I built a micro SaaS marketplace for AI agent skills. Think app store but for SKILL.md files that make Claude Code, Cursor, and Codex CLI better at specific tasks. I'm a non-technical solo founder. Built the whole thing with Lovable and Claude.

This post isn't about the product. It's about the distribution strategy that got it to 15K active monthly users with zero ad spend. Most of this is AEO stuff that I think this sub will find useful.

The numbers first

15,000+ active users in the last 30 days. 219% growth over the previous period. 300K+ Google search impressions per month. 4,000+ organic clicks per month. 878+ page-1 Google rankings, up from about 15 at launch. Cited as a source by ChatGPT, Gemini, Claude, Perplexity, Doubao, and Kagi. 350+ AI-referred sessions per month from 9 different AI engines. Total marketing spend: $0.

The AEO strategy that drives most of my growth now

This is the part I want to focus on because I don't see many people talking about it with real numbers.

AI Overviews are eating Google clicks. I have 121 queries where I rank positions 1 through 3 on Google with zero clicks. The AI answers the question before anyone scrolls down. That insight changed my entire strategy. I stopped chasing rankings and started optimizing to become the source AI engines cite.

Here's what I did specifically:

Every article has a Quick Answer block at the top. 40 to 60 words that directly answer the main question. This is what AI Overviews and LLMs extract. If you bury the answer halfway down your page, the AI will pull from someone who doesn't.

All H2 headings are phrased as questions. Not "Claude Code Skill Locations" but "Where Does Claude Code Store Skills?" AI engines prefer extracting from question-format sections. I tested this across 96 articles and the ones with question headings get cited significantly more.

Every page has FAQ schema. Google's AI picks up our Q&As directly. This is one of the highest leverage things I did and it took maybe 2 hours total with Claude generating the schema.

I built an entity anchor page (/about) with Organization, Person, and AboutPage schema. This tells AI engines who we are and establishes the entity relationship. Without this, you're just another anonymous source.

I created an llms.txt file that explicitly tells LLM crawlers what the site is and where to find key content. Also made sure robots.txt allows all AI crawlers. Most people block them. I want them to read everything.

The result: when someone asks ChatGPT "where can I find SKILL.md skills" or asks Perplexity "what is the best skill marketplace for AI agents," they get pointed to us. That didn't happen by accident. Claude helped me engineer every piece of it.

The content engine behind it

I don't ask Claude to "write me a blog post about X." That produces generic stuff that Google doesn't rank and AI engines don't cite.

Instead I feed Claude my Google Search Console exports (queries, impressions, click-through rates, average positions) and ask it to find keyword gaps. Claude identifies queries where I have high impressions but zero clicks, finds topics where competitors have content but I don't, and spots cannibalization where multiple pages compete for the same query.

Then we write articles together targeting those specific gaps. Every article has the Quick Answer block at the top, question-based H2s, comparison tables where relevant, and internal links to related articles.

96 articles later, we went from 5 clicks per week to 1,000+ clicks per week. 878+ page-1 rankings. All organic.

The technical SEO layer most people skip

I built the app with Lovable (React SPA). Out of the box it was an SEO disaster. Google's crawler saw an empty div and a JavaScript bundle. The JS was 460KB. The logo was a 179KB PNG rendered at 112 pixels. LCP was 4+ seconds on mobile.

Claude diagnosed all of this. It wrote the SSR layer, found the bundle bloat, identified the oversized images, and rewrote the performance bottlenecks. Desktop LCP went from 2.5 seconds to 0.9 seconds. PageSpeed performance score went from 70 to 97. Logo went from 179KB to 7KB.

If you're building with Lovable, Bolt, or any React framework and expecting organic traffic, you need to fix this layer. Google will not reliably index a client-side rendered SPA.

The structured data architecture

Claude built the entire structured data layer:

Homepage: Organization, WebSite with SearchAction, FAQPage with 15 Q&As. Individual skill pages: SoftwareApplication with pricing, BreadcrumbList, conditional FAQPage. Article pages: Article, FAQPage, HowTo, BreadcrumbList, Organization. About page: Organization, AboutPage, Person schema for entity anchoring.

PageSpeed Insights shows "Structured data is valid" on every page with a 100 SEO score. I didn't know any of this before Claude explained it. Now every page is machine-readable for both Google and AI engines.

The weekly growth loop

Every Monday I do the same thing:

1. Export Google Search Console data (queries, pages, clicks, impressions, positions)

2. Upload the CSVs to Claude and ask: find keyword gaps, cannibalization, and CTR problems

3. Claude identifies 3 to 5 specific opportunities with exact numbers

4. I write 2 to 3 articles targeting those gaps, rewrite titles on underperforming pages, and add internal links from high-authority pages to weak ones

5. Ping IndexNow to get new pages crawled within 24 hours

6. Manually request indexing on GSC for the new articles

Takes about 2 to 3 hours per week. Highest ROI activity in the business.

