Scrape HomeDepot search pages via Chrome debugging port and parse product data.
npx skills add https://github.com/AgentComputerAI/torch --skill homedepot
Please help me install this Agent Skill. Skill: homedepot Source: https://github.com/AgentComputerAI/torch/tree/main/skills/sites/homedepot Command: npx skills add https://github.com/AgentComputerAI/torch --skill homedepot
Direct HTTP requests to HomeDepot are blocked by Akamai, preventing reliable scraping of product listings. This skill bypasses those blocks by using the user's real Chrome instance to fetch and parse the HTML, delivering clean product data.
[data-testid="product-pod"] to extract product fields.?Nao= parameter until no new items appear.Ask the skill to scrape HomeDepot for “drill” and return the product list as JSON.
High-intent search queries and answers about installing and using this skill.
To scrape HomeDepot product data when Akamai blocks direct HTTP requests, you must connect to a real Chrome instance via its debugging port and parse the resulting HTML with Cheerio selectors to bypass TLS fingerprint challenges.
To handle pagination when scraping HomeDepot search results, you iteratively fetch pages using the `?Nao=` URL parameter until no new product items appear, then remove duplicate product pods by their product ID.
To extract HomeDepot product fields, you use Cheerio to parse HTML selectors targeting the `[data-testid="product-pod"]` elements, which contain the structured data for individual listings on the search page.
Scraping HomeDepot via real Chrome uses Puppeteer to connect to the browser's debugging port, avoiding Akamai blocks by leveraging your existing browser session rather than making direct HTTP requests.
You can scrape any HomeDepot search page using this method as long as it is a `/s/` query URL on homedepot.com, allowing you to retrieve clean product data for tasks like market price analysis.
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