Generate images with Google Gemini 3.1 Flash Image Preview via inference.sh CLI.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionnano-banana-2Execute the skills CLI command in your project's root directory to begin installation:
Fetches nano-banana-2 from inference-sh/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 nano-banana-2. Access via /nano-banana-2 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.
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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 images with Google Gemini 3.1 Flash Image Preview via inference.sh CLI.
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"}'
infsh app run google/gemini-3-1-flash-image-preview --input '{
"prompt": "A futuristic cityscape at sunset with flying cars"
}'
infsh app run google/gemini-3-1-flash-image-preview --input '{
"prompt": "Minimalist logo design for a coffee shop",
"num_images": 4
}'
infsh app run google/gemini-3-1-flash-image-preview --input '{
"prompt": "Panoramic mountain landscape with northern lights",
"aspect_ratio": "16:9"
}'
infsh app run google/gemini-3-1-flash-image-preview --input '{
"prompt": "Add a rainbow in the sky",
"images": ["https://example.com/landscape.jpg"]
}'
infsh app run google/gemini-3-1-flash-image-preview --input '{
"prompt": "Detailed illustration of a medieval castle",
"resolution": "4K"
}'
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
}'
| 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.) |
| 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 |
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
# 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
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']}")
# 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
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.
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nano-banana-2 is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
nano-banana-2 fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
nano-banana-2 fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: nano-banana-2 is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: nano-banana-2 is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: nano-banana-2 is focused, and the summary matches what you get after install.
Keeps context tight: nano-banana-2 is the kind of skill you can hand to a new teammate without a long onboarding doc.
nano-banana-2 has been reliable in day-to-day use. Documentation quality is above average for community skills.
nano-banana-2 has been reliable in day-to-day use. Documentation quality is above average for community skills.
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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