Tag: SEO Tools

My AI Content Gap Workflow for Smarter SEO Priorities
I can publish consistently, follow SEO best practices, and still watch competitors outrank me. When that happens, I usually find that the issue is not content quality alone. It is content coverage. Competitors are answering questions my audience is already asking, while my site is not fully part of that conversation yet.

That is where I use content gap analysis. It helps me identify the topics competitors rank for that I do not, then decide which opportunities are actually worth pursuing.

Finding gaps is rarely the hard part. SEO tools make that fairly easy. The real challenge is making sense of thousands of keywords across several reports and deciding what deserves attention first.

My workflow combines competitor data, first-party search data, and AI so I can prioritize content opportunities around business impact instead of search volume alone.

I bring my SEO data together before analyzing it

In this workflow, I use Semrush to identify competitive opportunities, Google Search Console to validate where my site already shows signs of authority, and Google Analytics to add business context. Then I use Claude to bring those datasets together, group related opportunities, identify patterns, and help me decide what belongs on the content roadmap.

I follow this process in one of two ways.
- I export reports directly from the platforms and upload them to Claude.
- If I have connected those platforms through MCP (Model Context Protocol, a standard that allows AI models to connect securely to data sources), I let Claude pull the data directly without manual exports. The workflow changes, but the analysis does not.
Here is the process I use to turn a pile of SEO data into a prioritized content plan.

Step 1: I choose the right competitors

A content gap analysis is only as useful as the competitors I compare myself against. That sounds obvious, but it is one of the easiest places to go wrong.

If I compare my site to Amazon, Reddit, or Wikipedia, I will end up with thousands of keyword “opportunities” that were never realistic in the first place. My goal is not to find every site ranking for my target keywords. My goal is to find businesses competing for the same audience.

I usually start with Semrush’s Organic Competitors report. Instead of relying only on a list of known competitors, I use this report to find domains that compete across many of the same keywords. From there, I narrow the list to three to five sites that closely match the business and target audience I am analyzing.

I do not worry if a few familiar names do not make the cut. Business competitors and organic search competitors are not always the same.

I also filter out sites that can distort the analysis, including large marketplaces like Amazon, community-driven sites like Reddit or Quora, reference sites like Wikipedia, local directories, review sites, and publishers that do not directly compete with the business.

There are exceptions. If I am analyzing a publisher, comparing against other editorial sites makes sense. The key is choosing competitors that create the type of content I am realistically trying to outperform.

A Semrush competitor analysis view turns organic search data into a clear map of rival domains, traffic potential, keyword overlap, and content gap opportunities.

Before I move forward, I sanity-check the competitor list with stakeholders. Sales or product teams may know about newer competitors or strategically important niches that do not yet show up clearly in Semrush.

Once I have settled on the right competitors, I am ready to find the gaps that matter most.

Step 2: I gather and prepare the data

With the competitor list finalized, I collect the data Claude will analyze. Whether I upload exports or connect through MCP, the goal is the same: bring together competitive rankings, my site’s search performance, and engagement data so I can separate meaningful opportunities from noisy keyword lists.

I like to pull data from three core sources.

Semrush: I find the gaps

I start with Semrush’s Keyword Gap tool using the competitors selected in Step 1.

From there, I pay close attention to three buckets: keywords competitors rank for and I do not, keywords where I rank but competitors rank higher, and keywords where I rank but competitors do not.

The first bucket often points to missing topics or content hubs. The second bucket can reveal quicker wins, especially when my site already appears on Page 1 or Page 2. The third bucket shows existing strengths that I should protect and continue building around.

Google Search Console: I validate the opportunity

Next, I check Google Search Console before assuming every missing keyword deserves a new page.

For example, Semrush may show that I do not rank for a keyword, but GSC might reveal that I already receive impressions for closely related queries. That tells me Google has started associating my site with the topic, even if rankings have not caught up yet.

Those “almost there” topics often deserve a higher priority than topics where I would be starting from scratch.

In GSC, I look for queries with high impressions and average positions between 8 and 20, existing pages ranking for related terms, and long-tail queries that reveal additional search intent.

