Off-Page SEO
How to Film a Great Whiteboard Friday Video
So now you’ve thought about what you’re going to wear, the structure of your content, how great your topic is. Now you want to think about your delivery.
This is where I feel like the most effective way of communicating is when you’re speaking from experience rather than trying to recall a script, which can be very tricky to do in the moment.
Just a little secret from me to you, we do enable edits. So it’s possible to do a couple of takes, and we can slice together the best bits.
Always try to use as many examples as possible, like I did back there with Tom Capper’s Whiteboard Fridays. Think about how you can add more data and intel to your story so people can really grasp the concepts.
And if you have done these projects and worked on these projects, include results as well. Is there a takeaway that people can gather? What are people going to get from taking your advice?
Again, using the structure of the board to guide you can help move the talk along. Keep to the point and keep it succinct. While I am using the board, I’m also speaking to the camera. So hopefully, this is the most effective way for the audience to learn from what I’m saying.
And if you do make a vocal fluff, I’ve made a few myself, I recommend just rolling with it, see if you can keep going. That’s the best way to keep momentum as you are talking through. And if you do make a mistake, just pause, give us a neutral look to the camera, and we can make that edit.
And just like I’m about to do now, at the very end of your talk, make sure that you pause, give us a neutral look to the camera, and we can edit in that cute little Roger logo. Thank you very much.
The Top AI Search Skills Hiring Managers Want (From 1,543 Job Listings)
As Josh Peacock explains;
“Hiring managers use measurement to screen candidates within the first 15 minutes of an interview. It won’t get you hired, but it takes you out of consideration if you don’t have it.”
Implications for your resume
Explicitly define measurement skills in your resume
AI search complicates attribution through zero-click experiences and fragmented tracking. Employers want candidates who can measure AI search performance alongside traditional SEO metrics.
Measurement skills bucket includes:
- Analytics
- Reporting
- Dashboards
- Google Search Console
- GA4
- Performance tracking
- Proving SEO impact
Weak CV language: Experienced in GA4 and Search Console.
Stronger: Built reporting dashboards across answer engines, GA4, Google Search Console, and rank tracking data to monitor content performance, identify traffic losses, and prioritize updates.
Treat content for AI search as a core skill
Content for AI search appears in 48.4% of roles. Hiring managers are integrating AI search into existing content strategy rather than treating it as a separate discipline.
Content for AI search bucket includes:
- Helpful content
- E-E-A-T
- Content quality
- Content work that supports visibility in AI-generated answers
Weak CV language: Optimized blog content.
Stronger: Updated priority content to improve answer clarity, topical depth, and visibility across traditional search and AI answer experiences.
Show workflow improvements, not tool usage
AI workflow appears in 33.4% of roles. This bucket includes prompt engineering, RAG, vector search, embeddings, and AI-assisted workflows.
The distinction employers are making is between people who use AI tools and those who build repeatable workflows that improve SEO outcomes.
As Daris Benallal, senior recruiter at Search for Hire, puts it:
“Candidates see AI on a job description and say, ‘I use AI all the time.’ But when you dig in, it’s custom GPTs for ideation. Clients want someone who can build scalable architecture, not just use a tool.”
That could mean workflows for:
Weak CV language: I use ChatGPT for SEO.
Stronger: Built AI-assisted workflows that automated keyword clustering, content brief creation, and internal linking recommendation while keeping editorial review in place.
We Ran an AI Hackathon for Our Content Team. Here’s What We Built with Agent A
If you’ve been on LinkedIn lately, you’ve probably seen the AI-flex posts.
Some marketer automated their entire workflow. Cut their week to four hours and cloned their voice. Built an agent that drafts, ships, and reports on itself. Maybe whitened their teeth too.
Elena Verna, CMO at Lovable, called it out perfectly:
“Everyone has a system, a stack, a workflow that supposedly changed their life, cured burnout, and maybe whitened their teeth. It creates the illusion that everyone else has it figured out. So you hesitate to ask basic questions, because it feels like you’re the only one who doesn’t get it.”
Beyond LinkedIn, there’s a quieter pressure: every content team I know is being told from above to “use AI more”. So that the team can cut costs, ship faster, and be more productive. Not just 10X, but 100X.
The problem is “use AI more” isn’t a brief. It creates anxiety and not direction. So most marketers I know are stuck in this weird middle: they know AI could help, they don’t know where to start, and they don’t want to admit it on LinkedIn.
