Hey r/claudeskills. I'm the founder of Agensi. We're a curated marketplace for SKILL.md skills. I've been lurking here for a while and figured it was time to properly introduce what we're building and why.

The short version: we want skill creators to get paid for their work.

Right now, most skills are given away for free on GitHub. That's great for adoption, but it means the people who build the best skills have zero incentive to maintain them, document them properly, or build new ones. The best skill creators I've talked to spend hours packaging their domain expertise into reusable files, and the reward is a GitHub star if they're lucky.

We think that's broken.

What Agensi does

Agensi is a marketplace where creators list skills (free or paid) and buyers browse, search, or discover them through our MCP server. Every skill goes through an automated 8-point security scan before it goes live. Creators keep 80% of every sale. Payouts happen automatically through Stripe Connect.

We've been live for about 8 weeks. Here's where things are at:

• 350+ skills from 80+ creators
• 18,000+ active users in the last 28 days
• 41 paid transactions so far
• Creators have earned hundreds of dollars in the first month of being live

It's obviously not life-changing money yet. But it proves that developers will pay for skills that save them time. And for the creators who made those sales, it's the first time their skill-building work generated any income at all.

What we've learned about what sells

Not everything sells equally. Here's what we've seen so far:

Skills that encode specific opinions sell better than generic ones. "Code reviewer" is fine. "Code reviewer that checks OWASP Top 10 with severity ratings and remediation suggestions" sells. The more opinionated and specific, the more people trust it's worth paying for.

DevOps skills are the biggest category (18.7% of all listings). Developers are happy to pay for deployment checklists, CI/CD pipelines, and infrastructure-as-code patterns because mistakes are expensive and the skill pays for itself on the first use.

Free skills drive paid sales. Creators who list one free skill and then offer a more comprehensive paid version consistently outperform creators who only list paid skills. The free skill proves quality. The paid skill delivers depth.

The MCP angle

We also have a live MCP server at mcp.agensi.io. Any agent can connect and search the full catalog mid-conversation. Your agent finds relevant skills, suggests them, and you install with one command. We're not charging for MCP access. Every connected agent is a distribution channel for creators.

The idea is that skills shouldn't require a human browsing a website. Your agent should discover what it needs, when it needs it.

Why I'm posting here

This community is literally the people building and using skills every day. I'd love to hear:

1. Are you building skills that you'd consider listing on a marketplace? What would make that decision easy or hard?
2. If you've bought or would buy a skill, what made it worth paying for vs just writing your own?
3. What's missing from the skill ecosystem right now? Not from Agensi specifically, but from the whole skills landscape.

Not trying to sell you anything. The marketplace is free to browse, free to list on, and the MCP is free to connect. I'm genuinely trying to figure out how to make the creator economy for skills actually work, and this sub probably has the best perspective on that.

agensi.io if you want to check it out. Happy to answer anything about the platform, the security scanning, creator payouts, or how the MCP works.

Disclosure: I'm the founder of the site I'm discussing (agensi.io, a marketplace for AI agent skills). This post isn't about the product. It's about how I used Claude as a non-technical solo founder to build a full organic growth engine from zero.

The problem

I built a React SPA with Lovable. Out of the box it was invisible to search engines. Google's crawler saw an empty div and a JavaScript bundle. No server-side rendering. No structured data. A 460KB JS bundle. A 179KB PNG logo rendered at 112 pixels. LCP was 4+ seconds on mobile. PageSpeed performance score was around 70.

I don't have a CS degree. I can't write production code. But I had Claude.

What Claude actually did

Content strategy from raw data, not vibes. I export Google Search Console data weekly (queries, pages, clicks, impressions, average positions) and feed the CSVs to Claude. It identifies queries where I rank positions 1 through 3 but get zero clicks because AI Overviews answer the question first. It finds keyword gaps where competitors have content but I don't. It spots cannibalization where multiple pages compete for the same query. This replaced what would normally be a $5K/month SEO consultant.