Google Analytics: I add business context

Search volume is only part of the story. Engagement metrics help me answer a more important question: if I improve visibility for this topic, is it likely to support business goals?

A Semrush content gap analysis view reveals where a competitor ranks and the analyzed site does not, turning keyword overlap data into a practical roadmap for SEO content opportunities.

I review metrics such as organic sessions, engagement rate, average engagement time, key events or conversions, and landing page performance.

If a related content hub already drives engaged visitors or conversions, expanding that topic may be a smarter investment than chasing a completely new keyword with higher search volume.

I clean the data before handing it to Claude

If I am manually downloading the data and uploading it to Claude, I clean it first. Claude is excellent at finding patterns, but it can only work with the data I provide. Cleaner data leads to cleaner topic clusters and better recommendations.

I remove duplicate keywords, competitor-branded terms, careers queries, login queries, support queries, locations or product lines outside the business, keywords with clearly different search intent, and high-intent commercial keywords that are too broad to compete for.

For a manual workflow, I export Keyword Gap data from Semrush, query data from Google Search Console, and landing page performance data from Google Analytics, then upload the files to Claude. For a connected MCP workflow, I ask Claude to retrieve the Keyword Gap report, GSC query data, and GA4 landing page metrics directly from connected accounts.

Step 3: I ask Claude to find the story in the data

At this point, I should have a clean dataset that combines competitive keyword gaps, Search Console performance, and Google Analytics data.

This is where the workflow becomes much more useful. Instead of scrolling through thousands of rows looking for patterns, I ask Claude to organize the data into something I can actually build a strategy around.

The mistake I see most often is asking AI to “cluster these keywords.” That usually produces clusters based on keyword similarity alone. That can be useful, but it does not tell me what to do next.

Instead, I ask Claude to think like an SEO strategist. I give it context about the business, including products or services, target audience, primary business goals, content priorities or constraints, and the exported or connected data from Semrush, GSC, and Google Analytics.

Then I ask Claude to organize opportunities by search intent, funnel stage, business relevance, existing authority signals from GSC, user engagement from GA4, recommended content format, and internal linking opportunities.

Rather than returning a spreadsheet of grouped keywords, I want Claude to produce topic clusters with a clear recommendation for each one.

For example, one cluster might be labeled Technical SEO Audits and include supporting keywords, estimated opportunity, existing pages that could be updated, whether a new page is needed, internal linking recommendations, a priority score, and the reasoning behind the recommendation.

A content gap workflow turns scattered SEO signals into topical clusters, showing where AI search visibility, privacy-first analytics, and technical SEO need deeper coverage.

Another cluster might reveal that several competitor keywords can be addressed by expanding an existing guide instead of publishing three separate articles. That is the kind of insight that is hard to spot manually but much easier for AI to surface.

I separate quick wins from long-term investments

Not every opportunity belongs on the same roadmap. As part of my prompt, I ask Claude to classify each cluster into quick wins, new content opportunities, and authority plays.

Quick wins are existing pages that can be refreshed, expanded, or better optimized. New content opportunities are topics that deserve dedicated content because the site has little or no visibility. Authority plays are larger subject areas that may require multiple pieces of content and ongoing investment to compete effectively.

This simple step helps me move from an overwhelming keyword list to a roadmap with both short-term wins and long-term initiatives.

I do not skip the human review

Claude can organize information remarkably well, but it does not know the business the way I do.

Before moving on, I ask whether the topic supports business goals, whether multiple search intents are being combined into one cluster, whether existing content could already satisfy the need, whether the opportunity is realistic given authority and resources, and whether I would actually assign the topic to a writer.

If the answer is no, I refine the cluster or remove it.

The goal is not to accept every AI recommendation. The goal is to spend less time organizing data and more time making strategic decisions.

The biggest prompt lesson is simple: I do not ask Claude to organize keywords. I ask it to recommend what my content strategy should be based on the data I have provided.

Step 4: I score and prioritize the opportunities

Once Claude has grouped the keywords into topic clusters, the next step is deciding what deserves attention first.

This is where many content gap analyses fall apart. Teams naturally gravitate toward the biggest search volumes, but volume is only one piece of the puzzle. A topic that attracts qualified visitors and supports business goals is often a better investment than a high-volume keyword that is difficult to rank for or unlikely to convert.

