Vibecoding for SEO Specialists: Build Your Own Tool Without a Developer

Picture a normal Tuesday. You need to hand a client a technical audit, and at the last minute you discover that after a template migration, some pages might have lost their H1 or turned into broken links. The sitemap.xml lists three and a half thousand URLs. Checking them by hand is out of the question — opening each page and reading the response code would take days you don't have before the deadline, and the client isn't going to push the date back for this.

Excel has no built-in formula for checking an HTTP status code, you last wrote a VBA macro back in college, and the in-house developer is buried in a release. They have a sprint, a backlog, and three features in flight, and 'check 3,500 links' doesn't make the cut — even if it would take them half an hour between other tasks. Best case, you get an answer in a week, long after the report already went out, checked by hand, line by line, with a couple of tired mistakes somewhere around row one thousand. Ask again a month later, and you'll find the developer has already moved on to something else — your request is back at the bottom of the pile, again.

Sound familiar? This is the classic gap: a task too small to join the development queue, but too fiddly to redo by hand in Excel every single time. Pull every H1 from a list of pages, dedupe a ten-thousand-row keyword list, diff two ranking exports to see exactly what disappeared after a redesign, automatically group queries into clusters, build a cannibalization report for pages competing against each other — an SEO specialist runs into dozens of tasks like this, and not one of them deserves its own ticket in a dev backlog.

That gap is exactly where vibecoding fits — a way to get a working tool built for your specific task, without hiring a developer and without opening a Python textbook.

The term 'vibe coding' was coined in early 2025 by Andrej Karpathy, a well-known deep learning researcher, and it stuck because it accurately captures a new way of working with code. The idea: you don't write the code yourself, and you don't read it line by line. You describe in plain language what you want, an AI coding agent — Claude Code, for instance — writes working code, and you check the result the way any ordinary user would check any app: you run it, look at what came out, try it against real data. Say, for example: 'pull every H1 from this list of pages and flag whichever ones are empty or duplicated' — a couple of minutes later you have a working file, not a code fragment you still need to finish yourself.

If the result isn't right, you don't go digging through the code yourself — you describe, again in plain language, exactly what's wrong: 'this should be reversed,' 'skip the empty rows,' 'add a column with the check date.' The agent doesn't get offended, doesn't get tired, and doesn't ask for extra pay to fix it — but it also won't save you any time if you describe the task vaguely and aren't sure yourself what result you're after.

Vibecoding doesn't turn you into a programmer. It turns you into someone who can afford a tool built for exactly one task — just to get that task off your plate today.

One important caveat: vibecoding doesn't mean blindly copy-pasting whatever the AI wrote straight into production. You can direct the process without being able to read code line by line — but describing the task clearly and honestly checking the result is still on you. That takes discipline, no less than programming skill, just a different kind: not writing code, but phrasing requirements precisely and testing the outcome carefully — and sometimes that turns out to be harder than it looks at first glance.

In practice, vibecoding isn't 'one prompt, one finished tool, instantly' — it's a short loop you run through a few times in a row until the result actually works.

The first version of a script is almost never the final one — and that's completely normal, not a sign you wrote a bad prompt. The agent might not account for pages that return a 200 instead of a 404 (so-called soft 404s), or for a sitemap.xml that doesn't list pages directly but points to other sitemap files instead (a sitemap index) that need to be crawled first. Or it turns out some of those 'broken' links are actually sitting behind a login, not broken at all — another thing that only shows up once you're looking at real data. You notice this not by reading code line by line, but by looking at the output: 'these ten pages are clearly broken, but they're missing from the list — why?' And you phrase that to the agent as a plain observation, no technical vocabulary required unless it's already familiar to you.

How many loops it takes depends on the task. A simple URL-checking script usually becomes workable in two or three rounds. Something trickier — diffing two large exports while accounting for case, stray whitespace, and duplicates — might take a dozen passes. You might find one file uses a comma as its decimal separator and the other a period, and without catching that, the script will silently treat matching numbers as different ones. But each round takes minutes, not days, and the whole time you're testing a finished result against your real data, not writing or debugging code yourself.

Here's what a first prompt for the task from the opening of this article might look like — checking a sitemap for broken links and redirects. Copy it, adapt it to your own needs, and send it to an agent like Claude Code, or to a chat with any sufficiently capable model:

Write a Python script that does the following:

1. Takes the URL of a sitemap.xml file as a command-line argument.
2. Downloads the sitemap and extracts every URL from its <loc> tags — that's the list to check.
3. Sends an HTTP request to each URL and records the final status code after following all redirects.
4. If the status is 404 or any other 4xx/5xx error, logs the URL, status code, and a short error description.
5. If there was a redirect (3xx), separately logs the original URL, the final URL, and the intermediate chain if it's longer than one hop.
6. When finished, saves two CSV files — errors.csv (broken links) and redirects.csv (redirects) — with clear column headers.
7. Prints a short summary to the console: total URLs checked, number of errors, number of redirects.

Requirements:
- Use the requests library; handle timeouts and connection errors — if a URL doesn't respond within 10 seconds, log it as an error instead of crashing the whole script.
- Add a 0.5-1 second pause between requests so you don't hammer the site.
- Print progress to the console — how many URLs checked out of the total.
- Add a short comment at the top of the file with an example command showing how to pass the sitemap URL.

