How I Automate SEO Using AI Agents

For the past year I’ve run my entire SEO workflow using AI agents for SEO—a chain of specialized agents instead of a stack of subscriptions.

The short version: I now ship audited, keyword-targeted, fully illustrated articles in a fraction of the time it used to take, and most of the tools I use are free or open source. If you only read one section, read the next paragraph.

Here is the punchline. Using AI agents for SEO has stopped being a “write me a paragraph” novelty and become a real production line.

With the right SEO agent chain, one person can diagnose a site, plan keywords, build a landing page, write the article, generate its images, and publish it—without hiring a team. Below is the exact AI agent for the SEO tool chain I use, in the order I use it, so you can copy it today.

Why AI Agents for SEO Changed the Work, Not Just Sped It Up

Two things moved fast in the last 18 months: code generation and agents. Models stopped being chat boxes and started being operators that can read a file, run a command, call an API, and check their own output.

That distinction is what makes real AI agents for SEO so effective — it matters more here than in almost any other field, because SEO is mostly structured, repeatable work: crawl, analyze, prioritize, write, publish, measure.

When that work can be handed to an AI SEO agent that runs end to end, your job shifts from doing the steps to directing the steps. You stop being a typist and become an editor. The leverage is real.

To put numbers on it: a single SEO article used to cost me roughly 4–6 hours — about an hour of keyword and SERP research, two to three hours of writing, 30–45 minutes hunting for and placing images, and another half hour wrestling with WordPress formatting.

With the agent chain below, that same article now takes me about 40 minutes, and most of that is me editing, not producing. Across a month of ~20 articles, that’s the difference between a full-time job and a couple of afternoons.

The old way also meant paying for at least three subscriptions (an audit tool, a keyword tool, and a writing tool) that easily ran $200–400/month; my current stack is mostly free or pay-as-you-go.

The rest of this article is my actual AI agents for SEO stack, broken into the five jobs I run almost every week: site diagnosis, keyword planning, landing page builds, content writing, and article visuals.

My Workflow, Tool by Tool

1. Site Diagnosis: a Free 92-Check SEO + GEO Audit

Before I touch content, I want to know what’s broken. I use the open-source seo-audit skill: github.com/wonfull888/seo-audit.

It runs 92 checks across four dimensions—Technical SEO, On-Page SEO, Content Quality & E-E-A-T, and Local SEO — and it’s built on public, trustworthy sources: Google Search Central, the Ahrefs SEO + AI search checklist, and Microsoft’s AEO/GEO guidance.

That last part matters now: it doesn’t only score you for classic search, it scores you for AI-search readiness (GEO/AEO) too.

Why I like it:

• –It’s free and open source (MIT). No account, no dashboard, no per-seat pricing.
• -It runs where I already work. It’s a skill for skill-enabled CLIs (Claude Code recommended), so it lives next to the agent that’s going to fix the issues.
• –It’s transparent. Every check maps to a public guideline, and it exports a full Markdown report with an executive summary, an overall grade (A–F), and P0/P1/P2 action items.
• -It’s cheap on tokens and gives results comparable to paid auditors.

How to use it:

git clone https://github.com/wonfull888/seo-audit.git
cp -r seo-audit ~/.claude/skills/

# then, inside your CLI:

/seo-audit https://example.com

If you add a free Google PageSpeed API key (25,000 requests per month at no cost—far more than a normal site will ever use), it runs the full 92-check mode, including Core Web Vitals and PageSpeed scores.

Without a key, it still runs an 84-check basic mode. Five minutes later I have a prioritized punch list, and my agent fixes the P0s while I read.

To make this concrete, here’s the kind of thing a single run surfaces. On one client site the report came back graded C (74/100) with a punch list like this:

• –P0 — 9 pages missing meta descriptions; H1 duplicated across the blog template; LCP at 4.1s on mobile (target: under 2.5s).
• –P1—no article or FAQ FAQPage schema on any post; thin intro copy flagged on 12 URLs; images served as unoptimized PNGs.
• –P2—internal links pointing at a /blog?p= redirect chain; missing LocalBusiness NAP signals.

The whole audit took about four minutes and zero dollars. Fixing the three P0 items lifted that site from C to B (84) on the next run, and the mobile LCP dropped to 2.3s after the image fix alone. That’s the loop I run on every site before I write a single word.

Keyword Planning: kwmaster Turns Raw CSVs Into a Page Plan

A diagnosis tells me what to fix. Keyword planning tells me what to build. For that I use KwMaster—a keyword-planning tool I built for myself and currently use privately.

It isn’t public yet (I’m the only user today), but I’m sharing how it works because I plan to release it publicly, and the workflow behind it is something any SEO can copy by hand in the meantime.

