> ## Documentation Index
> Fetch the complete documentation index at: https://docs.floom.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Templates

> Starter scripts to fork and deploy.

Copy any of these, customize, and deploy with `/floom` in Claude Code or via the API.

## Company summarizer

Summarize any company from its URL using Gemini.

```python theme={null}
import os
from google import genai

def run(url: str) -> dict:
    client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])
    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=f"Summarize the company at {url} in 3 sentences."
    )
    return {"summary": response.text}
```

**Secrets:** `GEMINI_API_KEY` | **Deps:** `google-genai`

***

## Email classifier

Classify email intent with confidence scoring.

````python theme={null}
import os, json, re
from google import genai

def run(email_text: str) -> dict:
    client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])
    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=f"""Classify this email intent.
Return JSON: {{"intent": "interested|counter_offer|rejection|unclear", "confidence": 0.0-1.0, "summary": "one sentence"}}
Email: {email_text}
Return ONLY valid JSON."""
    )
    cleaned = re.sub(r'```json\s*|\s*```', '', response.text).strip()
    return json.loads(cleaned)
````

**Secrets:** `GEMINI_API_KEY` | **Deps:** `google-genai`

***

## CSV analyzer

Upload a CSV and get summary statistics.

```python theme={null}
import pandas as pd
import requests, io

def run(csv_url: str) -> dict:
    response = requests.get(csv_url)
    df = pd.read_csv(io.StringIO(response.text))
    return {
        "rows": len(df),
        "columns": list(df.columns),
        "summary": df.describe().to_dict()
    }
```

**Secrets:** none | **Deps:** `pandas`

***

## URL health checker

Check if a list of URLs are reachable.

```python theme={null}
import httpx

def run(urls: str) -> dict:
    url_list = [u.strip() for u in urls.split("\n") if u.strip()]
    results = []
    for url in url_list:
        try:
            r = httpx.get(url, timeout=10, follow_redirects=True)
            results.append({"url": url, "status": r.status_code, "ok": r.is_success})
        except Exception as e:
            results.append({"url": url, "status": 0, "ok": False, "error": str(e)})
    return {"results": results}
```

**Secrets:** none | **Deps:** `httpx`

***

## Text translator

Translate text between languages using Anthropic.

```python theme={null}
import os, anthropic

def run(text: str, target_language: str = "Spanish") -> dict:
    client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        messages=[{"role": "user", "content": f"Translate to {target_language}:\n\n{text}"}]
    )
    return {"translation": message.content[0].text}
```

**Secrets:** `ANTHROPIC_API_KEY` | **Deps:** `anthropic`

***

## PDF text extractor

Extract text from an uploaded PDF.

```python theme={null}
import requests, fitz, io

def run(pdf_url: str) -> dict:
    response = requests.get(pdf_url)
    doc = fitz.open(stream=io.BytesIO(response.content), filetype="pdf")
    pages = []
    for page in doc:
        pages.append({"page": page.number + 1, "text": page.get_text()})
    return {"pages": pages, "total_pages": len(doc)}
```

**Secrets:** none | **Deps:** `PyMuPDF`