sickn33/agentic-awesome-skills/image-generator

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Generate and edit images using Gemini's Nano Banana Pro model (gemini-3-pro-image-preview). Use this skill when the user asks you to generate images, create visuals, edit photos, create logos, generate product mockups, or perform any image generation/editing task.

Install

npx vskill@latest install sickn33/agentic-awesome-skills/image-generator

SKILL.md

Image Generator

Detailed Guide

Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

When to Use

Use when this workflow matches the user request: Generate and edit images using Gemini's Nano Banana Pro model (gemini-3-pro-image-preview). Use this skill when the user asks you to generate images, create visuals, edit photos, create logos, generate product mockups, or perform any image generation/editing task.

_Source: dair-ai/dair-academy-plugins (MIT)._

This skill generates and edits images using Google's Gemini Nano Banana Pro model (gemini-3-pro-image-preview).

API Usage

Paid external API workflow: confirm approved model/budget, exact prompt and input image set, rights/consent and provider data policy before requests. Model ID/options below are historical examples; check current availability and SDK version. Never promise exact text, masking or resolution; inspect returned MIME/dimensions and visible output. Preserve originals and use new unique output names, writing only within the authorized destination. A missing/blocked image is not success; do not retry billable calls blindly after uncertain outcomes.

Basic Text-to-Image (Python)
python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=["Your prompt here"],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        image_config=types.ImageConfig(
            aspect_ratio="16:9",  # Optional
            image_size="2K"       # Optional: "1K", "2K", "4K"
        )
    )
)
for part in response.parts:
    if part.text is not None:
        print(part.text)
    elif part.inline_data is not None:
        image = part.as_image()
        image.save("generated_image.png")
Basic Text-to-Image (JavaScript)
javascript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
const ai = new GoogleGenAI({});
const response = await ai.models.generateContent({
    model: "gemini-3-pro-image-preview",
    contents: "Your prompt here",
    config: {
        responseModalities: ['TEXT', 'IMAGE'],
        imageConfig: {
            aspectRatio: "16:9",
            imageSize: "2K"
        }
    }
});
for (const part of response.candidates[0].content.parts) {
    if (part.text) {
        console.log(part.text);
    } else if (part.inlineData) {
        const buffer = Buffer.from(part.inlineData.data, "base64");
        fs.writeFileSync("generated_image.png", buffer);
    }
}
REST alternative

Prefer the SDK examples so credentials are not interpolated into command-line headers and JSON is serialized correctly. If implementing REST, use a protected header/request file, explicit timeouts and HTTP-status handling; validate candidate/part/MIME fields before writing bytes. Do not assume the first candidate or an image exists.

Image Editing (with input image)
python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
input_image = Image.open('input.png')
prompt = "Add a wizard hat to the cat in this image"
response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=[prompt, input_image],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)
for part in response.parts:
    if part.inline_data is not None:
        image = part.as_image()
        image.save("edited_image.png")
Multi-Image Composition
python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
image1 = Image.open('dress.png')
image2 = Image.open('model.png')
prompt = "Put the dress from the first image on the model from the second image"
response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=[image1, image2, prompt],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        image_config=types.ImageConfig(
            aspect_ratio="3:4",
            image_size="2K"
        )
    )
)
With Google Search Grounding
python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="Visualize the current weather forecast for San Francisco",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        image_config=types.ImageConfig(aspect_ratio="16:9"),
        tools=[{"google_search": {}}]
    )
)

Limitations

  • Requires the upstream tool, account, API key, or local setup when the workflow names one.
  • Does not authorize destructive, production, paid, or external-message actions without explicit user approval.
  • Validate generated artifacts or recommendations against the user's real sources before treating them as final.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

See the full security report

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

See evaluation results

Source

Popularity

Repository stars
47,054
Repository forks
6,853
7-day trend score
54

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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