So what is KwMaster? In plain terms, it’s a prioritization engine for keywords. The problem it solves is the one every SEO writer knows: you export thousands of keywords from Datafor SEO (your core set plus competitor sets) and then drown in a spreadsheet, with no objective way to decide what to write first.

Kwmaster takes those raw CSVs and runs a three-step pipeline—prepare → AI-score → rank—then hands back a clean, prioritized page-layout plan instead of a wall of rows.

Each surviving keyword comes out tagged with the kind of page it deserves (pillar, cluster article, comparison, or landing page) and a priority bucket.

Why I rely on it:

• –It scores intent and opportunity, not just volume. Output is bucketed into P0 / P1 / P2 / EXCLUDED, so I know exactly which pages to build first instead of chasing high-volume terms I can’t win.
• –It’s competitor-aware. Feed it competitor ranking CSVs, and it folds their winning keywords into your plan, flagging gaps where they rank and you don’t.
• -It ships a TRENDING cut—a cross-cutting view of rising terms—so I don’t miss momentum keywords.
• –The output is decisions, not data. Each row already says what kind of page should target it.

A real example of what it does to a messy export: I recently fed it ~3,200 keywords (1,400 core + 1,800 from three competitors). kwmaster collapsed that into 41 P0 keywords mapped to specific pages, 63 P1, and the rest pushed to P2 or excluded as irrelevant/too hard.

What would have been a full day of manual triage in Google Sheets became a ~10-minute run and a build list I could hand straight to the writing step. The first eight P0 articles I shipped from that plan now pull steady organic traffic — the prioritization did its job.

When will it be public? That’s the plan—I intend to release kwmaster (and a web version so it’s a few clicks instead of a CLI run) so other SEOs can use the same engine. If you’d rather not touch a command line, it’s worth waiting for.

3. Landing Pages: Build Them With Open Design

Once I know which money pages to build, I need them to actually look good. I use Open Design, the open-source, local-first alternative to Claude Design: github.com/nexu-io/open-design.

You describe what you want—make me a SaaS landing page for X with a hero, features, pricing, and a CTA—and it drives a coding agent you already have on your machine (Claude Code, Codex, Cursor, OpenCode, Gemini CLI, and a dozen others) through a real designer’s workflow: a discovery form locks the brief, a direction picker sets a deterministic palette and font stack, and then it builds the page and renders it in a sandboxed preview.

Why I like it:

• -150+ brand-grade design systems built in (Linear, Stripe, Vercel, Notion, and more), so pages don’t come out looking like generic AI slop.
• -137+ composable skills—SaaS landing, pricing page, blog post, dashboard, and so on—each producing a single, editable HTML artifact.
• –Local-first and BYOK. It uses the agent and keys you already have; nothing is locked to one vendor.
• –Export to HTML/PDF/ZIP and keep editing. The output is real code I can ship, not a screenshot.

How to use it: Download the desktop app from open-design.ai or run the web layer locally with pnpm tools-dev, pick a skill and a design system, type the brief, and let your agent build it. I get a polished, on-brand landing page in one sitting instead of waiting on a designer.

A concrete run: last month I needed a comparison landing page. I picked the SaaS landing skill, chose the linear-style design system, and typed a two-sentence brief.

The discovery form asked me four questions (audience, tone, primary CTA, and brand colors), and about three minutes later I had a full single-page HTML artifact—hero, feature grid, a 3-tier pricing table, FAQ, and CTA—rendered in the preview.

I exported it as HTML, dropped it into the site, and made maybe two copy tweaks. A designer quote for the same page would have been $300–500 and three to five days; this was a few minutes and nothing out of pocket beyond the model tokens.

Out of about a dozen pages I’ve built this way, roughly 80% shipped with only minor edits.

4 Content + Visuals + Publishing: GEOWriter

Now the core job: the article itself. This is where I use AI SEO Content Writer—the tool that ties writing, images, and publishing into one pass.

It’s also, conveniently, a genuinely Free SEO content writing tool to start with, which is why I keep recommending it.

What makes GEOWriter different from a generic chat prompt is that it writes from real search context, not from thin air. The workflow is three steps:

1. Enter a keyword. It analyzes the live SERP to understand intent and the structural patterns that are actually winning — so the draft targets the right angle, not just the right word.
2. Generate and refine. It builds an outline and full draft aligned to E-E-A-T principles, extracts LSI keywords and entities, refines the tone so it doesn’t read like a robot, and auto-generates and inserts contextual article images—the visuals are driven by Nano Banana Pro and GPT Image 2, so the pictures actually match the section they sit in. No separate image-sourcing step.
3. Publish or export. One click to WordPress, or export to Google Docs for collaboration. The workflow doesn’t break at the finish line.