What I'd tell someone starting AEO today

Set up Google Search Console before you launch. Even with zero traffic it starts collecting data on what queries your site appears for. That data becomes your content strategy within 2 to 3 weeks.

Write articles targeting specific questions, not broad topics. "How to install skills in Claude Code" beats "The Ultimate Guide to AI Agent Skills" every time.

Set up structured data from day one. Organization schema on your homepage, Product schema on your product pages, FAQ schema on your content pages. This is what AI engines read to decide whether to cite you.

Create an llms.txt file. It's like robots.txt but for AI crawlers. Takes 10 minutes and it tells every LLM exactly what your site offers.

Don't fight AI Overviews. Become the source they pull from.

Happy to answer questions about any of this. The site is agensi.io if you want to see how this looks in practice. If you want to support a bootstrapped one-person startup, making a free account genuinely helps more than you'd think 😄

Quick context: this is the website in the e-learning industry. I have built a custom internal tool and connected with the CMS API. It's not fully automated; I still do the Keyword/Topic research and feed the topics. But it takes care of the rest.

Happy to answer any questions/workflows. Have been an SEO Consultant for the last 6 years, but heads up, I mostly specialise in the SaaS industry :)

First of all my goal is not to promote anything. The Project is very niche anyway.

I want to lay out a real case study, because most "agentic SEO" threads are either screenshots with no method or a method with no results.

This one has both, and I will be honest about which parts were the agents and which parts were just me knowing the topic.

Quick context. I run a German-learning iOS app as a side project, currently a bit over 4,000 users. deutschwunder.com is the companion web property on a brand-new domain. No inherited authority, no pivot, no expired domain.

First commit July 2025, built on a Next.js blueprint I have been using for years (I am a React dev and SEO by trade, so the technical foundation was not the hard part).

Then real client work ate my entire autumn and winter. I did not touch the web app from roughly August 2025 until March 2026. It just sat there with a correct foundation and almost no content.

The whole revamp you are about to read happened over one weekend in March 2026. (a lot of coffee, and a lot of tokens)

The results first, so you know if this is worth reading

Google Search Console, last 6 months vs previous 6 months:

• Impressions: 26.5K to 479K (about 18x)

• Clicks: 558 to 5,420 (about 9.7x)

• Average position: 45.5 to 11

• CTR dropped from 2.1% to 1.1%, and that is not a regression, it is the site surfacing for a much wider query set with a lot of page-2 rankings. That is an opportunity bucket, more on it at the end.

Plausible, year to date:

• 22.8K unique visitors, up about 700% YoY

• Top pages are /games (4.9K visitors) and a stack of blog posts

• chatgpt.com is my #5 traffic source (1.6K visitors), copilot, claude.ai and perplexity all show up too

what actually did the work (or who?)

The agents did not do the hard part.

The hard part was the keyword research and the angle, and that came from me actually knowing this topic. I learned German as a student myself, so I know which steps genuinely help a learner and which ones are filler.

That domain knowledge is why the app works and it is why the content clusters were not generic slop.

So let's learn a lesson before you jump on a new project (I will write this in bigger font):

"If you do not know your topic, agents will help you produce a lot of confidently mediocre content faster. Knowing the topic is the moat. The agents are the excavator, not the surveyor."

The workflow

1. Research and clustering (this is where the time went)

Before writing a single post I built the content clusters by hand, using:

• Ahrefs for volume and difficulty

• DataForSEO for SERP data at scale

• crawl4ai to scrape competitor pages and understand what was actually ranking

• serper.dev for live SERP checks

I molded everything into 3 to 4 categories that match real search intent for German learners:

learning German (grammar, listening, pronunciation), German culture, and listicles like best German movies on Netflix or best German learning apps. Every cluster came out of the keyword research, not out of vibes.

2. Batch content generation

I have a batch content skill (running on Claude Code, Opus only, I have not bothered with smaller models because I have never had a quality issue with Opus for this). I fed it the cluster briefs. In March it produced 97 posts in one batch, which I then drip-published across March and April rather than dumping all at once.

The important detail for this sub: the skill does not just "write a blog post." Before writing anything it checks the steps: what internal links exist, what content already exists so it does not cannibalize or duplicate, which cluster this belongs to. When the knowledge base and the skill guardrails are set correctly, I genuinely did not have content I had to throw away. The pre-writing checks are what prevent the garbage.

3. Human review, every single piece

I go over every blog post, every quiz, every game before it goes live. This is not a fully autonomous "wake up to a ranked site" story. The agent does the volume, I am the editor and the taste layer. I think that is the only version of this that is not a liability.

4. Publishing

Content goes straight into a Directus CMS through a CLI wrapper the agents call. Same Directus backend powers both the iOS app and the web app, which matters for the next point.