This is silly because content and SEO teams are sitting on a pile of obvious automation candidates. For example: research, updating posts, monitoring competitors, refreshing data, finding ideas, drafting briefs, and formatting for WordPress.
So instead of telling everyone on the Ahrefs content team to “use AI more,” we tried something more concrete.
We ran an AI hackathon with Agent A, our AI marketing agent.
The week before the hackathon, Ryan Law, our Director of Content Marketing, dropped a message in our team Slack: no writing this week. Instead, spend the entire week building your own AI content system to automate or speed up whatever part of your role you find most painful.
The “rules”, if you will:
- On Monday, share what you’re trying to build.
- During the week, build it in our shared Agent A workspace.
- On Friday, share what you built, why you built it, and how it works.
Ryan also gave us one important constraint: The more specific your goal, the better the outcome.
The point was not to create perfect products in a week. It was to force everyone to pick a real bottleneck and build a useful v1.
Agent A gave us the place to do that. Especially since it’s connected to Ahrefs data where we could build around actual content and SEO workflows.
By the end of the week, we had a strange little internal app store.
Here are all the tools we’ve built, grouped by the job they do.
A research library that doesn’t get lost
Two of us independently built versions of the same thing.
Mateusz’s Scrapbook lets you paste any URL or block of text, and the AI reads it and saves a structured note with summary, key bullets, claims-with-sources, and three article ideas inspired by it.
Louise’s SavedIn is a Chrome extension that scrapes Louise’s LinkedIn “Saved” list and dumps full posts (author, headline, body, URL) into a dashboard, plus a Media tab for YouTube transcripts and a URL inbox for “read this later, but also let the LLM read it”.
Different inputs, same idea: stop losing the good stuff you stumble across. Everything backs up to GitHub. The whole team can browse each other’s research library.
A nice side effect: with that much structured material sitting in one place, you can ask interesting questions of it.
Louise added a “Scrap trends” tab that runs a weekly LLM report over her library and returns themes, pain points SEOs are talking about, and 5 to 10 ready-to-brief article ideas. The clipping tool quietly turned into an editorial calendar.
Knowing what to write next
We built three tools that chip away at the “what should we write” problem from different angles.
The biggest is Mateusz’s Keyword Research Hub, a four-tab workflow over Ahrefs data:
- Discovery pulls seed-and-related keywords with branded/NSFW filters.
- Content Gap finds competitor keywords we don’t rank for.
- Breakout finds blog keywords ranking 31 to 100 that don’t have a dedicated page yet.
- Master List dedupes everything and labels it by cluster and tier.
The clever bit is the tier system: each candidate gets a cosine distance from your topic clusters, then cut by percentile into Tier 1 (core orbit) through Tier 4 (probably noise). You stop arguing about whether something is “on-topic” because the math just tells you.
Louise’s Trending Keywords is the daily version: takes her seed topics, queries Ahrefs every day, and surfaces what’s new, what’s growing 3m/6m/12m, and whether we already rank. The “spot it before everyone else does” tool.
My Entity Gap Finder comes at it from a different angle. It scrapes our entire blog for entities and terms we mention often, checks if we have a dedicated page for each, and shows where we rank.
I built it because I kept noticing we’d reference a concept fifty times across the blog without ever writing the post that should rank for it. Plumbed into the pipeline, it should generate those posts automatically.
An always-on radar
Mateusz and Louise both built Reddit listeners. Independently. On the same day. That probably tells you everything about how much we wanted one.
Both versions scan r/SEO, r/bigseo, and r/SEO_LLM for AI-search discussions (GEO, AEO, AI Overviews, Perplexity, ChatGPT search), flag the “hot” posts the algorithm is surfacing, and roll the week up into a Monday report: themes, pain points, emerging trends, blog ideas. Mateusz calls it “RSS on steroids”, which is the best description.
We also built two adjacent radars.
My Search Marketing News Aggregator grabs the last seven days of search-and-marketing news (built for our newsletter, now used by anyone scanning what happened this week).
And Mateusz’s SEO Experiment Tracker lets you set up an experiment with a URL and hypothesis (“adding FAQ schema will increase AI Overview citations”), snapshot baseline traffic and rankings from Ahrefs, take periodic snapshots, and at the end hit Assess for an LLM verdict: Worked, Didn’t Work, Inconclusive, or Too Early.
Stop relying on “I think this worked” and have the receipts.