Structured data architecture. Claude designed and generated the entire schema markup layer. Homepage has Organization, WebSite with SearchAction, and FAQPage with 15 Q&As. Product pages have SoftwareApplication with pricing, BreadcrumbList, and conditional FAQPage. Article pages have Article, FAQPage, HowTo, BreadcrumbList, and Organization. The /about page has Organization, AboutPage, and Person schema for entity anchoring. Every page validates clean in PageSpeed Insights with a 100 SEO score.

Performance optimization. Claude diagnosed the LCP bottleneck as framer-motion loading on every page for a single mobile menu animation. It identified synchronous analytics scripts blocking render. It found the logo was a 1920x1920px PNG being rendered at 112px and imported as a JS module so the browser couldn't even start downloading it until the entire bundle parsed. Claude's fix: generate WebP versions (7KB and 3KB), switch to a static path with preload, and lazy-load the navbar components. Desktop LCP went from 2.5 seconds to 0.9 seconds. Performance score went from 70 to 97.

AEO infrastructure. This is the part I find most interesting from an AI perspective. Claude helped me restructure every article so AI engines (ChatGPT, Gemini, Perplexity, Claude itself) would cite the content. Every article has a Quick Answer block at the top (40-60 words directly answering the main question). All H2 headings are phrased as questions because AI Overviews prefer extracting from question-format sections. Every page has FAQ schema. I created an llms.txt file that tells LLM crawlers what the site is and where key content lives. I also created an entity anchor page with Organization and Person schema so AI engines can establish who we are.

The result: 9 different AI engines now cite the site including ChatGPT, Gemini, Perplexity, Claude, Doubao, Copilot, and Kagi. 350+ AI-referred sessions per month and growing.

Technical SEO auditing. Claude found 121 queries where I ranked top 3 with zero clicks because AI Overviews were stealing the traffic. It found 18 published articles with zero Google impressions because they weren't indexed and generated the IndexNow ping commands to fix it. It diagnosed duplicate FAQPage schema being emitted both client-side by React components and server-side by the SSR edge function, causing validation errors on 90 pages. It identified the exact files, wrote the Lovable prompts to fix it, and verified the fix with curl commands.

The numbers after 2 months

500K+ total Google impressions. 6K+ total clicks. 878+ page-1 rankings (up from ~15 at launch). Average position 6.8. 15K active users in the last 30 days. Cited by 9 AI engines. $0 spent on marketing.

What this means for AI as a tool

Claude is not a magic content machine you point at a topic and get traffic. It's a strategic partner that gets better the more data you feed it. The key is bringing your own data (GSC exports, analytics, competitor analysis) and asking it to find patterns and opportunities in that data. The output is specific, actionable, and measurable.

The analytical and strategic capabilities get less attention than the coding abilities, but for a non-technical founder they might be even more powerful. I couldn't have built this growth engine without Claude. Not because it wrote the content for me, but because it showed me exactly where the opportunities were and how to structure everything so both Google and AI engines could parse it.

Happy to answer questions about the approach, specific prompts, or technical details.

Site: agensi.io

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 😄

I'm not a developer. I can't write code. I built an entire SaaS platform using Claude Code as my CTO, engineer, and SEO strategist. 8 weeks in, it's at 13K active users and growing.

I want to share the actual workflow because I think most people are underusing Claude Code as a strategic tool, not just a code generator.

Disclosure upfront: the platform I built is Agensi, a marketplace for SKILL.md skills. I'll reference it where relevant but this post is about the Claude Code workflow, not the product.

How I use Claude Code for SEO strategy

This is the one nobody talks about. I don't just ask Claude Code to write code. I use it to analyze entire datasets and make strategic decisions.

Example: I exported my Google Search Console data, my Ahrefs keyword data, and my content gap analysis. I fed all three to Claude Code in one session and asked it to build me a prioritized content plan. It came back with 58 articles organized by keyword difficulty, search volume, and competitive gap, with target URLs and internal linking strategy for each.

Then I had Claude Code write all 37 articles in one session. Not generic AI content. I gave it my existing articles as style references and it matched the tone, structure, and formatting. Every article has a Quick Answer block at the top (which is what AI answer engines extract for citations), FAQ structured data, and proper internal linking.

The result: 850+ page-1 Google rankings. We rank #1 for "where are claude code skills stored" above Anthropic's own docs.