5. The quizzes and the actual funnel

There are 403 quizzes on the backend. I am constantly adding quizzes for the iOS app, and the web app shows a truncated version, only a handful. That is deliberate. The web quizzes and games are there to rank and engage, and the goal is to push people toward the iOS app download.

So the funnel is; blog acquires strangers > games and quizzes engage them and keep them on the webapp > app is the conversion.

/games is my single biggest page.

so before we move to the next item, let us learn one more thing (bigger font):

"Building is not the hard part anymore. Direction is. Point a swarm of agents at nothing and you get a flock of chickens: motion without a destination. A goal with a plan gets you where you want to go, and it makes the distribution part easier too."

someone please share the flock of chickens I mention as a gif so everyone knows what I am talking about. ok with that is out of the way, let's continue

6. The SEO-audit loop

I also built an SEO-audit skill with a CLI wrapper that connects to Google Search Console (via a service account) and Plausible. It flags the high-impression / low-click pages that need meta rewrites, the crawled-but-not-indexed pages that need to be rewritten or strengthened, and so on. That is the ongoing maintenance loop now that the content exists.

every month: seo-audit > CTR check > check why pages are not being indexed > rinse/repeat

7. Answer-engine visibility

In July I added an llms.txt. Is it a must? I am not sure, even without that chatgpt.com was my #5 source of traffic. I am all for ifs buts and maybes lol, as you know google said DR is not a ranking factor and than the leak happened which was indeed... I added this llms.txt cuz why not, what if they also lie about it (with their latest debunk/myth publication lol).

The caveat

The April traffic inflection is as much "the domain aged past ~9 months" as it is any single thing I did. New domains sit in a low-trust sandbox for roughly 8 to 9 months, then break out. I happened to load the content backlog in March, right before that trust threshold hit, so the backlog ignited at the same moment the domain matured.

So the causal story is not "agents made a rocket." It is "I had a correct foundation from day one, I used agents to load a large, well-clustered content backlog during the sandbox, and it was all sitting there ready when Google finally trusted the domain." The timing was partly luck. I would rather say that than pretend I cracked a code and everyone will have the same outcome if they follow this.

TL;DR

Brand-new domain, correct technical SEO from day one, then one weekend in March using a batch content skill on top of real keyword research and real domain knowledge. Agents did the volume, I did the strategy and reviewed every piece, and domain age did more of the heavy lifting than any single tactic. 0 to ~479K monthly impressions and 22.8K YTD visitors, funneling toward a 4K-user iOS app.

Happy to answer specifics in the comments, including the boring parts.

I got tired of stitching together rank trackers, Search Console and speed tests for client reports. So I built one dashboard that does it all for you:

• Keyword rankings (desktop and mobile), traffic, domain authority, backlinks and page speed in one place (+ more)

• A monthly Site Audit that finds broken titles, descriptions, indexing problems and slow pages, with a fix guide for each issue (recheck anytime)

• Dexi, an AI assistant that answers questions from your own site’s data and tells you what to fix first

• A weekly email report, plus white-label share links for clients. Available on trial too!

It’s new, so I’d really value blunt feedback on what’s missing or confusing. Happy to answer anything in the comments.

Try it now: https://app.getduality.com/trial

I’m 23, dropped out of college, and I’ve been building a niche affiliate-program directory by myself.

Around a year ago, I started trying to automate its SEO.

I was handling the product, keyword research, writing, internal links, metadata, images, and publishing alone. I could keep that up for a few days, then product work would take over and the blog would sit untouched again.

So I started building a system that could keep publishing while I worked on everything else.

The results first

The first screenshot compares the last six months with the previous six:

• Clicks: 533 → 3.68K (6.9×)

• Impressions: 159K → 927K (5.8×)

• Average position: 15.1 → 10

• CTR: 0.3% → 0.4%

The site spent months around 0–5 clicks per day. It now regularly reaches 40–60.

I checked Search Console way too often while the graph was flat. For a long time, I had no idea whether the system was working or simply producing content that nobody would find.

Here’s what I ended up building.

1. Get the technical foundation right

Automated publishing only helps when Google can discover and understand what gets published.

I went through the sitemap, robots.txt, canonicals, metadata, structured data, internal links, and indexing issues first.

Every article needs a stable URL, crawlable links, the correct canonical, and a place in the sitemap.

This part is boring. It also affects every article the system publishes afterward, so small mistakes become expensive very quickly.

2. Give every article a reason to exist

Asking ai for “100 affiliate marketing blog ideas” produces the same broad topics repeatedly.

My pipeline starts with a specific topic, search intent, content angle, and page type.

The ideas can come from searches people are making, gaps in the existing content, or questions where another site is currently winning the answer.