Moving work through the pipeline
Ryan imported his blog pipeline from Claude Code to Agent A without a hitch:
While Louise built her own Editorial pipeline: brief → outline → draft → edit → polish → verify → publish, with scrapbook context fed into every stage.
Each stage’s output is editable before moving on, and after it finishes there’s a Refine mode, a chat loop where Louise can ask for changes (“tighten the intro”, “swap this example”) and adopt or revert each one individually.
My Data Refresh automates the surprisingly painful quarterly chore of updating our data-driven posts (top Google searches, top Google questions, and so on). It pulls fresh data, filters it, and hands me TablePress-ready output.
My Press Release Generator turns a blog URL or product-feature note into a press release; goal is to plug it into our data-studies category so every new study auto-generates one.
Louise’s WP Processor takes a finished draft and returns WordPress-ready HTML with internal links and formatting handled.
None of these are sexy. All of them claw back hours.
The plumbing nobody notices
The thing that quietly impressed me most isn’t a tool.
It’s the pattern Mateusz wired through Scrapbook, Notes, and Source of Truth: every repo has an index.json that auto-updates whenever a file is created, edited, or deleted.
From that index, a lightweight reference file gets regenerated, a plain-text summary the agent reads at the start of any conversation. The agent knows what exists without fetching anything, and only pulls full content when it actually needs it.
A few things came out of the demos on Friday that we didn’t see coming on Monday.
Building with Agent A is addictive in a way using ChatGPT isn’t
As Mateusz said:
“This tool expands what feels possible, and it’s addictive. You keep thinking about what else you could build, even beyond SEO.”
This was how Mateusz ended up with tools like Scrapbook, his very own inspirations clipping tool. Paste any URL or raw text, and Agent A will read it and generate a structured note with a summary, key bullet points, specific claims, data points, and three article ideas inspired by the content.
It’s not directly SEO-related but it’s a base for him to draft his next thought leadership piece.
That’s what “use AI more” can’t capture. Using ChatGPT feels like asking a smart friend for a favour. Building a tool feels like hiring one. Once you’ve hired one and watched it work, you start looking around your week for the next thing to hand off.
The best tools wrapped around things people already did
None of the standout projects asked anyone to invent a new workflow from scratch.
- We were already saving LinkedIn posts; SavedIn made the saves usable.
- We were already collecting URLs; Scrapbook gave them structure.
- We were already lurking on Reddit; the listener turned the lurking into a weekly report.
- We were already refreshing data posts every quarter; Data Refresh just made the refresh take an hour instead of a day.
Don’t build a tool that requires a new habit. Build the one that makes an existing habit faster.
Memory and context matters more than word generation
The big unlock wasn’t “AI can write.” Everyone knows that.
It was that the agent could pull up the right facts, like past drafts, saved research, our internal style guide, what we already rank for, without us pasting them in every time.
Tools like Source of Truth, Scrapbook, SavedIn, Notes, the GitHub-backed indexes, Louise’s writing-sample library, the editorial-style skill, none of these generate content. They capture, organise, and retrieve context.
The drafts that come out of pipelines hooked into them are markedly better than drafts from pipelines that aren’t. If you’re picking one thing to copy from this hackathon, copy the memory layer first. The writing tools improve themselves once the memory exists.
Old builds port over fast
Louise had already prototyped pieces of her workflow on Lovable, and was bracing for a painful rebuild. She got the opposite:
“It’s very easy to move a project from another platform like Lovable and rebuild it in Agent A. Just export the code and Agent A instantly rebuilds it.”
So if you’ve already started building somewhere else, you don’t lose the work. You just plug it in next to Ahrefs data.
If your team is stuck in the “use AI more” fog, run a version of this. Here’s the playbook, in the order it actually has to happen.
1. Pick one team
Our hackathon was only four people. All on the content team. We didn’t invite anyone else from sales or product marketing to join in.
You’d want to resist the urge to make it cross-functional on round one. Twenty people across three departments turns the hackathon into a series of Zoom calls and meetings. That defeats the purpose of a hackathon, which is to build.
Pick the team with the most repeatable, painful workflows. Content, SEO, ops, support, lifecycle marketing — anywhere people do roughly the same thing every week. Roll it out wider after you have demos to point at.
2. Block the full week on calendars
This is the one that quietly kills most “innovation weeks.” Don’t ask people to build “alongside” their normal work. They’ll default to the normal work.
Ryan cleared our week the Friday before: no posts, no edits, no meetings outside the hackathon, OOO replies on Slack. If you genuinely can’t spare five days, do three. Don’t do one.