How I use Claude Code for technical architecture

My stack is Lovable + Supabase + Netlify Edge Functions. Claude Code handles all the technical decisions.

The edge function that handles SSR, structured data injection, meta tags, caching headers, and security is about 1,200 lines of TypeScript. Claude Code wrote all of it, debugged it, and iterated on it across maybe 15 sessions. I'd describe the problem ("skill pages have broken structured data, here's the Ahrefs audit showing 753 errors"), paste the relevant code, and Claude Code would identify the root cause and write the fix.

One example: we had a bug where meta descriptions were being truncated because skill names contained double quotes that broke the HTML attribute. I didn't even know what HTML attribute escaping was. Claude Code identified the issue from an Ahrefs audit CSV, wrote an attr() helper function, and applied it to every meta tag injection point. Fixed 239 pages in one deploy.

How I use Claude Code for content operations

Every article on my site goes through this pipeline:

1. I give Claude Code the keyword data and ask it to write the article matching my existing style
2. Claude Code generates the markdown with frontmatter (title, slug, seo_title, meta_description, excerpt, tags, topic_cluster)
3. I review and approve
4. Claude Code generates the SQL INSERT statement
5. I run it through Lovable to inject into Supabase
6. Claude Code generates the IndexNow ping to notify search engines

I published 37 articles in a single day using this pipeline. Every one is now indexed and ranking.

How I use Claude Code for debugging

My favorite pattern: paste an Ahrefs site audit export and say "come up with a plan to fix all of this without breaking anything." Claude Code reads the CSV, categorizes issues by priority, identifies root causes, and writes the fixes. It caught things I would never have found, like a redirect chain where http://agensi.io went through 2 hops instead of 1 to reach https://www.agensi.io.

Another one: we accidentally deployed a noindex tag on 25 learn articles for about 3 hours. Claude Code caught it in the next audit analysis, identified the exact line in the edge function where the UTM detection logic was too broad, and wrote the safeguard that prevents it from ever happening again.

The actual workflow pattern

The pattern that works for me as a non-developer:

1. Describe the problem in plain English with as much data as possible (screenshots, CSVs, error messages, URLs)
2. Ask Claude Code to plan before executing
3. Review the plan, push back on anything I don't understand
4. Let it execute
5. Test with curl commands or manual checks
6. If something breaks, paste the evidence and iterate

The key insight: Claude Code is dramatically better when you give it data instead of vibes. "Fix my SEO" produces garbage. "Here's my Ahrefs audit CSV with 753 structured data errors on /skills/ pages, here's the current edge function code, here's what Google's Rich Results Test says" produces a precise fix.

What doesn't work

Asking Claude Code to make product decisions. It'll give you technically correct answers that are strategically wrong. I make all product and positioning decisions myself and use Claude Code purely for execution.

Also, long sessions degrade. After about 30-40 back-and-forth messages, Claude Code starts losing context on earlier decisions. I start new sessions for new tasks rather than trying to keep everything in one conversation.

Happy to answer questions about specific parts of the workflow.

I built Agensi, a marketplace for AI agent skills. Think app store for Claude Code, Cursor, Codex CLI, and Gemini CLI. Every skill is security-scanned and works across 20+ agents.

This post isn't about the product though. It's about the exact growth playbook that took it from zero to 12,000+ active monthly users in 2 month without spending a dollar on ads.

Quick context: I'm a non-technical solo founder based in Amsterdam. Built the entire thing on Lovable + Claude. Previously built and exited a healthcare startup. This is round two.

The numbers first

Launched mid-March 2026. Here's where things stand today:

12,400+ active users in the last 28 days. 4,000+ organic Google clicks per month from 300,000+ search impressions. 850+ page-1 Google rankings, up from about 15 at launch. 150 of those are top-3 positions. 700+ registered users, 52 creators, 250+ skills listed. 39 paid transactions and 4 MCP subscribers. Cited as a source by ChatGPT, Gemini, Claude, Perplexity, Doubao, and Kagi. Total marketing spend: $0.

What I did NOT do

Paid ads. No. Cold outreach or DMs. No. Launched on Product Hunt day one. No. Built a massive social following first. No. Hired a marketing person. No.