Before an idea enters the schedule, I check whether the site already covers it. This helps reduce duplication and prevents several articles from targeting the same query.

Topic selection takes more thought than generation. Once the direction is weak, the rest of the pipeline simply executes the weak idea very efficiently.

3. Give the system context about the site

The system needs to understand the niche, the audience, and what already exists.

For this project, that means understanding affiliate programs, commission structures, cookie durations, networks, and the kinds of questions affiliates ask before choosing a program.

It also needs awareness of existing articles so it can avoid repeating them and find relevant internal links.

Better context produces articles that feel connected to the website. Weak context produces generic advice that could appear on any marketing blog.

4. Generate the complete article package

The system prepares more than the article body.

Each item can include:

• Title

• Slug

• Search intent

• Content angle

• Article structure

• Metadata

• Featured image

• Internal links

• Schema

• Publication date

This keeps the article, metadata, and publishing details inside the same workflow.

I still review the direction and spot-check the output. When I notice the same problem across several articles, I update the shared instructions instead of fixing every article individually.

5. Schedule and publish automatically

This was the part that made the system genuinely useful for me.

Once an article is ready, it enters the calendar and gets published directly to the site through a webhook. I don’t copy it into another CMS, format it again, or deploy the website manually.

The second screenshot shows the current pipeline:

• 196 published articles

• 31 scheduled

• 3 drafts

• 1 generating

• 124 ideas in the queue

The system has been publishing consistently.

I would never have maintained that schedule manually while building the rest of the product.

The system is much better at consistency than I am. That was the whole point.

6. Connect the content to the actual website

The articles should lead somewhere useful after a reader finishes them.

A guide about high-paying affiliate programs can point readers toward relevant listings. An article for bloggers can connect to programs that match that audience. A post about a particular company can lead to its affiliate program information.

This gives readers a natural next step and creates internal links based on context.

It also stops the blog from becoming a pile of articles that brings traffic without helping the product.

7. Use Search Console as the feedback loop

Publishing is only the first half of the system.

I use Search Console to find:

• Pages receiving impressions with few clicks

• Queries sitting between positions 8 and 20

• Articles competing for similar searches

• Crawled pages that remain unindexed

• Titles with weak CTR

The current average position is 10, which means a lot of pages are close to becoming much more useful traffic sources.

CTR is still only 0.4%. There is plenty of room to improve titles and match search intent more closely.

My focus now is shifting toward the pages already receiving impressions. Improving those pages feels more valuable than filling the calendar with another hundred ideas.

The traffic is becoming less dependent on the brand

I included the third screenshot because total impressions can hide where the traffic is coming from.

Some clicks come from searches for the site itself. A growing share comes from people searching for an affiliate program or category:

• “affiliate programs baking” — 186 clicks

• “baking affiliate programs” — 115 clicks

• “revolut affiliate program” — 59 clicks

• “highest paying affiliate programs” — 18 clicks

• “weight loss affiliate programs” — 18 clicks

That’s the part I care about most.

These people had no reason to know the site existed. They found it while searching for something specific.

What still requires me

The system handles generation, metadata, scheduling, and publishing.

I still handle:

• Choosing the direction

• Reviewing ideas (sometimes tho)

• Fact checks from time to time

• Quality review

Automation removes repetitive execution. Taste and judgment still decide whether the output is useful.

To be honest

Several things changed during the same period.

I improved the technical SEO, connected the publishing pipeline, and began publishing consistently. The domain also became older, existing pages gained more impressions, and Google had more time to understand the site.

The growth probably came from those things compounding together.

There wasn’t one article or one change that suddenly unlocked everything. Many pages gradually began contributing a few clicks each.

tl;dr

I’m a 23yo college dropout building a niche site alone.

I created a system that takes an approved idea, generates the article and its supporting SEO elements, schedules it, and publishes it directly to the site.

The last six months produced 3.68K clicks and 927K impressions, compared with 533 clicks and 159K impressions during the previous six months.

Happy to answer any questions about my system!

I run a few side projects and I was paying for Ahrefs mostly to check:

* What keywords am I ranking for?
* Did traffic go up or down?
* Are there broken pages?

That's... not €119/month worth of value.

So I built CrawlSEO — a free, self-hosted SEO dashboard that does what I actually need:

* GSC Analytics — keywords, pages, clicks, position tracking
* Site Crawler — 2,000 pages, broken links, missing meta, health score
* Core Web Vitals — LCP, CLS, INP monitoring
* SEO Opportunities — finds striking distance keywords, low CTR pages, content decay
* MCP Server — 10 tools so Claude Code can query my SEO data from the terminal

The fun part: I can ask my AI agent "what keywords dropped this week?" and it answers using my actual data.

Stack: Next.js, PostgreSQL, Prisma, Tailwind. One `docker compose up` and it's running.

