1. What you scraped -> Top 50 Skools and got a structured CSV 2. One thing I learned: When scraping skool.com, I asked Claude Code to also get the category of each community — but that info isn't on the listing page. Instead of giving up or hallucinating, it automatically switched to a different Firecrawl format (json with an LLM classification prompt) to visit each community's /about page and infer the category using AI. It chose the right tool for the right job without being told — that's the "agent thinking" moment that surprised me. 1. One use case idea: Website audit agent for clients. Many business owners don't know if their website looks outdated — old fonts, expired copyright year, slow tech stack, broken links. Build a scraper agent that takes a list of competitor or prospect URLs, visits each site, and returns a structured report scoring: design freshness, copyright year, tech stack age, mobile responsiveness signals, and CTA clarity. Sell it as a "digital health check" to agencies or run it yourself as a lead gen tool (scrape local business sites → flag the worst ones → cold outreach with a ready-made audit report). Looking forward to completing Day 3.