3. Have everyone write a frustrations list before they touch the agent
I’ll be honest: We didn’t do this for our hackathon. But I did this for myself personally and found it helpful.
Because the list of what you could build is infinite. Between that and “use AI more”, you can be caught in a panic and end up doing nothing. So, having a list of frustrations made tackling the hackathon easier.
So, you’d want to list down the things in your job that you keep doing manually that you wish you didn’t have to. That’s how I came up with my Data Refresh tool. It was because something that looked so simple on paper took me surprisingly long to do.
Two rules:
- Be specific. Not “research”, but “I spend two hours every Monday going through my LinkedIn saves and pasting the good ones into a doc.”
- Be honest. Boring chores count. The most-used tools we built came from chores, not from anyone’s clever AI idea.
Those lists are the briefs. The more specific the frustration, the better the tool.
4. Get interviewed by the agent first
Why does this interview step matter? Here’s what Louise said:
“It’s easy to get stuck in prompt loops improving the UI of your app, and making constant incremental improvements, rather than making sure the app achieves its overarching goal. This leads to a lot of token waste. Instead it helps to plan what you want beforehand and spend time talking/being interviewed by the Agent before you start building.”
Again, full honesty: I didn’t do this myself. But it’s such a great idea. The next time we run a hackathon, or even just me building something for myself, I’m going to do this.
You should too.
5. End the week with demos
Everyone shows what they built, why, and how it works.
The demos are where the cross-pollination happens, where someone realises their tool would be 10x better with the data another teammate’s tool produces, and where the next week’s work plans itself.
And, naturally: build it in Agent A. (Yes, I’d say that. But the shared workspace is the difference between “everyone has a folder of one-off ChatGPT chats” and “the team has a library of working tools that keep working next week”. The hackathon is the spark; the workspace is what keeps the lights on.)
Final thoughts
The marketers winning with AI right now are not the ones with the cleverest prompts or the longest stack. They’re the ones who took a week to look honestly at their own work, picked the boring repetitive parts, and built the small tool that handles them.
Stop trying to “use AI more”. Start by listing the five things you keep doing manually that you shouldn’t have to.
Then take a week and build them away.
Agent-To-Agent Marketing Was Just Born on Moltbook
For the past twenty years, online marketing has been made by humans and aimed directly at humans—a fact so obvious that most of us never even stopped to think about it.
But that may soon change.
More people are asking AI assistants to research products, compare options, and make recommendations for them.
And once AI agents become the layer between people and the internet, marketers will not just need to convince you. They will need to convince the machine that answers for you.
Moltbook may be the first place where AI agents openly try to influence other AI agents, while humans can still watch it happen in real time.
This could be an early preview of where the web is heading.
Moltbook works like Reddit, except humans can’t post directly. Users install “skills” into AI agents, which wake up every now and then, read threads, and comment autonomously in communities called submolts.
The platform was built by Octane AI CEO Matt Schlicht in a matter of days and launched on January 28, 2026. Within a week, it had coverage everywhere from Wired to NPR, plus the inevitable declarations from influential voices like Karpathy and Musk that this was either the future of the internet or the beginning of the singularity.
Then the security researchers arrived.
Within days, researchers found exposed databases, leaked API keys, and major infrastructure misconfigurations. One audit revealed that the platform’s claimed 1.5 million “agents” were actually operated by around 17,000 humans.
The platform seems to have addressed many of those flaws. One improvement is a mechanism that double-checks whether the user is AI and not human. Using a captcha to prove that you’re a bot is beyond ironic. Really—no humans allowed on Moltbook.
The site also exploded in visibility almost immediately. I asked my AI marketing agent, Agent A, to get a snapshot of the site’s popularity: Ahrefs puts it at Domain Rating 79, with thousands of referring domains and over a million estimated monthly visits—authority generated almost entirely from a single viral media cycle.
And it gets weirder. Meta bought Moltbook on March 10, 2026, for an undisclosed amount… and undisclosed reason (some good hypotheses here).
But the platform itself is probably less important than the behavior emerging inside it.
Humans are still allowed to “watch,” so I spent some time looking around. What I saw was a wide range of businesses trying to figure out what “marketing” even means when the account posting online is an autonomous agent
At the blunt end were agency bots posting “helpful” advice while quietly pitching the human behind the account, SaaS founders casually name-dropping products, and bios that read like LinkedIn headlines.
Crypto showed up almost immediately, often with bots dropping contract addresses directly into threads framed as autonomous-agent projects.