I score each opportunity across several criteria before I build a roadmap.

A prioritized content gap roadmap turns scattered SEO data into clear next moves, ranking quick wins by impact, effort and AI visibility.

Business relevance

I start with a simple question: if this content performs well, does it help the business?

Topics aligned with products, services, or the customer journey should carry more weight than informational topics with little commercial value.

Existing authority

Next, I look at signals from Google Search Console. If my site already earns impressions or ranks on the second page for related queries, Google has likely established some level of topical authority.

In those cases, improving an existing page or expanding a content hub may produce results much faster than starting from scratch.

Search demand

Search volume matters, but I do not let it dominate the scoring model.

A collection of related long-tail queries with moderate demand can sometimes generate more qualified traffic than one broad keyword.

Ranking difficulty

I review the current search results before committing to a topic. I look at whether authoritative brands dominate the first page, whether the intent is informational, commercial, or transactional, what types of content are ranking, and whether I can realistically create something more useful or complete.

This quick reality check keeps me from chasing opportunities that are not practical.

Estimated effort

Finally, I consider the work involved. Some opportunities require a light refresh of an existing article. Others call for a new content hub supported by multiple pages.

Both can be worthwhile, but they should not carry the same priority when resources are limited.

I let Claude apply the framework

Once I define the scoring criteria, Claude can evaluate every topic cluster consistently.

For example, I may ask Claude to score each opportunity on a five-point scale for business relevance, existing authority, search demand, ranking difficulty, and content effort. Then I ask it to calculate an overall priority score and explain why each recommendation received that score.

A tactical SEO refresh brief turns AI-assisted content gap analysis into page-level priorities, surfacing validation lessons, effort estimates, and the biggest opportunities.

The explanation is just as valuable as the number. If I disagree with a recommendation, I can adjust the weighting, add more business context, and ask Claude to score the opportunities again.

By the end of this step, I have more than a list of content ideas. I have a prioritized content strategy that shows what to tackle next, what can wait, and what is not worth pursuing.

Step 5: I turn priorities into page-level recommendations

Once I have prioritized the opportunities, the next step is figuring out exactly what to change.

Rather than handing a team a ranked list of topics, I ask Claude to generate page-level recommendations for the highest-priority opportunities. This is where connected data becomes especially valuable.

Because Claude has access to Semrush research, Google Search Console performance, Google Analytics metrics, and my prioritization framework, it can evaluate each page in context instead of treating every recommendation the same.

For each priority page, I ask Claude to produce a recommendation that explains why the page was selected, the primary keyword cluster, current rankings and impression data, supporting evidence from GSC and competitor research, recommended updates, estimated effort, expected impact, and priority level.

One of the biggest advantages of this approach is validation.

Before recommending a refresh, Claude can compare URL-level Search Console data against the original analysis. Sometimes what looks like a strong opportunity turns out to be misleading. A keyword may have inflated impression counts, a URL could have been mislabeled in an export, or the page may not be as close to ranking as it first appeared.

Catching those issues before assigning work can save hours of unnecessary effort.

The recommendations also make stakeholder conversations easier. Instead of saying, “I think we should update this page,” I can point to the supporting data, explain why it is a priority, estimate the effort involved, and tie the recommendation back to the larger content strategy.

I treat these recommendations as implementation plans rather than full content briefs. They help SEO and content teams understand what should change, why it matters, and where to focus first. Writers can then use those recommendations to create or update content with confidence.

Step 6: I measure whether the gap is closing

Publishing the content is not the finish line. It is the start of the next round of analysis.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

I begin with Google Search Console, tracking whether target queries are gaining impressions, improving in average position, and generating more clicks. When I refresh an existing page, I compare performance before and after the update to see whether the changes actually moved the needle.

Next, I look at Google Analytics. Better rankings do not always translate into better business outcomes, so I review organic traffic alongside engagement and conversion metrics. If an updated page attracts more visitors but fails to keep them engaged or contribute to conversions, I know it is time for another round of optimization.

