I had 300 old blog posts that were technically fine — good content, decent keywords — but structurally a mess. H1s used as subheadings. No meta descriptions. Schema markup that hadn't been touched since 2019. Images with alt text that just said "image." I wasn't going to fix them by hand. So I built a Python pipeline to do it. Here's the exact system I ended up with: it scrapes raw HTML, corrects the heading hierarchy, generates meta descriptions programmatically, injects schema markup, and outputs clean, SEO-ready files. It handles the boring 80% automatically so you only have to think about the 20% that actually requires judgment. Why This Is Harder Than It Looks The naive approach — "just parse the HTML and fix the tags" — hits three real problems fast: Heading hierarchy is contextual. A post that jumps from H1 to H4 can't be mechanically fixed without understanding the content structure. You need heuristics to infer what was meant to be a section header versus a subpoint.…