Agent skill
datalab
Convert documents (PDF, EPUB, PPTX, DOCX, XLSX, HTML, images) to Markdown using Datalab cloud API. Use when user wants to use Datalab API for document conversion, or prefers cloud-based processing over local marker CLI.
Stars
163
Forks
31
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/datalab
SKILL.md
Datalab Document Converter
Convert PDF, EPUB, PPTX, DOCX, XLSX, HTML, and image files to Markdown using the Datalab cloud API.
Prerequisites
bash
# Install Datalab Python SDK
uv pip install datalab-python-sdk
# Set API key (get from https://www.datalab.to)
export DATALAB_API_KEY="your_api_key_here"
Python SDK Usage
Basic Conversion
python
from datalab_sdk import DatalabClient
client = DatalabClient() # Uses DATALAB_API_KEY env var
# Convert document to markdown
result = client.convert("document.pdf")
print(result.markdown)
# Save output
result = client.convert(
"document.pdf",
save_output="./output/document"
)
# Creates: output/document.md, output/document_meta.json, output/*.png
With Options
python
from datalab_sdk import DatalabClient, ConvertOptions
client = DatalabClient()
options = ConvertOptions(
output_format="markdown", # markdown, json, html, chunks
force_ocr=False, # Force OCR on all pages
paginate=True, # Add page separators
use_llm=True, # Use LLM for better accuracy
disable_image_extraction=True, # Plain text only
page_range="0,5-10,20" # Specific pages
)
result = client.convert("document.pdf", options=options)
Async Client (Better Performance)
python
import asyncio
from datalab_sdk import AsyncDatalabClient, ConvertOptions
async def convert_document():
async with AsyncDatalabClient() as client:
result = await client.convert(
"document.pdf",
options=ConvertOptions(output_format="markdown")
)
return result.markdown
markdown = asyncio.run(convert_document())
print(markdown)
OCR Only
python
from datalab_sdk import DatalabClient
client = DatalabClient()
# OCR a document
ocr_result = client.ocr("document.pdf")
print(ocr_result.pages) # Get all text
REST API Usage
Submit Document for Conversion
python
import requests
url = "https://www.datalab.to/api/v1/marker"
headers = {"X-API-Key": "YOUR_API_KEY"}
with open("document.pdf", "rb") as f:
files = {"file": ("document.pdf", f, "application/pdf")}
data = {
"output_format": (None, "markdown"),
"force_ocr": (None, "false"),
"use_llm": (None, "false"),
"disable_image_extraction": (None, "true")
}
response = requests.post(url, headers=headers, files=files, data=data)
result = response.json()
print(f"Request ID: {result['request_id']}")
print(f"Check URL: {result['request_check_url']}")
Poll for Results
python
import requests
import time
check_url = result['request_check_url']
headers = {"X-API-Key": "YOUR_API_KEY"}
while True:
response = requests.get(check_url, headers=headers)
status = response.json()
if status.get('status') == 'complete':
print(status['markdown'])
break
elif status.get('status') == 'failed':
print(f"Error: {status.get('error')}")
break
time.sleep(2) # Poll every 2 seconds
Using curl
bash
# Submit document
curl -X POST "https://www.datalab.to/api/v1/marker" \
-H "X-API-Key: $DATALAB_API_KEY" \
-F "file=@document.pdf" \
-F "output_format=markdown" \
-F "disable_image_extraction=true"
# Check status
curl "https://www.datalab.to/api/v1/marker/{request_id}" \
-H "X-API-Key: $DATALAB_API_KEY"
API Options
| Parameter | Type | Description |
|---|---|---|
output_format |
string | markdown, json, html, chunks |
force_ocr |
boolean | Force OCR on all pages |
paginate |
boolean | Add page separators |
use_llm |
boolean | Use LLM for better accuracy |
strip_existing_ocr |
boolean | Remove existing OCR and re-process |
disable_image_extraction |
boolean | Plain text only |
page_range |
string | Specific pages, e.g., "0,5-10,20" |
max_pages |
integer | Maximum pages to convert |
Batch Processing
python
import asyncio
from pathlib import Path
from datalab_sdk import AsyncDatalabClient, ConvertOptions
async def batch_convert(files: list[Path], output_dir: Path):
output_dir.mkdir(parents=True, exist_ok=True)
options = ConvertOptions(
output_format="markdown",
disable_image_extraction=True
)
async with AsyncDatalabClient() as client:
tasks = [
client.convert(
file_path=f,
options=options,
save_output=output_dir / f.stem
)
for f in files
]
results = await asyncio.gather(*tasks, return_exceptions=True)
for f, result in zip(files, results):
if isinstance(result, Exception):
print(f"✗ {f.name}: {result}")
elif result.success:
print(f"✓ {f.name}: {result.page_count} pages")
else:
print(f"✗ {f.name}: {result.error}")
# Usage
files = list(Path("documents").glob("*.pdf"))
asyncio.run(batch_convert(files, Path("output")))
Error Handling
python
from datalab_sdk import (
DatalabClient,
DatalabAPIError,
DatalabTimeoutError,
DatalabFileError
)
client = DatalabClient()
try:
result = client.convert("document.pdf", max_polls=60, poll_interval=2)
if result.success:
print(result.markdown)
else:
print(f"Conversion failed: {result.error}")
except DatalabAPIError as e:
if e.status_code == 401:
print("Authentication failed - check API key")
elif e.status_code == 429:
print("Rate limit exceeded - wait before retrying")
else:
print(f"API Error: {e}")
except DatalabTimeoutError:
print("Operation timed out - try increasing max_polls")
except DatalabFileError as e:
print(f"File error: {e}")
Datalab vs Marker CLI
| Feature | Datalab API | Marker CLI |
|---|---|---|
| Processing | Cloud-based | Local |
| GPU Required | No | Yes (recommended) |
| Setup | API key only | Python + PyTorch |
| Speed | Fast (cloud GPU) | Depends on hardware |
| Privacy | Data sent to cloud | Local processing |
| Cost | API credits | Free |
Instructions
-
Confirm the input file path exists
-
Check if
$DATALAB_API_KEYenvironment variable is set -
Use AskUserQuestion tool to ask user preferences:
Question 1 - Processing Method:
- Header: "Method"
- Question: "使用哪种方式调用 Datalab API?"
- Options:
- "Python SDK (Recommended)": 使用 datalab-python-sdk,更简洁
- "REST API": 使用 requests 直接调用 API
- "curl": 使用命令行 curl
Question 2 - Image Extraction:
- Header: "Images"
- Question: "是否需要提取文档中的图片?"
- Options:
- "No (Recommended)": 仅提取文本,生成纯 Markdown
- "Yes": 提取图片并保存
-
Generate and run the appropriate code based on user's choice
-
Report the output file location and any extraction notes
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