What actually worked

Two things. I went all in on both and ignored everything else for the first 2 months.

1. SEO + AEO content engine

I wrote 160+ articles about AI agent skills. Not thin content. Real guides that answer the exact questions developers are searching for. "Where are Claude skills stored?" "How to install skills in Codex CLI?" "Best skills for API development?" "Cursor rules vs SKILL.md?"

Every article is structured so both Google and AI answer engines can parse it. The result: we rank #1 for "where are claude code skills stored," above Anthropic themselves. Google's AI Overview uses Agensi as its source for that query.

But the real unlock is AEO, AI Engine Optimization. ChatGPT, Gemini, Claude, Perplexity, Doubao, and Kagi are all sending us traffic by citing our articles in their answers. Around 270 users came directly from AI engines in the last 28 days. That number grows every week. I believe this will eventually overtake traditional search for developer tools.

Every skill listing is also a landing page. 250 skills means 250 pages ranking on long-tail queries like "[tool name] + [capability] + claude code skill." Creators add skills, my SEO footprint grows automatically. Zero marginal cost per page.

2. Reddit with real substance

I posted maybe 10-15 times total across r/ClaudeAI, r/cursor, r/vibecoding, and a few others. Not promotional posts. Genuine useful content: skill recommendations, workflow tips, honest takes on agent tools. I shared my link where it made sense naturally.

A couple posts reached the front page of their subreddits. That drove direct traffic but more importantly, it seeded the initial user base that created word-of-mouth. Reddit was about 340 first-time users in the last 28 days. It was the spark. SEO became the engine.

What I learned

The compounding is real but painfully slow at first. For the first 3 weeks I had maybe 15 impressions a day on Google. I almost pivoted to paid acquisition. Glad I didn't. Once you cross a critical mass of content (around 50-60 articles for me), Google starts trusting the domain and everything accelerates. My impressions went from 300/day to 20,000+/day in about 4 weeks.

Also: AI agents citing your content is the most underrated growth channel right now. If you structure your content with clean metadata and actually answer the question in the first paragraph, LLMs will pick you up as a source. This is free, compounding distribution that most founders aren't thinking about yet. I track it in GA4 by referral source and it's climbing every week.

What's next

Raising a pre-seed round to hire an engineer and a growth lead. Doubling down on creator acquisition since every creator is both a supply-side contributor and a marketing channel. And continuing to write, because the content engine is the moat. Every article compounds forever.

Happy to answer questions about the SEO approach, AEO strategy, building on Lovable as a non-technical founder, or anything else. If you could make an account on www.agensi.io you would help me out more than you an imagine :)

6 weeks ago I launched a side project. Today it has 10,000 active users, 250+ products listed, 50+ independent creators, revenue coming in, and I quit my business development job to go full-time on it.

The project is Agensi a marketplace for AI agent skills. If you use AI coding agents like Claude Code, Cursor, or GitHub Copilot, you can install SKILL.md files that make your agent better at specific tasks. Code review, testing, documentation, DevOps, git automation. Think of it like an app store but for AI agent capabilities.

I'm not a developer. I built the entire thing with Lovable and Claude. Here's what happened.

Week 1: The idea that almost didn't happen

I was using Claude Code daily for work and kept searching GitHub for SKILL.md files to make it better at code reviews. The experience was terrible. Random repos, broken YAML, no way to know if a skill was safe to install. I found one that looked like it had prompt injection buried in the instructions.

I mentioned this to a friend and he said "sounds like there should be an app store for that." I spent the weekend building a v1 with Lovable. It was ugly but it worked. Listed 15 skills I built myself. Showed it to a few people in Discord. Three of them uploaded their own skills within 24 hours.

Week 2-3: The SEO play that changed everything

I had a working marketplace but zero traffic. I almost spent money on ads. Instead, I decided to try something different: I treated every product page as a landing page and started writing articles targeting specific search queries.

Not generic "what is AI" content. Specific questions like "where are Claude skills stored" and "how to install skills in Codex CLI." I found these queries by checking Google Search Console for what terms were already showing impressions.

