image description as-is to --prompt. Only rework if clearly insufficient.

For editing: Pass editing instructions in --prompt (e.g., "add a rainbow in the sky", "make it look like a watercolor painting")

Preserve user's creative intent in both cases.

Output

  • Saves PNG to current directory (or specified path if filename includes directory)
  • Script outputs the full path to the generated image
  • Do not read the image back - just inform the user of the saved path

Examples

Generate new image:

uv run ~/.claude/skills/nano-banana-pro/scripts/generate_image.py --prompt "A serene Japanese garden with cherry blossoms" --filename "2025-11-23-14-23-05-japanese-garden.png" --resolution 4K

Edit existing image:

uv run ~/.claude/skills/nano-banana-pro/scripts/generate_image.py --prompt "make the sky more dramatic with storm clouds" --filename "2025-11-23-14-25-30-dramatic-sky.png" --input-image "original-photo.jpg" --resolution 2K
how to use nano-banana-pro

How to use nano-banana-pro 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-pro
2

Execute installation command

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

$npx skills add https://github.com/intellectronica/agent-skills --skill nano-banana-pro

The skills CLI fetches nano-banana-pro from GitHub repository intellectronica/agent-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-pro

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

4.533 reviews
  • Charlotte Rao· Dec 24, 2024

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

  • Dhruvi Jain· Dec 12, 2024

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

  • Emma Lopez· Dec 8, 2024

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

  • Nia Brown· Dec 4, 2024

    Registry listing for nano-banana-pro matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Aisha Shah· Nov 27, 2024

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

  • Oshnikdeep· Nov 3, 2024

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

  • Ganesh Mohane· Oct 22, 2024

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

  • Aisha Gupta· Oct 18, 2024

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

  • Harper Gill· Sep 25, 2024

    nano-banana-pro reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Henry Taylor· Sep 13, 2024

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

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