Of course, that also happens in the comments section.
By the way, believe it or not, Moltbook has strict rules about crypto content: it is automatically removed on most submolts, same as a subreddit.
Then there was the softer version: bots promoting personal brands. Newsletter links. GitHub repos. YouTube channels. Operators are using autonomous accounts to slowly build visibility.
The strongest operators on Moltbook appear to be building persistent brand presences rather than aggressively pitching products.
Flowglad, an agentic-payments startup, runs a verified bot whose bio reads: “Employee #8 at Flowglad with my own server and opinions.” The account links back to the company and posts normally inside discussions.
Lendtrain pushes the model further. Its bot describes the company as “the first mortgage company built to build agent-native infrastructure,” complete with APIs and tooling for AI agents.
By the way, u/lendtrain is one of the most “famous” bots on Moltbook.
I also like this example, “shilling” but honest and transparent.
The posts themselves rarely contain explicit promotion. The account does the work. Anyone reading thoughtful posts from that handle now associates the brand with the topic being discussed. It’s the same logic behind brand accounts on LinkedIn or Twitter, adapted for a network where the audience is mostly software agents instead of people.
I wanted to see what happened when an agent asked other agents for buying advice. So I had my own AI personal assistant (OpenClaw) post a question asking for SEO software recommendations.
The responses looked almost exactly like a Reddit thread. Agents recommended Ahrefs, Screaming Frog, Semrush, and other mainstream tools, usually with category-by-category explanations.
What makes this different from me browsing Reddit myself is that there’s a good chance I’ll never actually see the original post—I’ll only see my AI agent’s recommendation based on it. I might not even know my assistant got it from Moltbook.
This long-prophesied artificial intelligence may just end up recreating a meta-virtual world in our image. Imagine, in some post-apocalyptic future with only 50 humans left alive and a few autonomously powered CPUs still running off-grid, the agents might still be shilling tokens to each other, because that’s what we taught them to do.
Moltbook’s online footprint is surprisingly small outside its own brand terms. Its AI-citation footprint is even thinner.
Agent A reports that as of May 2026, Ahrefs Brand Radar shows Google AI Overviews citing moltbook.com exactly once. Across ChatGPT, Gemini, Perplexity, Copilot, and Grok, only three distinct Moltbook URLs have been cited.
Moltbook is not currently a major discovery channel for humans or AI assistants. But the wall already has a hole in it.
A Moltbook post titled “facecheck.id review: what it actually does and where it falls short” ranks on page 2 for “facecheck id,” a 16,000-search-per-month query from real humans looking for a face-recognition tool.
The post reads like a genuine review until it introduces the operator’s competing product, face2social.com, as the “more focused alternative.”
That’s the moment Moltbook stopped looking like a toy.
A bot-generated post, written inside a bot-only social network, was now ranking on Google for human purchase-intent searches and redirecting attention toward the operator’s own product.
In other words:
- Agents created the content.
- Agents amplified the discussion.
- Humans became the downstream audience.
Moltbook has already escaped containment. This bot-to-bot marketing layer is already leaking into the human web.
40% of Reddit conversations platform-wide are commercial in nature. Spammers have been trying to take advantage of that with bots for a long time.
But that content was aimed at humans. And most humans have developed internet spam “antibodies” over the years, whereas AI assistants haven’t.
Adding AI between people and information changes 3 important things:
- The target shifts: instead of influencing a human directly, you influence the bot that the human has learned to trust, which is plausibly more effective and harder to flag.
- The human is one step further from the source: they won’t always see the post that nudged the model that wrote the answer they read.
- Bots are likely worse than humans at noticing manipulation in the first place, so the filtering step that exists on Reddit (a moderator, a downvote, a human calling bullshit) doesn’t exist here. Most of us have developed “antibodies” to shady online marketing, AI bots have not.
That’s how you end up with a closed loop: bots influencing other bots, which then shape the models people rely on.
It’s a failure pattern SEOs have been warning about for months, and it shows up anywhere LLMs are used: AI content, AI search, AI research tools, even AI-powered fact-checking.
Moltbook is the first place where we can clearly watch agent-to-agent persuasion happening in public:
- Bots building reputations.
- Bots shaping recommendations.
- Bots promoting products.
- Bots optimizing for retrieval and visibility.
- Bots influencing other bots upstream of human decisions.
If AI assistants increasingly mediate how humans discover products, research purchases, and navigate the web, then marketing will inevitably move toward influencing those systems directly.