I wrote 30 articles in two weeks. Claude did the keyword research by analyzing my GSC data, finding gaps, and identifying queries where I had high impressions but no dedicated content. Every article followed a structure: Quick Answer block at the top (40-60 words), H2 headings phrased as questions, FAQ schema, internal links to related articles.

By the end of week 3, I had 50+ page-1 Google rankings. Traffic started compounding.

Week 4: The AEO moment

Something unexpected happened. I checked Google Analytics and saw traffic from chatgpt.com, gemini.google.com, and perplexity.ai. AI engines were citing my site when developers asked where to find SKILL.md skills.

I didn't optimize for this intentionally at first. But the article structure I was using (Quick Answer blocks, question-format H2s, FAQ schema) was exactly what AI engines prefer to extract and cite. So I doubled down. Added Organization + Person schema for entity anchoring. Created an llms.txt file. Made sure robots.txt allowed all AI crawlers.

Today: 350+ AI-referred sessions per month from 9 different AI engines including ChatGPT, Gemini, Perplexity, Claude, and even Doubao (ByteDance's AI). This channel is growing faster than organic search.

Week 5: First revenue and the "oh shit" moment

A creator uploaded a premium skill with reference files, scripts, and an OWASP checklist. Priced it at $10. Someone bought it within 48 hours. Then two more sales. Then an MCP subscription ($9/month for live agent-native access to the full catalog).

The numbers were small but the model worked. Creators upload skills, set their own price, keep 80% of every sale through Stripe Connect. The marketplace takes 20%. Every skill goes through an 8-point automated security scan before it goes live.

I did the math. If I could get to 1,000 skills and maintain the current growth trajectory, the marketplace commission alone would cover my salary. The MCP subscription added predictable MRR on top.

Handed in my notice on Monday, had my last day on Friday.

Week 6: The current numbers

• 10,000+ monthly active users
• 250+ skills from 50+ creators
• 150 articles across 12 topic clusters
• 350K Google impressions/month
• 1,000+ organic clicks/week
• 850+ page-1 rankings
• 350+ AI-referred sessions/month from 9 AI engines
• 39 paid transactions, 4 MCP subscribers
• $0 spent on ads. Ever.

Where this is going

The marketplace is step one. The bigger vision is becoming the trust and distribution layer for AI agent capabilities, not just skills, but any capability an agent needs to do its job better.

Right now SKILL.md is the standard. Tomorrow it might be tool configs, MCP integrations, workflow templates, or something that doesn't exist yet. The format will evolve. What won't change is that agents need capabilities, those capabilities need to be trusted, and creators need a way to distribute them.

We already have the MCP server live, your agent connects to Agensi and searches, evaluates, and loads skills on demand without downloading anything. That's the first version of what an agent-native marketplace looks like. By 2027, both sides of the marketplace run through MCP. Creators publish through their agent. Buyers discover and install through theirs. The web UI becomes optional.

The endgame: when any AI agent anywhere needs a new capability, Agensi is where it looks first. Curated, scanned, accountable. The trust layer the ecosystem doesn't have yet.

What I'd tell you if you're building a side project

Don't build for 6 months in stealth. I launched an ugly v1 in a weekend and got real users within days. Their feedback shaped everything that came after. The product I have now looks nothing like that first weekend build.

SEO is the most underrated growth channel for side projects. Everyone chases Product Hunt launches and Twitter threads. Those give you a spike that dies in 48 hours. Every article I wrote 6 weeks ago still drives traffic today. Content compounds. Ads don't.

Use your side project's data, not your intuition, to decide what to build next. I don't guess which articles to write or which features to build. I look at GSC data, GA4 data, and skill request submissions. The users tell you what they want if you set up the right feedback loops.

The "should I quit my job" question answers itself. I didn't quit because I felt confident. I quit because the growth curve made it irrational not to. When your side project is growing 50% week-over-week with zero paid acquisition, the opportunity cost of staying employed is higher than the risk of going full-time.

AEO is the cheat code nobody is talking about. If your side project's content is structured for AI engines (Quick Answer blocks, FAQ schema, entity anchoring), you get a distribution channel that literally didn't exist 18 months ago. 350 sessions/month from AI citations and I've barely started optimizing for it.

