gemini-api

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• SKILL.md

SKILL.md (first lines)

# Gemini API

Build applications with the Google Gemini API and SDKs.

## When to Use

- Building applications that call the Gemini API
- Code imports `google.generativeai` (Python) or `@google/generative-ai` (TypeScript)
- User asks about Gemini API patterns, function calling, streaming, or vision
- Implementing agent workflows with the Gemini API
- Optimizing API costs, token usage, or latency

## Model Selection

| Model | ID | Best For |
|-------|-----|----------|
| Pro 2.5 | `gemini-2.5-pro` | Complex reasoning, architecture, research |
| Flash 2.5 | `gemini-2.5-flash` | Balanced coding, most development tasks |
| Flash Lite 2.5 | `gemini-2.5-flash-lite` | Fast responses, high-volume, cost-sensitive |

Default to Flash 2.5 unless the task requires deep reasoning (Pro) or speed/cost optimization (Flash Lite). For production, prefer pinned snapshot IDs over aliases.

## Python SDK

### Installation

```bash
pip install google-generativeai
```

### Basic Message

```python
import os
import google.generativeai as genai

genai.configure(api_key=os.environ["GEMINI_API_KEY"])

model = genai.GenerativeModel("gemini-2.5-flash")
response = model.generate_content("Explain async/await in Python")
print(response.text)
```

### Streaming

```python
model = genai.GenerativeModel("gemini-2.5-flash")
response = model.generate_content(
    "Write a haiku about coding",
    stream=True,
)
for chunk in response:
    print(chunk.text, end="", flush=True)
```

### System Instruction

```python
model = genai.GenerativeModel(
    "gemini-2.5-flash",
    system_instruction="You are a senior Python developer. Be concise.",
)
response = model.generate_content("Review this function")
```

## TypeScript SDK

### Installation

```bash
npm install @google/generative-ai
```

### Basic Message

```typescript
import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY!);
const model = genAI.getGenerativeModel({ model: "gemini-2.5-flash" });

const result = await model.generateContent("Explain async/await in TypeScript");
console.log(result.response.text());
```

### Streaming

```typescript
const model = genAI.getGenerativeModel({ model: "gemini-2.5-flash" });

const result = await model.generateContentStream("Write a haiku");

for await (const chunk of result.stream) {
  const text = chunk.text();
  process.stdout.write(text);
}
```

## Function Calling (Tool Use)

Define tools and let Gemini call them:

```python
def get_weather(location: str, unit: str = "celsius") -> dict:
    """Get current weather for a location."""
    # Your implementation here
    return {"temp": 18, "unit": unit, "location": location}

model = genai.GenerativeModel(
    "gemini-2.5-flash",
    tools=[get_weather],
)

chat = model.start_chat(enable_automatic_function_calling=True)
response = chat.send_message("What's the weather in SF?")
print(response.text)
```

For manual function calling (more control):

From Jamkris/everything-gemini-code, MIT.

More from Jamkris/everything-gemini-code