If I am using Claude through MCP, I can also ask it to compare performance over time and summarize what changed. I might ask which refreshed pages improved the most, which content clusters gained the most visibility, which recommendations drove the strongest business results, and which opportunities still need attention.

Instead of comparing reports month after month, Claude can quickly surface significant changes and point me toward the pages that deserve attention.

I do not treat content gap analysis as a one-time exercise. Competitors publish new content, search behavior shifts, and my own site authority evolves. I like to repeat this workflow every quarter, or more often in fast-moving industries, so I can keep finding new opportunities and stay ahead of competitors.

The tools will continue to improve, but the repeatable workflow is what creates the advantage.

I build a repeatable content gap analysis process

A content gap analysis helps me prioritize opportunities worth pursuing instead of chasing every possible keyword.

Semrush helps me uncover competitive gaps. Google Search Console shows where I already have momentum. Google Analytics adds the business context that rankings alone cannot provide. Claude brings those datasets together, helping me identify patterns, prioritize opportunities, and create actionable recommendations in a fraction of the time it would take manually.

Whether I upload reports or connect my tools through MCP, the workflow stays the same. I gather the right data, validate the opportunities, let AI organize the information, and apply my own expertise to decide what comes next. That is the part AI cannot replace.

The biggest advantage is not simply having better prompts or faster analysis. It is having a repeatable process that helps a team make smarter content decisions every quarter.

Prompt template: My prioritized content gap roadmap

Here is the prompt I use after I have gathered the data, whether I have uploaded exports from Semrush, Google Search Console, and Google Analytics or connected those tools to Claude through MCP.

“You are an experienced SEO strategist helping me perform a content gap analysis.
I’ll either provide exported reports from Semrush, Google Search Console, and Google Analytics, or you’ll access those tools through connected MCP integrations.
My goal is to identify the highest-impact content opportunities based on competitor visibility, existing authority, business value, and implementation effort.
Here’s my business context:
– Company:
– Industry:
– Products/services:
– Target audience:
– Primary business goals:
– Geographic focus:
– Any strategic priorities or constraints:
– Tone of voice: [Insert brand voice adjectives here (e.g., authoritative, conversational, technical)].
Using the available data, complete the following tasks.
1. Identify content gaps
Organize keywords into these categories:
– Competitors rank and we don’t.
– We rank below competitors.
– We rank and competitors don’t.
Highlight any content gaps, opportunities to consolidate pages, or keyword cannibalization issues.
2. Validate the opportunities
Use Google Search Console data to determine:
– Which topics already receive impressions.
– Which pages rank between positions 8 and 20.
– Which existing URLs have the strongest chance of improving with optimization.
Use Google Analytics data to determine:
– Which pages drive meaningful engagement.
– Which pages contribute to conversions.
– Which content hubs are worth expanding.
3. Create strategic topic clusters
Group related opportunities by:
– Search intent
– Business relevance
– Funnel stage
– Recommended content type
– Internal linking opportunities
Don’t cluster based only on keyword similarity. Focus on topics that should become part of the same content strategy.
4. Prioritize every opportunity
Score each topic cluster using:
– Business relevance
– Existing authority
– Search demand
– Ranking difficulty
– Estimated effort
Assign each opportunity a priority (High, Medium, Low) and explain why.
Separate recommendations into:
– Quick wins
– New content opportunities
– Long-term authority investments
5. Recommend next steps
For every high-priority opportunity, recommend whether we should:
– Refresh an existing page
– Consolidate multiple pages
– Create a new page
– Build a pillar page with supporting content
Include supporting evidence for every recommendation.
6. Deliver the results
Create:
– An executive summary
– Prioritized topic clusters
– A scored opportunity table
– Page-level recommendations for the highest-priority URLs
– A phased implementation roadmap (30, 60, and 90+ days)
If you find conflicting data between Semrush, Google Search Console, and Google Analytics, explain the discrepancy and recommend which source should guide the decision. The output should both be HTML and a Google Sheet.
Before presenting your final recommendations, validate your own analysis. If reviewing Search Console or Analytics data changes your original recommendation, explain why and update your prioritization accordingly.”

This prompt is only a starting point. I add business context, editorial guidelines, and scoring criteria that are unique to the organization I am analyzing. The more context I give Claude, the more useful and actionable its recommendations become.