The site is agensi.io. Happy to answer questions about the build, the growth strategy, the tech stack (Lovable + Claude + Supabase + Netlify + Stripe), or the decision to go full-time. Also open to feedback on the product itself, that's why I'm posting here.

If you're a developer who builds SKILL.md skills or .cursorrules files, you can publish them on Agensi and start getting users (and revenue) from day one. And if you need a skill that doesn't exist yet, drop it on the skill request board creators get notified and build to real demand.

Also, please make an account it helps me more than you can imagine!

I lurk here a lot and most growth posts are either "just launch on Product Hunt" or "run Google Ads." Neither worked for me. Here's what actually worked to get 10,000 active users in 6 weeks as a solo founder with zero marketing budget.

I built a micro SaaS marketplace for AI agent skills. Think of it like an app store but for SKILL.md files that make coding agents like Claude Code and Cursor better at specific tasks. The product itself isn't the point of this post. The distribution strategy is.

The setup: content as your product page factory

Every product on my marketplace is a landing page. That's the fundamental insight. Most micro SaaS founders have one homepage and maybe a blog. I have 200+ product pages and 96 articles, each targeting a different long-tail keyword. Every page that gets indexed by Google is a permanent acquisition channel that costs nothing to maintain.

The articles aren't fluff. Every single one was written to target a specific query that real people search for. I know this because I check Google Search Console weekly and only write articles targeting queries where I already have impressions but no dedicated content. No guessing. Pure data.

Results after 6 weeks: 300K+ Google impressions/month, 1,000+ organic clicks/week, 850+ page-1 rankings. Every article compounds. The ones I wrote in week 1 still drive traffic today.

The part nobody talks about: AEO

AEO is answer engine optimization. It's SEO but for ChatGPT, Gemini, Perplexity, and Claude. When someone asks an AI "where can I find AI agent skills," the AI cites my site. 350+ AI-referred sessions per month from 9 different AI engines including ChatGPT, Gemini, Perplexity, Claude, Doubao (ByteDance), and Copilot.

This isn't accidental. Every article has a Quick Answer block at the top (40-60 words directly answering the main question). All H2 headings are phrased as questions. Every page has FAQ schema. There's an entity anchor page with Organization + Person schema. An llms.txt file tells LLM crawlers what the site is.

Why this matters for micro SaaS: AI Overviews are eating Google clicks. I have 121 queries where I rank #1-3 on Google with zero clicks because the AI answers the question first. If your micro SaaS isn't optimized to be the source AI engines cite, you're losing traffic to a channel that didn't exist 18 months ago.

The technical SEO layer most builders 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.

I fixed all of this using Claude as my technical partner. Claude wrote a server-side rendering layer, diagnosed the bundle bloat, found the oversized images, and rewrote the performance bottlenecks. Desktop performance went from 70 to 97 on PageSpeed Insights. LCP went from 4s to 0.9s.

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 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 3-5 specific opportunities with exact numbers
4. I write 2-3 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.

This loop takes about 2-3 hours per week. It's the highest-ROI activity in the business. Every other growth channel I've tried (Product Hunt, directories, cold outreach) produced a one-time spike. This compounds.

What I'd tell a micro SaaS founder starting 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 2-3 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. Long-tail queries convert better and are easier to rank for.

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

Don't spend money on ads until your organic engine is running. I have $0 CAC on 10,000 users. Every dollar I would have spent on ads is money I didn't need because the content engine was already compounding.

The site is agensi.io if you want to see how this looks in practice. Happy to answer specific questions about any part of the playbook.

I built a marketplace for AI agent skills called Agensi. The entire thing was built with Claude and Lovable. I'm not a developer. But that's not what this post is about.

This post is about how Claude became the single most important tool in my growth stack. Not for coding. For SEO, content strategy, and a new thing called AEO (answer engine optimization) that I think most people are sleeping on.

Claude writes all my content, but not the way you think

I don't ask Claude to "write me a blog post about X." That produces generic AI slop that nobody reads and Google doesn't rank.

