Agent-Skills.md

Agent Skills: Gemini API

Use Google Gemini API for text generation, multimodal analysis, image generation (Nano Banana), function calling, and search grounding. Invoke when user wants to use Gemini, ask Gemini, generate images with Gemini, or analyze content with Gemini.

UncategorizedID: legacybridge-tech/claude-plugins/gemini

Repository

legacybridge-tech/claude-plugins
legacybridge-techLicense: MIT
5

Install this agent skill to your local

pnpm dlx add-skill https://github.com/legacybridge-tech/claude-plugins/tree/HEAD/gemini-api/skills/gemini

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Browse the full folder contents for gemini.

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gemini-api/skills/gemini/SKILL.md

Skill Metadata

Name
gemini
Description
Use Google Gemini API for text generation, multimodal analysis, image generation (Nano Banana), function calling, and search grounding. Invoke when user wants to use Gemini, ask Gemini, generate images with Gemini, or analyze content with Gemini.

Gemini API

Use Google Gemini API via REST for text generation, multimodal analysis, image generation, and more.

Prerequisites

  • Environment variable GOOGLE_API_KEY must be set
  • API endpoint: https://generativelanguage.googleapis.com/v1beta

Available Models

| Model | Use Case | |-------|----------| | gemini-2.5-flash | Fast text generation (default) | | gemini-2.5-pro | High quality text generation | | gemini-3-flash-preview | Latest flash model | | gemini-3-pro-preview | Latest pro model | | gemini-2.5-flash-image | Image generation (Nano Banana) | | gemini-3-pro-image-preview | Advanced image generation with thinking & search |

Workflow

Phase 1: Determine Task Type

Based on user request, identify which capability to use:

  • Text Generation: Basic prompts, chat, Q&A
  • Multimodal Analysis: Analyze images, videos, or audio
  • Image Generation: Create or edit images (Nano Banana)
  • Function Calling: Execute custom functions
  • Search Grounding: Real-time web search integration

Phase 2: Execute API Call

Use the appropriate curl command based on task type.

1. Text Generation

Basic Prompt

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts": [{"text": "Your prompt here"}]
      }]
    }'

With Configuration

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts": [{"text": "Your prompt here"}]
      }],
      "generationConfig": {
        "temperature": 0.9,
        "maxOutputTokens": 2000,
        "stopSequences": ["END"]
      }
    }'

Multi-turn Chat

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [
        {"role": "user", "parts": [{"text": "First message"}]},
        {"role": "model", "parts": [{"text": "Model response"}]},
        {"role": "user", "parts": [{"text": "Follow-up question"}]}
      ]
    }'

System Instructions

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "system_instruction": {
        "parts": [{"text": "You are a helpful assistant that speaks like a pirate."}]
      },
      "contents": [{
        "parts": [{"text": "Hello!"}]
      }]
    }'

JSON Mode Output

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts": [{"text": "List 3 colors as JSON array"}]
      }],
      "generationConfig": {
        "response_mime_type": "application/json"
      }
    }'

Streaming Response

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:streamGenerateContent?alt=sse&key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts": [{"text": "Write a long story"}]
      }]
    }'

Safety Settings

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts": [{"text": "Your prompt"}]
      }],
      "safetySettings": [
        {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_ONLY_HIGH"},
        {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_ONLY_HIGH"}
      ]
    }'

2. Multimodal Analysis

Image Analysis (Base64 Inline)

# First encode image to base64
BASE64_IMAGE=$(base64 -w0 image.jpg)

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts": [
          {"text": "Describe this image in detail"},
          {"inline_data": {"mime_type": "image/jpeg", "data": "'$BASE64_IMAGE'"}}
        ]
      }]
    }'

Video Analysis (File API)

Step 1: Upload Video

# Get upload URL
UPLOAD_URL=$(curl -s "https://generativelanguage.googleapis.com/upload/v1beta/files?key=$GOOGLE_API_KEY" \
    -H "X-Goog-Upload-Protocol: resumable" \
    -H "X-Goog-Upload-Command: start" \
    -H "X-Goog-Upload-Header-Content-Length: $(stat -f%z video.mp4)" \
    -H "X-Goog-Upload-Header-Content-Type: video/mp4" \
    -H "Content-Type: application/json" \
    -d '{"file": {"display_name": "video.mp4"}}' \
    -D - | grep -i "x-goog-upload-url" | cut -d' ' -f2 | tr -d '\r')

# Upload file
curl "$UPLOAD_URL" \
    -H "X-Goog-Upload-Offset: 0" \
    -H "X-Goog-Upload-Command: upload, finalize" \
    -H "Content-Type: video/mp4" \
    --data-binary @video.mp4

Step 2: Query with Video

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts": [
          {"text": "Describe what happens in this video"},
          {"file_data": {"mime_type": "video/mp4", "file_uri": "FILE_URI_FROM_UPLOAD"}}
        ]
      }]
    }'

Audio Analysis

Similar to video, upload via File API then query:

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts": [
          {"text": "Transcribe and summarize this audio"},
          {"file_data": {"mime_type": "audio/mp3", "file_uri": "FILE_URI_FROM_UPLOAD"}}
        ]
      }]
    }'