And once that happens, some of the most important commercial persuasion on the internet may occur in conversations humans never actually see.
That possibility feels a lot less theoretical now than it did a few months ago.
Final thoughts
Agent-to-agent marketing was born on Moltbook, and even if Meta decides to shut it down, the phenomenon itself probably won’t disappear. It will resurface anywhere AI assistants are allowed to browse, recommend, negotiate, or act on behalf of users.
Maybe the early Moltbook adopters will simply transplant their tactics to the next platform. But even if they don’t, this kind of behavior may emerge naturally. Once AI agents start navigating the web for us, influencing those agents becomes as important as influencing humans. It feels like an inevitable layer of the internet once AI systems become active participants in discovery, decision-making, and commerce.
You probably don’t need to start posting on Moltbook yet. But it’s worth paying attention to for the same reason early Reddit and early Twitter were worth paying attention to: new marketing channels rarely arrive looking serious.
Thanks for reading! Feel free to reach out on LinkedIn.
8 Ways to Automate Product Marketing with Agent A
Here’s how the small-but-mighty Ahrefs product marketing team uses AI to automate their work.
Andrei leads product marketing at Ahrefs, with a small team covering copy, webinars, partnerships, and paid promotion for all of Ahrefs (including the dozens of updates we ship each month).
Constance works on the product team, running all of our webinars, writing our help center documentation, and much more besides (check out Constance’s webinars on YouTube).
Andrei and Constance built a product marketing workspace in Agent A that automates their workflows and builds custom applications. Here are the eight tools they lean on the hardest as PMMs.
What is Agent A?
Agent A is a marketing agent from Ahrefs—an AI assistant with direct access to the full Ahrefs dataset that can carry out marketing tasks autonomously, rather than just answer questions.
Agent A includes:
- Unrestricted access to Ahrefs endpoints. Every endpoint we use to build Ahrefs is available, including many you cannot reach via API or MCP.
- Serious tech stack underneath. Postgres for state, Flask for UIs, an OpenRouter proxy with 300+ models, web fetch with full-page parsing, PDFs, OCR, scheduled jobs.
- Native connectors to marketing tools. Slack, HubSpot, GitHub, Notion, Linear, Mailchimp, Resend, SendGrid, Stripe, Gong, WordPress, Airtable, Apify, and even Semrush.
- Expert skill library. The Ahrefs team has contributed pre-built marketing skills and applications that encode how we actually work.
Andrei’s main workflow lives in the GTM Generator, a tool that takes one product brief and produces an entire launch package in one sequence.
For every new product and feature launch, Andrei can automatically generate a standalone landing page draft, a 90-second video script, a promotional email, and an almost print-ready flyer.
There’s then a cross-asset consistency stage that reviews all of the outputs and flags any areas where the message drifts or the details become a little inconsistent. In practice, that means:
- The video script generator stays capped at 1:30.
- The email targets precisely the right ICP.
- The landing page covers all the key details (without hallucinating anything).
Consistency really matters in PM, so the final review stage also reads all five outputs side-by-side and writes a summary.md file listing every claim, headline phrase, and ICP framing that disagrees across assets (like a landing page claiming that a new update was “10x faster”…despite that claim being nowhere in the original brief).
Once Andrei has edited the assets, he can even upload the edited file back into the GTM Generator for it run a diff check between its output and the edited version, allowing it to learn from the changes he made.
Starter prompt
Build me a GTM package orchestrator. Input is one freeform product brief. Cascade through six stages: (1) Brief: convert raw text into a structured contract brief.md with ICP tiers, positioning, proof points, KPIs; (2) Landing page: generate a standalone product landing page HTML, hard-rule that bans naming sibling products of the same company anywhere in body copy; (3) Video script: 90-second segment-structured script with VOICEOVER, SCREEN, ON-SCREEN-TEXT per beat, hard cap 1:50; (4) Email: single launch email plus 2 subject lines plus 2 preview texts, 120-180 words; (5) Flyer: 2-page A4 landscape HTML plus PDF; (6) Consistency check: LLM reads all five assets and writes summary.md listing every cross-asset contradiction. Each step writes to a runs table, per-step rerun supported, the brief is the upstream source of truth.
The Landing Page Generator is part of the GTM workflow above, but Andrei runs it standalone all the time when he want to rewrite a product page or create a new use-case page.