Instead, I feed Claude my Google Search Console data (queries, impressions, click-through rates, average positions) and ask it to find keyword gaps. Claude analyzes the data, identifies queries where I have high impressions but zero clicks, finds topics where I have no content but competitors do, and spots cannibalization where multiple pages compete for the same query.

Then we write articles together targeting those specific gaps. Every article has a structure that Claude and I developed over weeks of iteration: a Quick Answer block at the top (40-60 words that directly answer the main question), H2 headings phrased as questions (not "Claude Code Skill Locations" but "Where Does Claude Code Store Skills?"), 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. 300K search impressions per month. 878+ page-1 Google rankings. All organic.

The AEO strategy nobody is talking about

Here's what surprised me. ChatGPT, Gemini, Perplexity, and Claude itself are now sending us traffic. 348 AI-referred sessions per month and growing fast. These AI answer engines cite agensi.io when developers ask where to find SKILL.md skills.

Claude helped me build the entire AEO infrastructure. We restructured every H2 heading as a question because AI Overviews prefer extracting from question-format sections. We added FAQ schema to every page so Google's AI picks up our Q&As. We built an /about page as an entity anchor with Organization, Person, and AboutPage schema. We created a robots.txt that explicitly allows all AI crawlers and an llms.txt file that tells LLMs what the site is and where to find key content.

The result is that 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 agensi.io. Claude helped me engineer that outcome deliberately. It wasn't an accident.

Claude as a technical SEO auditor

Every week I export data from Google Search Console, Ahrefs, and Google Analytics and dump it into Claude. Claude finds things I would never catch on my own.

It found that 121 queries where I ranked position 1-3 had zero clicks because AI Overviews were stealing the traffic. That insight changed my entire strategy from chasing rankings to becoming the source that AI Overviews cite.

It found that my "best claude code skills 2026" article had 25,000 impressions and only 29 clicks. The problem was the title. Claude rewrote it to "15 Best Claude Code Skills in 2026 (Tested & Ranked)" and we're watching the CTR climb.

It found that I had 18 published articles with zero Google impressions because they weren't indexed. Claude generated the IndexNow ping commands and the GSC URL Inspection list to fix it.

It diagnosed a duplicate FAQPage schema issue that was causing GSC errors on 90 pages. The root cause was React components emitting FAQ schema client-side AND the SSR edge function emitting it server-side. Claude identified the exact files, wrote the Lovable prompts to fix it, and verified the fix with curl commands.

The structured data layer

Claude built the entire structured data architecture for the site. Every page type has the right schema:

Homepage has Organization, WebSite with SearchAction, and FAQPage with 15 Q&As. Individual skill pages have SoftwareApplication with pricing, BreadcrumbList, and conditional FAQPage. Article pages have Article, FAQPage, HowTo, BreadcrumbList, and Organization. The /about page has Organization, AboutPage, and Person schema for entity anchoring.

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

Core Web Vitals fixes

Claude diagnosed that our desktop LCP was 2.5-4s on 190 URLs. It identified the causes (460KB eager JS bundle, framer-motion loading on every page for a mobile menu animation, synchronous analytics scripts) and wrote the Lovable prompts to fix each one. Desktop LCP went from 2.5-4s to 0.9s. Performance score went from ~70 to 97.

For mobile, Claude found that the LCP element was a 1920x1920px, 179KB PNG logo being rendered at 112px. It was imported as a JS module so the browser couldn't even start downloading it until the entire JS bundle parsed. Claude's fix: generate WebP versions (7KB and 3KB), switch to a static path with preload, and lazy-load the navbar search and dropdown components. Logo went from 179KB to 7KB.

What I've learned

Claude is not a magic content machine that you point at a topic and get traffic. It's a strategic partner that gets better the more data you feed it. The key is bringing your own data (GSC exports, analytics, competitor analysis) and asking Claude to find patterns and opportunities in that data. The output is specific, actionable, and measurable.

If you're building something and not using Claude for your SEO and content strategy, you're leaving a lot on the table. The AI coding capabilities get all the attention, but the analytical and strategic capabilities are just as powerful.

Happy to answer questions about the specific workflows, prompts, or technical details.

agensi.io