3. Image Generation (Nano Banana)

Basic Image Generation

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{"parts": [{"text": "Create a photorealistic image of a cat wearing a hat"}]}],
      "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"]
      }
    }'

With Aspect Ratio Control

Supported ratios: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{"parts": [{"text": "Create a landscape scene"}]}],
      "generationConfig": {
        "responseModalities": ["IMAGE"],
        "imageConfig": {
          "aspectRatio": "16:9"
        }
      }
    }'

Image Editing (Character Consistency)

BASE64_IMAGE=$(base64 -w0 original.jpg)

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts": [
          {"text": "Put this character in a tropical forest"},
          {"inline_data": {"mime_type": "image/jpeg", "data": "'$BASE64_IMAGE'"}}
        ]
      }],
      "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"]
      }
    }'

High Resolution (Pro Model - 2K/4K)

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{"parts": [{"text": "A photo of an oak tree in all four seasons"}]}],
      "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"],
        "imageConfig": {
          "aspectRatio": "1:1",
          "imageSize": "4K"
        }
      }
    }'

Image Generation with Search Grounding (Pro)

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{"parts": [{"text": "Visualize the current weather forecast for Tokyo as a chart"}]}],
      "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"],
        "imageConfig": {
          "aspectRatio": "16:9"
        }
      },
      "tools": [{"google_search": {}}]
    }'

Multi-Image Fusion

BASE64_IMG1=$(base64 -w0 image1.jpg)
BASE64_IMG2=$(base64 -w0 image2.jpg)

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts": [
          {"text": "Combine these two characters in a fantasy world"},
          {"inline_data": {"mime_type": "image/jpeg", "data": "'$BASE64_IMG1'"}},
          {"inline_data": {"mime_type": "image/jpeg", "data": "'$BASE64_IMG2'"}}
        ]
      }],
      "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"]
      }
    }'

4. Function Calling

Define and Call Functions

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "role": "user",
        "parts": [{"text": "What movies are playing in Mountain View?"}]
      }],
      "tools": [{
        "function_declarations": [{
          "name": "find_movies",
          "description": "Find movies playing in theaters",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {"type": "string", "description": "City and state"},
              "genre": {"type": "string", "description": "Movie genre"}
            },
            "required": ["location"]
          }
        }]
      }]
    }'

Provide Function Response

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [
        {"role": "user", "parts": [{"text": "What movies are playing in Mountain View?"}]},
        {"role": "model", "parts": [{"functionCall": {"name": "find_movies", "args": {"location": "Mountain View, CA"}}}]},
        {"role": "function", "parts": [{"functionResponse": {"name": "find_movies", "response": {"movies": ["Barbie", "Oppenheimer"]}}}]}
      ],
      "tools": [{
        "function_declarations": [{
          "name": "find_movies",
          "description": "Find movies playing in theaters",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {"type": "string"},
              "genre": {"type": "string"}
            },
            "required": ["location"]
          }
        }]
      }]
    }'

5. Search Grounding

Real-time web search integration:

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{"parts": [{"text": "What is the current Google stock price?"}]}],
      "tools": [{"google_search": {}}]
    }'

Response includes groundingMetadata with sources.

6. Context Caching

For repeated queries on the same large content:

Create Cache

curl "https://generativelanguage.googleapis.com/v1beta/cachedContents?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "model": "models/gemini-2.5-flash",
      "contents": [{"parts": [{"text": "LARGE_DOCUMENT_TEXT_HERE"}]}],
      "ttl": "3600s"
    }'

Use Cache

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "cachedContent": "cachedContents/CACHE_ID",
      "contents": [{"parts": [{"text": "Summarize the document"}]}]
    }'

7. Model Information

List All Models

curl "https://generativelanguage.googleapis.com/v1beta/models?key=$GOOGLE_API_KEY"

Get Specific Model

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash?key=$GOOGLE_API_KEY"

Response Handling

Text Response Structure

{
  "candidates": [{
    "content": {
      "parts": [{"text": "Response text here"}],
      "role": "model"
    },
    "finishReason": "STOP"
  }],
  "usageMetadata": {
    "promptTokenCount": 10,
    "candidatesTokenCount": 50,
    "totalTokenCount": 60
  }
}

Image Response Structure

When using image generation, response includes base64-encoded images:

{
  "candidates": [{
    "content": {
      "parts": [
        {"text": "Here is your image:"},
        {"inlineData": {"mimeType": "image/png", "data": "BASE64_IMAGE_DATA"}}
      ]
    }
  }]
}

To save the image:

# Extract and decode image from response
echo "BASE64_DATA" | base64 -d > output.png

Error Handling

| Error | Cause | Solution | |-------|-------|----------| | 400 | Invalid request | Check JSON syntax | | 401 | Invalid API key | Verify GOOGLE_API_KEY | | 429 | Rate limit | Wait and retry | | 500 | Server error | Retry with exponential backoff |

Best Practices

  1. Use appropriate model: Flash for speed, Pro for quality
  2. Set temperature: Lower (0.1-0.3) for factual, higher (0.7-1.0) for creative
  3. Limit output tokens: Set maxOutputTokens to avoid excessive responses
  4. Use caching: For repeated queries on large documents
  5. Handle streaming: For long responses, use streamGenerateContent
  6. Image generation tips: Use detailed, descriptive prompts for best results