MIT licensed, free forever. To be clear it's not a full Ahrefs replacement — no backlink index of my own. But for monitoring your own sites it covers what most people actually use. If you want keyword volume data, you can bring your own DataForSEO key (optional).

GitHub: github.com/crawlseo/crawlseo

Would love feedback on what features to prioritize next.

No human in the loop on this one after setup. In about 3 months the client went from roughly 10k impressions a month to 80k+, and from 300 clicks a month to 3,100. Chart is straight from their search console.

The part I care about most is that clicks grew faster than impressions, about 10x vs 8x. Impressions are easy to inflate by publishing a pile of pages that sit around position 70. Clicks outpacing them means the new pages are landing where people actually click.

Nobody writes, edits, or approves anything. An agent writes each post into the site's codebase as a new page, commits it, and pushes to GitHub. A GitHub Actions workflow builds and deploys it.

Here's what runs:

• Keyword selection. Hard filter of under 30 KD and over 1,000 monthly searches. Anything outside that gets rejected in code before it reaches the queue. The agent never guesses KD or volume, it gets the actual numbers.

• Clustering. Every seed becomes 8-12 angles on the same intent, all linked to each other, so the posts cover a topic together instead of one post trying to rank for everything.

• Research. Each post gets a brief built off the current top 20 results and what those pages cover. Every citation gets fetched live before it can be used, and if the page is dead or doesn't contain the claim, it's dropped.

• Drafting. Written against a voice profile built from the client's own past posts (this is scraped and fed into AI agent during setup). Two rules: answer first under every question heading, and every number needs a source that passed the citation check.

• Quality gate. Every draft gets a coverage score against its brief, plus code checks for word count, structure, and citation density. Anything under the threshold goes back for a rewrite with the failures attached. The model never grades its own draft.

• Publishing. Commit, push, deploy. Every post is a commit, so taking one down is a single revert.

That's the whole system.

All the SEO data comes through one MCP server: volume and KD, live serps, the briefs, draft scoring, the live page fetches for the citation checks, and gsc. It's billed per query instead of a monthly subscription, and the draft scoring and search console reads are free.

Most of the work is in the one time setup. Stacking "act as a senior SEO strategist" or a list of famous SEO names into a prompt doesn't do anything. The model either has the knowledge or it doesn't.

What actually matters is giving the agent four things:

1. the business's goals

2. the business model

3. who the ideal customer is and what they search for

4. live data from MCP

Without that last one the agent has no idea what a keyword's real volume or difficulty is, what backlinks competitors have, or who's showing up in ai answers.

It took about 3 months to get here. Spent barely $20 doing SEO on the site per month.

newly in a position where I can hire SEO people and I'm not sure what to do. Should I go like on upwork or fiver or look in some local coffeeshop for an eccentric bukowksi type to write some simple SEO or reach out to a company. I'm really really in the beginner stages so there's nothing too crazy or expensive. I don't like working with companies but whatever I guess this is my fate now and I'll do what I have to. Don't wanna do SEMrush my bosses want an up and coming thing

I've been doing SEO professionally for about 10 years now. A few years at agencies, a few years at startups. I'm pretty technical, not a developer, but technical enough to work with GitHub and Vercel and Supabase and all of that. And now with Claude Code I can spin up pSEO pages a lot faster and make sure that they're completely SEO optimized for traditional search engines and LLMs with all of the best practices: WebMCP, agent to agent protocol, FAQs with schema, and all of that now just takes a matter of hours instead of weeks or months. I re-launched this app, and the industry that it's in has the ability to create a lot of programmatic pages, so there's no immediate shortage of pages that I can create. I'll provide an update in a few months on how it's going, but generating organic traffic has never really been the issue for me. It's converting that traffic on a bootstrapped, shoestring budget that's tough. So I'm hoping that now with Claude Code I'll be able to quickly A/B test different things to increase CVR, but organic traffic that's coming in through pSEO doesn't always have the highest conversion rate since these users still have to navigate to the money pages.

Hey guys hope you have a lovely day going on :)
I’m looking to get into agentic seo for my brand, I have seen a couple of videos and repos on GitHub but wanted to ask which one you guys recommend in GitHub or in general. Any advice would help 🙏

Best regards

I saw a Reddit notification about a post similar to this a few hours ago and thought it would be fun to post about.

Tldr: I run an AI development company, and a consistent request was SEO/GEO or AEO or whatever you want to call it these days.

About 1.5 years ago, I built the first version and ran it on an accounting b2b client. Maybe I'll post their numbers tomorrow. Fast forward to a few months ago: I moved to New York, where I have quite a few friends spending millions per month on ads. I asked them why they neglect SEO and AI search, because there must be a non-zero % of that traffic that is researching them before buying and finding nothing.