Why it earns its place in my stack:

• –It’s a workflow, not an assistant. SERP intelligence → structured draft → E-E-A-T alignment → editorial refinement → visuals → publish, in one place.
• –The images are automatic. For me, sourcing and inserting article visuals used to eat 30–45 minutes per post. GEOWriter removes that step entirely — a typical article comes out with a cover plus 3–5 inline images already placed.
• –It’s built for AI search, not just Google. It structures content to be citation-ready—the kind of writing a GEO agent is designed to surface—so you’re optimizing for ChatGPT and Perplexity-style discovery alongside classic rankings.
• –The pricing is built for real volume, not a subscription trap. GEOWriter is still in beta, and every account gets 10 free articles per month—enough to test the whole loop, or to cover a small blog, without paying a cent. Here’s the part I care about most: if you need to write and publish at scale, you don’t have to buy a monthly subscription. It’s pay-as-you-go at roughly $0.60 per article, so you only pay for what you publish. Most competing tools lock you into $49–99/month plans whether you write 5 articles or 50. Run the math: at 30 articles a month, a typical SaaS writer might cost $99 flat; GEOWriter is about $18 for the same output. For anyone with a large, variable content pipeline, on-demand pricing is simply cheaper.

A real example: I pushed the keyword “best invoicing software for freelancers through GEOWriter. Five minutes later I had a ~1,900-word draft with a SERP-aligned outline, an FAQ block, LSI terms baked in, a cover image plus four inline visuals, and it published straight to WordPress as a draft.

My edit pass was about 15 minutes — mostly tightening the intro and fact-checking two stats. End to end, one keyword to a live, illustrated post in under 25 minutes, at a cost of well under a dollar.

In practice, an AI SEO agent is at the end of my chain that turns a planned keyword into a published, illustrated, search-ready article while I review rather than type.

Putting It Together

Here’s the whole loop, start to finish:

• -Diagnose the site with an SEO audit (free, 92 checks, SEO + GEO).
• -Plan keywords with KWMaster (P0/P1/P2 page plan from your CSVs).
• -Build money pages with Open Design (on-brand, real code, local-first).
• -Write + illustrate + publish with GEOWriter (SERP-driven draft, auto visuals, one-click to WordPress).

None of this replaces judgment. I still decide what’s worth writing, still edit every draft, and still check that the audit’s fix is the right fix.

But the grunt work — the crawling, the spreadsheet triage, the page scaffolding, the image hunting, the CMS wrangling — is gone. That’s the real promise of AI agents for SEO: you keep the strategy and hand off the labor.

If you’re starting from scratch, do this today: run the free SEO audit on your homepage, then register for GEOWriter’s free tier and push one keyword through the full workflow.

Two free tools, one afternoon, and you’ll feel the difference between using AI as a chatbot and using it as a production line.

FAQ

1. Do I need to be technical to use this workflow?

Partly. SEO-audit runs in a CLI, and KWMaster is currently a private CLI tool I built for myself, so basic comfort with a terminal helps for those two—though a public, web-based KWMaster is on the way, and GEOWriter is a fully hosted web app with no setup. If you can register for an account and paste a keyword, you can use the most important piece (the writing, images, and publishing) immediately.

2. Is an AI SEO agent good enough to replace a human SEO?

No—and you shouldn’t want it to. The agent handles execution: auditing, triage, drafting, visuals, and publishing. You still own strategy, editorial judgment, and quality control. The point is leverage, not autopilot. Treat it as a very fast junior team, not a replacement for a senior brain.

3. Will content from an AI SEO content writer rank, or will Google penalize it?

Google ranks helpful, original, well-structured content regardless of how it was produced. The risk isn’t that AI wrote it; it’s that it’s thin and generic. That’s exactly why I use a SERP-driven tool with E-E-A-T alignment and human editing on top—the structure and credibility are what win, and a real editor closes the gap.

4. What is a GEO agent, and why should I care now?

GEO stands for Generative Engine Optimization — structuring content so it gets cited by AI answer engines like ChatGPT and Perplexity, not just ranked by Google. A GEO agent optimizes for that emerging surface. You should care now because AI search is growing fast, and content built for citations today compounds before it becomes the default.

5. How much does all of this cost?

Less than you’d guess. SEO Audit is free and open source. Open Design is open source and BYOK (you bring your own model keys). kwmaster currently pairs with DataForSEO exports (and will be released publicly). GEOWriter gives you 10 free articles a month during beta, then pay-as-you-go at ~$0.60/article—no mandatory subscription, so a high-volume month and a quiet month cost exactly what you produced. You can run the entire diagnosis-to-draft loop without paying anything to start, and even at 30+ articles a month the writing step stays in the ~$18 range rather than a flat $99 plan.

Want to try the writing-and-publishing piece first? It’s a free SEO content writing tool to start—register at SEO Writer and push one keyword through the full SERP-to-publish workflow.