After the first run, don't trust the output blindly — run the script against a small sample, say 50 URLs from your real sitemap, and spot-check a handful of rows by hand: open a 'broken' link in your browser and confirm it really is a 404, not a temporary network hiccup. Check the redirects too: sometimes a page technically responds with a 200, but after a few hops it lands somewhere completely different from what was intended — worth spot-checking a sample of those by eye as well. If everything checks out, go ahead and run it against the full list — all three and a half thousand URLs from the opening example, if that's what you're dealing with.

We're not saying this in the abstract: SEO Control itself was largely put together exactly this way — in an ongoing dialogue with an AI coding agent, with a human reviewing and testing every step. The concrete example is right in front of you. The article-publishing system this very text runs on, with its block schema (paragraph, list, table, quote, a code block with a copy button, a diagram, an FAQ block), wasn't a ready-made template out of the box. It was described in plain language, turned into a working structure, tested on real text, found lacking something — a diagram variant for cyclical processes, needed for exactly this article — and extended the same way. Every new capability — say, that same copy button next to a code block — got described in words first, then checked: does it copy the full text, does the formatting survive, does it behave the same way in both language versions.

That doesn't mean the entire product was vibecoded without a single hand-written line, and it doesn't mean you can just talk a complex multi-tenant SaaS system into existence over one evening. But a substantial share of the pieces users actually touch — including parts of AI Control, the product that tracks how a brand and its competitors show up in answers from ChatGPT, Perplexity, Copilot, Gemini, and Google AI — went through exactly this loop: describe the task, get code, test it like an ordinary user, describe what's wrong, repeat as many times as it took.

Even the less trivial pieces were built the same way — just with noticeably more iterations and much more careful testing at every step. Take the component that runs long checks against AI systems in the background: the job lives in the database, not in process memory or a browser tab, so closing the tab doesn't cancel the run, and a server restart doesn't lose unfinished work — it just gets picked back up. And if the AI system being checked times out or returns an error mid-run, the job doesn't just hang forever — it gets a clear status and can be restarted. If you're curious how that piece actually works under the hood, we have a deeper technical write-up: how we built the AI Control capture queue.

The more consequential the task, the more it matters to remember: code the agent wrote is code you're responsible for, not it. The agent never signed an NDA with your client and won't be the one explaining things if a script accidentally overwrites a working file full of current rankings, or deletes a needed sheet from an export. The more people who end up relying on the result — not just you, but colleagues or the client directly — the higher the cost of a careless mistake. A few rules are worth following without exception, no matter how simple the task looks:

• Never run a script that deletes, overwrites, or bulk-modifies anything against production data — test it on a copy or a small sample first.
• Never paste real passwords, access tokens, API keys, or a client's credentials into a prompt — use placeholders, and swap in the real values locally, outside your conversation with the agent.
• Spot-check the output instead of trusting it blindly: open a handful of result rows by hand and compare them against reality before the report goes to a client.
• Treat the first working version as a draft, not a finished tool — especially if you plan to run it regularly rather than once.

None of this is specific to vibecoding — the same rules apply to any script you downloaded from a forum or got from a freelancer. The difference is that vibecoding makes producing such a script so fast and cheap that the temptation to skip the check is higher than usual, while the cost of a mistake in SEO data a client is about to base decisions on is entirely real — anywhere from a wrong report to a real site problem going unnoticed. Make it a habit to check exactly what a script was tested against before folding it into a regular process: one clean run on test data doesn't prove it'll survive contact with the real oddities of a live site.

Vibecoding isn't the only way to close the gap between 'too small a task for a developer' and 'too tedious a task for Excel.' An SEO specialist usually has two other paths available: a ready-made no-code builder (services with visual workflows and prebuilt templates), and the classic route of hiring a developer, in-house or freelance. Sometimes a third option shows up alongside those two — handing the task to a freelance analyst who writes the script for you — but that's really just hiring a developer again, one-off and informal. All three work — the question is what matters most for the task at hand: speed, flexibility, cost, or who's going to maintain the result afterward.

If the task is routine and a no-code service already has a template for it, use that — don't reinvent it with vibecoding. If the task is nonstandard but not especially complex and you need it fast, vibecoding usually wins on both speed and total cost. If you're talking about a system meant to live for years, grow new features, and be understood by someone other than you, hiring a developer is still the right call — and vibecoding there is less a replacement than a fast way to build a prototype you can hand to a developer as a reference for exactly what's needed.

If you've never tried vibecoding before, don't start with a client deadline on the line — try it on something low-stakes first, with no time pressure. That goes even for people already comfortable using AI agents for writing text — coding for a real task is a somewhat different skill, worth getting used to at a small scale before it actually matters. Here's a reasonable place to start:

• Install one AI coding agent (Claude Code, for example) and try it on a test project, not straight on a client's production data.
• Pick a small, clear-cut task with an obvious result for your first attempt — checking a URL list for 404s, or pulling every H1 from a sitemap.
• Describe the task to the agent the way you'd brief an intern: what goes in, what should come out, and what the constraints are.
• Run the result on a small sample and manually check at least a few rows by hand before you trust the script with the whole list.
• Keep the working script and the prompt that produced it — next time you'll just ask the agent to extend the existing code instead of starting from zero.