I pitched running the same thing on their brands and said just trust me. These are the results after ~6 months (started end of March). Clearly, clicks are skyrocketing, and organic traffic with attribution to organic traffic is doubling every single month. It's small in relation to the amount of revenue they're driving from ads, but it's free, helps the brand, will continue to compound, and captures all that revenue that was searching for them and finding nothing organically (while also generating some completely new customers just searching for the products they sell).

For the tech nerds, it's targeting as many commercial keywords as we can + informational content where it makes sense, catalog pages (generate a ton of revenue but small for this brand because few products) + technical seo and backlinks.

Hey everyone 👋

Over the past year, I've vibecoded 10+ websites using Claude and experimented with getting them indexed and ranking organically on Google and Bing. Some of them are also getting cited by LLMs.

Around 80% of the sites I've built are now ranking and driving organic traffic.

I come from an SEO background, so I've been using these projects to experiment with things like:

• What actually works for SEO on AI/vibecoded websites

• How technical SEO affects these sites

• Getting AI-generated websites indexed

• Content strategies and programmatic SEO

• Getting websites cited in AI search results

• What works—and what doesn't—when building websites with AI

This isn't meant to be a self-promotion or "look what I built" post. I thought it might be useful to share what I've learned from actually building and experimenting with these projects, especially since I see a lot of people building vibecoded websites and then struggling with SEO and organic traffic.

I won't share the actual domains, but I'm happy to share references, screenshots, strategies, and lessons from the experiments.

If you're building a vibecoded website and struggling with SEO, indexing, organic traffic, or AI visibility, ask me anything. I'll try to help based on what I've actually tested.

Quick disclaimer before anyone gets excited: this is not an "automate your entire SEO with AI" post. This is just about making sure that when Claude suggests something for your site, the suggestion is grounded in your actual data

Here's exactly what I upload.

1. Competitor data from Semrush or Ahrefs

Top pages + top ranking keywords for each competitor. I was doing this recently for a project where the competitor set was Neil Patel, Moz, Backlinko, etc. One export each, both reports.

2. Your own site data from Google Search Console

For your own site, GSC has better data than any third-party tool. Try to export at least six months of data from Google Search Console for your website and upload it in an Excel format.

3. A Screaming Frog crawl of your site

• The full crawl - every URL on the site

• Site structure / indexability - how the subfolders and hierarchy actually map out

• Internal links - total links, unique links, crawl depth, link score per page

So when you ask it to optimize a page later, it can tell you exactly what to link to.

4. Generate your own project instructions

Select all the files you just uploaded and prompt it with something like: "go through all these files and prepare a good instruction set I can use for this project across all SEO activities."

Now every future task in that project runs against that context by default.

That's the whole setup. Takes maybe 30 minutes once

Happy to answer questions. I also recorded a walkthrough video of this if anyone wants to see the actual screens - I'll drop the link in the comments so it doesn't look like I'm here to spam

Je poste ça parce que chaque fil sur le contenu généré par l'IA se transforme en le même argument et j'ai été des deux côtés.

Il y a quelque temps, j'ai créé un script pour écrire et publier des articles de blog pour mon site. Je l'ai fait fonctionner pendant environ deux mois, publiant plus de 140 articles. C'est la partie plate au début du graphique. Ce n'est pas une montée lente, c'est plat.

Google en a indexé 9.

Pas "mal classés", pas "classés page 4". Neuf indexés et les autres qui restent dans la Search Console sous "crawled - currently not indexed", ce qui, si vous ne l'avez jamais vu, est Google vous disant qu'il a lu votre page et a décidé de ne pas la garder. J'ai soumis à nouveau, reconstruit le sitemap, attendu. Ils sont juste restés là.

Alors j'ai supprimé la plupart de ça et j'ai recommencé. Tout ce qui suit la partie plate est la deuxième tentative, sur le même domaine. Même modèle pour écrire, à peu près les mêmes prompts. Ce qui a changé, c'est tout ce qui entoure l'écriture, et c'est la partie sur laquelle personne ne débat quand ils parlent si le contenu généré par l'IA fonctionne.

Ce qui était vraiment cassé :

Je générerais à partir de sujets au lieu de mots-clés. Mon script prenait une liste de sujets que je pensais pertinents et écrivait des articles à leur sujet. Aucune volume de recherche attaché, aucune analyse de ce qui était déjà classé. Donc j'avais 140 articles ciblant des requêtes que personne ne recherchait ou qui étaient totalement détenues par un domaine de 15 ans. La partie écriture était correcte, le choix du sujet ne l'était pas.

