The Scenario
A content researcher on a machine learning project has been given a list of 50 competitor blog URLs collected by the strategy team — all in an Excel workbook, column A. The task is to get the article body text from each one, cleaned and formatted as Markdown, so it can be reviewed and potentially used as fine-tuning data. The researcher's background is linguistics, not web scraping. The strategy team's background is not either. Everyone agreed the URLs would be "easy to scrape." Nobody thought through the actual mechanics.
The bad version:
- Install a Python library, write a scraper, discover the first five sites use JavaScript rendering and return empty HTML.
- Switch to a paid scraping service, set up an account, parse the API response format.
- Get clean Markdown back for 30 of the 50 URLs. Spend an afternoon figuring out why the other 20 failed.
The researcher has a linguistics background. None of this is in scope for the project. The timeline is not long enough to learn web scraping from scratch.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the URLs in your sheet and, through its built-in Supadata web scraping integration, fetches cleaned Markdown content for each page and writes it directly into the workbook — with failure flags for anything that does not come back cleanly.
Scrape every URL in column A using Supadata and write the extracted Markdown content into column B of this sheet — if a page returns no content flag it as 'Scrape failed' in column C
What You Get
- Column B populated with cleaned Markdown text for each successfully scraped page.
- Column C flagged as 'Scrape failed' for any URL that returned empty content, a 404, or a bot block — so the researcher knows exactly which pages need manual follow-up.
- Content with navigation, footers, and sidebars stripped — just the article body.
- Output ready to hand to the data science team without a reformatting pass.
What If the Data Is Not Quite Ready
The Markdown output is too long for the context window you are working with
The fine-tuning pipeline has a 4000-character limit per example.
Scrape every URL in column A using Supadata, write the page title into column B, and write the first 4000 characters of the Markdown body into column C — add a note in column D if the content was truncated
You need to capture the publish date alongside the article text
Dataset versioning requires knowing when each article was published.
Scrape every URL in column A using Supadata, write the title into column B, the Markdown content into column C, and the publication date (if visible on the page) into column D
Some URLs in the list duplicate the same article with different parameters
The strategy team's URL collection process sometimes produces duplicates.
Scrape every URL in column A using Supadata, write the title into column B and the Markdown content into column C — then check column B for duplicate titles and flag any duplicate rows in column D as 'Duplicate title'
You want to combine the scrape with a word-count filter before handing off
The data science team only wants articles over 800 words.
Scrape every URL in column A using Supadata, write the Markdown content into column B and the word count into column C — then flag any row where column C is under 800 with 'Too short' in column D
The pattern: building the quality filter into the same prompt means the data science team receives a pre-screened dataset, not a pile to sort through.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook with a column of article URLs and ask it to scrape and import the content in one pass. If you are also doing a full site audit, look at the spoke on crawling a website URL map to pull the complete page inventory first.