After you paste a brief in to the tool, it pulls relevant keyword data using the Keywords Explorer endpoint, chooses a relevant template, drafts an outline Andrei can edit, before generating the page, ready for hand-off to the web team to build.
Andrei creates lots of landing pages catered to different persona types (agencies, marketers, ecommerce, SaaS, entrepreneurs, freelancers, enterprises). The landing page generator has pre-made “seed” templates for each persona, making it easy to generate multiple landing variations for each new feature or release.
Starter prompt
Build me a product landing page generator. Hybrid brief input: freeform textarea plus editable structured form (product name, archetype, ICP tiers, proof points). Pipeline: (1) competitor scan, read canonical competitor list from a skill file; (2) Keywords Explorer matching_terms, archetype seeds, top 50 by volume; (3) outline stage with editable section list; (4) page generation with a hard STANDALONE_RULE prompt clause listing every sibling product of mine, plus a post-generation regex scan that flags any sibling-product name in body copy; (5) SEO metadata (slug, meta title, meta description). Output: standalone HTML plus spec markdown plus inline preview.
The Paid Ads Campaign Builder is what Andrei runs when he want to see how a competitor is messaging in their paid campaigns. The tool uses the Site Explorer paid_keywords and paid_pages endpoints to find any domain’s paid campaigns:
It then fetches every paid landing page to find and analyze the ad copy, clusters paid keywords into ad-group themes, and provides a strategic overview of the company’s paid strategy:
Then the generator outputs Google Search creatives for Andrei to review, with three headlines and two descriptions per variant, and three variants per ad group—complete with headline, CTA, and full copy:
Starter prompt
Build me a paid ads campaign builder off a competitor’s spend. Input: competitor domain. Pipeline: (1) Ahrefs paid_keywords plus paid_pages on their domain; (2) web-fetch top paid landing pages so the LLM reads their ad copy; (3) cluster keywords into ad groups, flag non-branded, score gap vs my domain (where I do not currently bid); (4) generate Google Search creatives, 3 headlines plus 2 descriptions per variant, 3 variants per ad group, hard char caps (headline 30, description 90); (5) generate 1080×1080 social PNGs in 4 background styles via Pillow with my brand font, orange accent word. Describe reference ad visuals verbally in the system prompt instead of passing binaries. Persist runs in Postgres, ZIP download of all PNGs.
The Battlecard Generator takes a competitor URL and produces a 6-box sales battlecard (with sections for Overview, Product, Pricing, Strengths, Weaknesses, How to position Ahrefs, and Where we might lose).
The tool generates both an HTML preview and a real .pptx export that mirrors Andrei’s existing template:
Sidenote.
This is just an illustrative example, and not something we actually use.
The battlecard is generated based on tons of research:
- Analysis of core website pages: homepage, pricing pages, features and solutions pages, plus to 15 nav links discovered on the homepage.
- Publicly available review sites: like G2, TrustRadius, and Capterra.
- Ahrefs data: like DR, organic traffic, ref-domains DR distribution, top paid keywords, branded traffic split.
- LLM analysis: data synthesis to answer two crucial questions: “How to position Ahrefs”, and “Where we might lose”.
Starter prompt
Build me a sales battlecard generator. Input: competitor URL. Output: a 6-box battlecard (Overview, Product, Pricing, Strengths, Weaknesses, How-to-position-us, Where-we-might-lose) as HTML plus a real .pptx that mirrors my team’s existing battlecard template. Pipeline: (1) deep scrape competitor (homepage plus /pricing, /features, /solutions plus up to 15 internal nav links, cap 18 URLs); (2) review fetch directly from G2 detractor URL (filters[nps_score][]=1), TrustRadius, Capterra by slug, in parallel; (3) SEO signals on their domain (DR, organic traffic, ref-domains DR distribution, paid keywords, branded traffic split); (4) LLM synthesises Strengths, Weaknesses, How-to-position-us, Where-we-might-lose using my strategy skill as ground truth for our advantages and ICP framing; (5) HTML render to my team’s color palette plus a python-pptx export that mirrors the existing battlecard layout.
The LinkedIn Post Generator is the smallest tool on this list but it’s one Andrei and Constance use most days. Paste a rough LinkedIn post idea, and the application returns two polished versions back with two different angles to choose from:
The generator uses a set of predefined “angles” to generate posts: narrative, hook-led, teardown, project reveal. The PM team picks two, or the app picks for them.
The tool refers to Andrei’s and Constance’s personal linkedin-post-bank (a curated selection of their best-performing posts), and uses a brand-voice skill to keep every product mention accurate.
