菜单
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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/canar1406/thaytrieu --skill image-generator命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | image-generator |
| description | 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. |
| allowed-tools | Read, Write, Bash, WebFetch |
| category | media |
| risk | safe |
| source | official |
| source_repo | dair-ai/dair-academy-plugins |
| source_type | official |
| date_added | 2026-06-19 |
| author | DAIR.AI |
| license | MIT |
| license_source | https://github.com/dair-ai/dair-academy-plugins/blob/main/README.md#license |
| tags | ["dair-academy","ai","workflow"] |
| tools | ["claude-code","codex-cli","cursor"] |
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).
Before using this skill, the user must set the GEMINI_API_KEY environment variable:
~/.zshrc, ~/.bashrc, etc.):
export GEMINI_API_KEY="your_api_key_here"
source ~/.zshrc (or ~/.bashrc)The skill will not work without this configuration.
Before making any API call, verify the key is set:
if [ -z "$GEMINI_API_KEY" ]; then
echo "ERROR: GEMINI_API_KEY is not set. Please export it in your shell profile."
exit 1
fi
If the key is missing, stop and tell the user to set it using the instructions above.
Model: gemini-3-pro-image-preview
API Key: Read from the GEMINI_API_KEY environment variable
When the user provides a path to an image they want to edit or iterate on, use this workflow:
# Get the image path from user
IMG_PATH="/path/to/user/image.png"
# Detect mime type
[[ == *.png ]];
MIME_TYPE=
[[ == *.jpg ]] || [[ == *.jpeg ]];
MIME_TYPE=
[[ == *.webp ]];
MIME_TYPE=
MIME_TYPE=
[[ == ]];
IMG_BASE64=$( -i )
IMG_BASE64=$( -w0 )
IMPORTANT: Always use a file-based approach for the request body. Base64-encoded images are too large for command-line arguments and will cause "argument list too long" errors.
# User's edit request
EDIT_PROMPT="Add a santa hat to the person in this image"
# Write request to a JSON file (avoids command line length limits)
cat > /tmp/gemini_request.json << JSONEOF
{
"contents": [{
"parts": [
{"text": "$EDIT_PROMPT"},
{
"inline_data": {
"mime_type": "$MIME_TYPE",
"data": "$IMG_BASE64"
}
}
]
}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"]
}
}
JSONEOF
# Call the API using the file
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d @/tmp/gemini_request.json > /tmp/gemini_response.json
# Extract image from response and save
python3 -c "
import json
import base64
with open('/tmp/gemini_response.json') as f:
data = json.load(f)
for part in data['candidates'][0]['content']['parts']:
if 'inlineData' in part:
img_data = part['inlineData']['data']
mime = part['inlineData']['mimeType']
ext = 'png' if 'png' in mime else 'jpg'
with open('edited_image.' + ext, 'wb') as out:
out.write(base64.b64decode(img_data))
print(f'Saved: edited_image.{ext}')
elif 'text' in part:
print(part['text'])
"
For iterating on images, always use file-based requests:
# Variables
IMG_PATH="/path/to/image.png"
EDIT_PROMPT="Make the background a sunset beach"
OUTPUT_PATH="edited_output.png"
# Detect mime type and encode
MIME_TYPE=$([[ "$IMG_PATH" == *.png ]] && echo "image/png" || echo "image/jpeg")
IMG_BASE64=$(base64 -i "$IMG_PATH" 2>/dev/null || base64 -w0 "$IMG_PATH")
# Write request to file (required - base64 images are too large for command line)
cat > /tmp/gemini_request.json << JSONEOF
{
"contents": [{
"parts": [
{"text": "$EDIT_PROMPT"},
{"inline_data": {"mime_type": "$MIME_TYPE", "data": "$IMG_BASE64"}}
]
}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"]
}
}
JSONEOF
# Call API and extract image
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d @/tmp/gemini_request.json > /tmp/gemini_response.json
# Save the output image
python3 -c "
import json, base64
with open('/tmp/gemini_response.json') as f:
data = json.load(f)
for part in data.get('candidates', [{}])[0].get('content', {}).get('parts', []):
if 'inlineData' in part:
with open('$OUTPUT_PATH', 'wb') as f:
f.write(base64.b64decode(part['inlineData']['data']))
print('Saved: $OUTPUT_PATH')
"
To combine elements from multiple images (also uses file-based approach):
IMG1_PATH="/path/to/image1.png"
IMG2_PATH="/path/to/image2.png"
PROMPT="Put the dress from the first image on the person in the second image"
IMG1_BASE64=$(base64 -i "$IMG1_PATH" 2>/dev/null || base64 -w0 "$IMG1_PATH")
IMG2_BASE64=$(base64 -i "$IMG2_PATH" 2>/dev/null || base64 -w0 "$IMG2_PATH")
# Write request to file
cat > /tmp/gemini_request.json << JSONEOF
{
"contents": [{
"parts": [
{"text": "$PROMPT"},
{"inline_data": {"mime_type": "image/png", "data": "$IMG1_BASE64"}},
{"inline_data": {"mime_type": "image/png", "data": "$IMG2_BASE64"}}
]
}],
"generationConfig": {"responseModalities": ["TEXT", "IMAGE"]}
}
JSONEOF
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d @/tmp/gemini_request.json > /tmp/gemini_response.json
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")
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);
}
}
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [{"text": "Your prompt here"}]
}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"],
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "2K"
}
}
}' | jq -r '.candidates[0].content.parts[] | select(.inlineData) | .inlineData.data' | base64 --decode > output.png
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")
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"
)
)
)
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": {}}]
)
)
Instead of: cat, wizard hat, cute
Write: A fluffy orange cat wearing a small knitted wizard hat, sitting on a wooden floor with soft natural lighting from a window
Be explicit about:
Mention:
| Aspect Ratio | 1K Resolution | 2K Resolution | 4K Resolution |
|---|---|---|---|
| 1:1 | 1024x1024 | 2048x2048 | 4096x4096 |
| 16:9 | 1376x768 | 2752x1536 | 5504x3072 |
| 9:16 | 768x1376 | 1536x2752 | 3072x5504 |
| 3:2 | 1264x848 | 2528x1696 | 5056x3392 |
| 2:3 | 848x1264 | 1696x2528 | 3392x5056 |
Create a modern, minimalist logo for a coffee shop called 'The Daily Grind'.
The text should be in a clean, bold, sans-serif font.
Black and white color scheme. Put the logo in a circle.
A high-resolution, studio-lit product photograph of a minimalist ceramic
coffee mug in matte black on a polished concrete surface. Three-point
softbox lighting with soft, diffused highlights. Slightly elevated
45-degree camera angle. Sharp focus on steam rising from the coffee.
Transform this photograph of a city street at night into Vincent van Gogh's
'Starry Night' style. Preserve the composition but render with swirling,
impasto brushstrokes and deep blues with bright yellows.
Create a vibrant infographic explaining photosynthesis as a recipe.
Show "ingredients" (sunlight, water, CO2) and "finished dish" (sugar/energy).
Style like a colorful kids' cookbook, suitable for 4th graders.
Common issues:
response_modalities includes 'IMAGE'To use the Python SDK:
pip install google-genai pillow
For JavaScript:
npm install @google/genai