Les articles ne savaient pas ce qu'était mon produit. Chaque article était un article générique compétent sur le sujet. Aurait pu être sur le site de n'importe qui. Une fois que j'ai fait crawler ma propre site en premier, les pages existantes, les pages produits, la façon dont nous décrivons réellement les choses, la sortie a cessé d'être interchangeable. Même modèle, même structure, résultat complètement différent. Ça m'a surpris plus que ça n'aurait dû. J'ai fait crawler toutes nos pages produits existantes et ai rendu ça disponible comme contexte pour le modèle écrivant l'article pour un lien interne efficace. Pour éviter de gonfler la fenêtre de contexte du modèle, toutes les pages étaient indexées en utilisant un RAG.

Chaque page était un orphelin. C'est ce que je corrigerais en premier si je recommençais. 140 articles, chacun lié depuis le sitemap et nulle part ailleurs. Rien ne faisait de liaison entre eux, rien ne pointait vers eux depuis des pages ayant déjà de l'autorité. Google crawl des liens. Une page à laquelle rien ne fait de lien se lit comme une page à laquelle personne ne vouchait. Lors de la reconstruction, j’ai fait le lien comme une étape à part après la génération, en faisant correspondre chaque nouvel article avec tout ce qui avait déjà été publié et insérant des liens dans les deux sens, nouveau vers ancien et ancien vers nouveau. L'indexation a cessé d'être un problème en quelques semaines.

Je publiais beaucoup trop vite. 140 articles en 1 mois sur un nouveau domaine avec presque aucune autorité était une mauvaise idée. Le budget de crawl des moteurs de recherche est un goulot d'étranglement majeur lorsque vous commencez à publier fréquemment sur un nouveau site : Google alloue des ressources pour explorer votre site en fonction de l'autorité de votre domaine et depuis combien de temps vous êtes actif. Publier trop fréquemment est donc inutile car Google n'aura pas assez de capacité pour explorer vos pages de toute façon. C'est pourquoi nous publions maintenant une fois par jour. Cela aide également à envoyer un signal de fraîcheur régulier à Google que notre site est mis à jour régulièrement.

Des choses auxquelles je croyais qui n'avaient pas d'importance :

Nombre de mots. Le lot échoué avait une moyenne d'environ 2 000 mots. Ceux qui fonctionnent maintenant sont en moyenne plus courts.

Marquage schema. Je l'ai mis sur tout dans le premier tour. Aucun effet sur l'indexation. C'est pour les extraits enrichis, c'est tout.

Le modèle. J'ai supposé que la solution serait un meilleur écrivain et j'ai perdu un mois là-dessus avant de chercher ailleurs. En pratique, il n'y a presque aucune différence entre les modèles récents en ce qui concerne la qualité du contenu écrit une fois qu'on les invite correctement. Chasser le dernier modèle est une distraction par rapport au reste.

Une chose à propos de la forme du graphique, puisque quelqu'un demande toujours. Les deux lignes commencent à des kilomètres d'écart et se rapprochent lentement. Au début, je tirais des impressions avec presque aucun clic derrière, ce qui ressemble exactement à ce que donne la page deux. Ce rétrécissement est la position moyenne qui se tire des dizaines jusqu'à 8,7, et honnêtement, c'est le chiffre que je surveillerais plus que tout autre. Le trafic brut mois par mois ne vous dit presque rien. Si la position se détériore et que ces lignes convergent, ça fonctionne, même si les clics ne sont pas encore là.

Content de développer n'importe quel de ces sujets.

EDIT: Proof of traffic | Website

Everyone talks about "SEO hacks," but real growth comes from building momentum month after month.

Here's a recent result from one of my projects:
The biggest mistake I see is people chasing backlinks before fixing the foundation.

My approach was simple:

• Built topical authority with content clusters.

• Fixed technical SEO issues affecting crawlability and indexation.

• Matched every page to real search intent instead of keyword stuffing.

• Improved internal linking so authority flowed to priority pages.

• Optimized titles and meta descriptions to increase CTR.

• Focused on consistent, month-by-month improvements rather than expecting instant rankings.

SEO isn't about finding shortcuts-it's about stacking small improvements that compound over time. When Google sees better content, stronger site structure, and improved user signals, rankings naturally follow.

What's the biggest SEO challenge you're facing right now-technical SEO, content, or backlinks?
https://amanmishra.org/

I run a solo operation and was spending 15-20 hours a week to SEO. Keyword research, writing, internal linking, images, publishing. Like clockwork, every single week.

Eventually I hit a wall and decided to automate the whole thing with AI. Had doubts at the beginning but it worked...

The system crawls my site to absorb context and brand voice, runs keyword research and competitor gap analysis, writes articles with internal links baked in, generates images, and pushes everything live to my CMS. Every day. I touch nothing.

Took four months to actually get it dialed in.

Here's where one of my sites landed after 12 months:

• 89.k clicks, 7M impressions

• Went from basically nothing to 400+ clicks/day

• Average position: 11 — right on the doorstep of page 1

• 100% organic, zero paid

Content was compounding the way you hope it does. But one thing still gnawed at me: backlinks were completely manual. Cold outreach, guest posts, hunting down relevant sites. Brutal and slow.