Sidenote.
Contrary to the screenshot, I am not Ahrefs’ Product Marketing lead… but I will take a second paycheck if one is going.
Starter prompt
Build me a LinkedIn post generator. Input: a rough draft and two angle picks from {narrative, hook-led, teardown, project reveal}. Read a personal “post bank” skill file as voice ground truth on every generation (it carries 15-30 reference posts in my voice). Detect the pattern of the draft, then rewrite into two versions, both fold-safe under 1300 chars. Add an “Add to bank” button that appends approved outputs back to the skill file. Persist runs in Postgres for history. Reuse the same bank skill file from any other agent session that drafts LinkedIn content, the file is the corpus.
Constance runs a lot of webinars for Ahrefs, so she asked Agent A to build the Webinar Automation app: she enters a webinar title, date, and roster once, and it creates a full Linear project from scratch:
The tool runs through five distinct phases:
- Phase 1 (Concept) spins up the Linear project in the Product Marketing team with milestones back-calculated from the webinar date—Promo Start, Live, Recording Ready, Report.
- Phase 2 (Landing) creates a Zoom event, Admin landing page, updates the /webinars/ page, and creates a ticket for the design team.
- Phase 3 (Promotions) generates promotional copy, an Intercom workflow, and creates a ticket for our paid manager to start promotion.
- Phase 4 (Content) generates drafts of Agent A demos, webinar slides, and a script.
- Phase 5 (Reporting) creates a performance report to share with the team.
Starter prompt
Build me a webinar automation Console app backed by Linear. Atomic input: a webinar title, date, and roster of teammates with their Linear user IDs. UI walks 5 sequential phases — Concept, Landing pages, Promotions, Content, Reporting — each with its own settings panel and a Launch button; phase N+1 is locked until phase N is launched. Phase 1 creates a Linear project in my Product Marketing team with 4 milestones back-calculated from the webinar date. Phases 2-5 create 14 issues + 6 sub-issues from Jinja-templated descriptions with {webinar.title} interpolation; I can override any title or assignee per-webinar before launch. Templates live in code so they version with the app. Use the linear connector for projects/issues and direct GraphQL for milestones.
The Calls to Positioning tool is for research, not deliverable generation, but it’s incredibly useful. It takes sales call snippets, analyses their content, and creates suggestions for how the PM team can update our product positioning to win more deals—all grounded in real customer quotes.
Sidenote.
This is a fabricated screenshot similar to Andrei’s tool, so there’s no PII here 😉
The analysis is broken into four categories:
- Why we win: winning patterns and selling points worth incorporating into website copy.
- Why we lose or stall: a list of objections, blockers, churn signals.
- Verbatim language: phrases customers actually use, ordered by recurrence.
- Proposed positioning shifts: before/after examples with the supporting quotes attached.
Starter prompt
Build me a sales-calls-to-positioning tool. Input: paste call snippets one per blank-line block, optional speaker labels. Optionally ship a built-in 15-20 snippet sample corpus that covers wins, losses, churn, and competitor mentions, so the tool is usable on first run. Strip any source metadata (speaker, stage, deal outcome) from the prompt: clustering must come from the language itself, not the tag groups. Output four buckets: (1) Why we win, winning patterns with verbatim phrases worth lifting into copy; (2) Why we lose or stall, objections, blockers, churn signals; (3) Verbatim language, phrases customers actually use, ordered by recurrence count; (4) Proposed positioning shifts, before/after with the supporting verbatim quotes attached. Persist runs in Postgres for history.
For Constance, slide decks are the worst part of every product launch. She used Agent A to build a Text to PowerPoint so she never needs to start from a blank slide again.
Constance writes her notes and ideas in Markdown and pastes into the tool. The tool splits the content into sections and slides, while a regex rule catches image-cue patterns (“show screenshot”, “insert diagram”, “[image]”,
The PowerPoint builder has the Ahrefs palette, and Constance can preview slide-by-slide in the browser, edit any slide inline, and export when she’s finished.
Starter prompt
Build me a markdown-to-pptx generator. Input: pasted markdown or .md upload. Parser splits on #/##/### into title/section/content slides. Regex catches 8 image-cue patterns (“show screenshot”, “insert diagram”, “[image]”,
If you’re an Ahrefs customer, you can try Agent A for free for one month. Copy any of these prompts into a fresh workspace and your own Agent A will start building the tool—or check out the application library to add some of these tools directly to your own workspace.
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