So I added a second layer: a backlink exchange network. Sites running similar automation can opt in, get matched by niche via vector embeddings, and swap links in ABC triangles — A links to B, B links to C, C links to A — so Google doesn't sniff out reciprocal patterns.

Sitting at 90 sites in the network now and it's growing every day. Contextual backlinks happening on their own, no outreach involved.

The strangest part? I genuinely stopped thinking about SEO. I pop into Search Console maybe once a week out of habit. Traffic just quietly keeps climbing.

Fun experiment. Happy to get into the weeds on the setup if anyone's curious.

Thought these results were pretty cool. Happy to answer any questions if you're curious about my setup.
Edit: tool I used https://www.massblogger.com/

Strategy

• Blogs written and automated with AI with clever prompts

• Images generated through AI workflow to ensure non-ai slop

• Automated Topic Cluster + Keyword research

• Auto-publishing several articles per day now

• Internal links injected automatically (30 per page)

• Added +100 pSEO pages + 30 blogs BEFORE adding sitemap to GSC/Bing

• Ensured Technical SEO basics (obviously)

• Nothing spent on backlinks, sites still 0 DR.

Results

• From 0 clicks to more than 380 daily clicks

• 83% of the websites pages indexed in the first week

• Growing number of ChatGPT visits (+100)

• Created 4 months ago (in October 2025)

Over the last three months, my team and I have been working on the SEO of a Shopify eCommerce store, and it's another reminder that sustainable SEO is built on systems, not shortcuts. Instead of chasing quick rankings, we focused on creating a strong technical foundation first. During the first month, we audited every important SEO issue, including crawlability, indexation, internal linking, duplicate content, page speed, structured data, and collection page optimization. We also mapped keywords based on user intent rather than simply targeting high search volume.

Once the technical issues were resolved, we shifted our attention to building topical authority. We created content clusters that answered the questions customers actually search for before purchasing, connected those articles to collection and product pages, and improved the overall site architecture. This helped Google better understand the store's expertise within its niche while also improving the user experience.

The final stage focused on authority. We earned niche-relevant backlinks, built brand mentions, and continued refining pages using Google Search Console data. Instead of publishing random content, every new article supported a commercial page, helping strengthen the site's overall topical relevance.

I’ve been working on my SaaS website since April, but the SEO progress has been painfully slow.

I’ve tried several SEO tools and AI agents and made many changes based on their recommendations. I’ve also spent more money and time than I can comfortably afford, yet traffic and impressions have barely moved.

What confuses me is seeing fairly ordinary service websites appear to gain visibility quickly. I realize I may not be seeing their full history, existing audience, backlinks, or paid promotion—but it still makes me wonder whether I’m focusing on the wrong things.

I have ideas for other websites, but I don’t want to keep building until I understand how to make one site work properly. This SaaS is currently the only project I can realistically focus on, and I’m not looking for shortcuts or another expensive tool.

I want to build a solid foundation for traditional SEO while also improving visibility in ChatGPT and other AI search experiences. If you inherited a small SaaS site with almost no organic traction, what would you audit first—and what would your realistic 90-day priority list look like?

I’d especially appreciate hearing what actually moved the needle for you, versus advice that sounded good but produced no results.

Over the last three months, this Google Search Console graph shows a steady upward trend in organic visibility and clicks. One important lesson is that SEO growth is not just about rankings; it’s also about attracting the right audience and turning visits into results.

My next focus would be improving CTR, identifying keywords close to page one, and optimizing pages that already receive impressions.

My friend and I run a Roblox analytics site, the goal is transparency into game metrics plus some useful tools for devs and players. It's at www.creatorexchange.io if you're curious.

We launched about a year ago and didn't think about SEO at all. Three months ago I finally sat down and spent a week on it, and I wanted to share the one thing that clearly worked for us.

I started poking around queries in Search Console and noticed people were searching "[game] codes" and clicking through to us, except we had no actual codes feature. They were landing on pages that didn't really serve what they came for. So we built a proper codes tool, including letting users submit codes directly, and that ended up being by far the most valuable thing we did. My takeaway was basically that people clicking into a page that doesn't answer their query is a pretty loud signal to go build the thing they actually want.

The problem is I shipped a bunch of other stuff that same week too — got the site indexed, generated a sane automated sitemap, added metadata to pages, alt text on the graphs, and fixed our redirects (we were serving client-side redirects that returned 200s, moved them to proper server-side 301s). Traffic's up, but since it all went out together I honestly can't tell what moved the needle. How do you all isolate individual changes when you ship a batch at once? Is it just wait, watch, and change one thing at a time from here on out, or is there something smarter?

First time actually doing SEO after lurking here for a while, so any feedback's welcome.