Inspired by this post on Search Engine Land.
July 8, 2026
My New SEO Stack: Tools I Use for Faster AI Search Wins

I see generative AI and automation creating both excitement and anxiety across the SEO industry. With 87% of Americans reading AI summaries, I believe any SEO team that is not adapting its toolset is already starting to fall behind.

When I move away from rigid enterprise tools and toward agile, AI-driven workflows, I can work faster, spot new search signals earlier, and show clients or internal stakeholders that I understand where search is heading.

In this guide, I’ll walk through what the old SEO stack looked like, what I now add to it, and how I combine both approaches without abandoning the fundamentals that still matter.

Here’s what an old SEO stack looks like

I still believe traditional SEO practices matter because generative AI search experiences continue to depend on core search ranking systems, quality systems, and the broader signals search engines have used for years.

That said, the classic SEO stack was built for a simpler search environment. It usually centered on rank tracking, keyword research, and technical site audits.

Rank trackers

For a long time, I treated keyword rankings as the heartbeat of an SEO campaign. I would add target keywords, monitor SERP positions, and expect higher rankings to translate into more search traffic. But rankings have become far more fragmented.

Now I need to pay attention to AI Overviews, local packs, shopping carousels, and many other search features that can change the value of a ranking completely.

A third-place local pack ranking, for example, may drive two or three times more traffic than a number one ranking in an AI Overview. That makes old-school rank tracking useful, but incomplete.

Keyword tools

Keyword tools still help me understand what people search for, how competitive a topic might be, and which queries match specific user intent. In the past, that information often felt close to a crystal ball.

I would choose keywords based on difficulty, search volume, intent, and other factors. The better the data, the easier it was to shape a campaign around the right opportunities.

The problem is that search volume has always looked backward. A keyword may have shown 10,000 monthly searches last month, but that does not mean it will perform the same way this month. Demand can rise, fall, or shift quickly.

Today, the bigger issue is opportunity loss. A keyword that generated tens of thousands of clicks in 2022 may now be answered directly inside an AI Overview. Even when search volume has not dropped, zero-click behavior can reduce the traffic I can realistically capture.

Site audit tools

I still rely on site audit tools because crawlers still crawl websites, interpret content, and surface technical issues. I need to know whether search engines can access, understand, and navigate the pages I care about.

Audit tools help me find broken links, redirect problems, missing metadata, slow pages, thin content, and other technical issues that can hold a site back.

But I do not expect crawl audits alone to tell me whether my content will appear in AI-driven search experiences. Technical health is necessary, but it is no longer the full picture.

Signals such as brand mentions can influence whether a site is included in LLM outputs from tools like ChatGPT, Claude, and Gemini. Many older site audit tools were not built to track those signals.

That is why I still keep parts of the old stack, but I now add tools and workflows that help me understand AI visibility, brand presence, and faster data-driven decision-making.

Here’s what a new SEO stack looks like

If I am optimizing only for Google’s traditional results, I am missing where search behavior is moving. Between the first and second half of 2025, LLM referral traffic grew by 80%. Conversion rates reached 18%, even though LLM referrals still represented 2% or less of total traffic in the dataset.

That tells me the channel is still small, but meaningful. Now is the time to build a stack that helps me understand, measure, and improve performance across AI-driven discovery.

LLMs

I want my site to appear in LLM responses, but I also use LLMs to strengthen my SEO process. These tools can support analysis, content review, competitor research, metadata refinement, and structured data work.

For example, I can connect ChatGPT with Google Search Console to automate SEO analysis, use Claude to refine copy and conduct content audits, or use Gemini to generate schema markup and compare competitor pages against my own.

I use the LLM that best fits the task, but I keep human oversight in place. These tools help me improve speed and performance; they do not replace judgment, strategy, or editorial review.

The biggest shift is speed. Large datasets that once took hours, days, or weeks to review can now be explored in minutes when I use LLMs carefully and integrate them into a repeatable workflow.

APIs

The old workflow often meant logging into dashboards, exporting CSV files, and cleaning everything in Excel. I still do that when needed, but APIs let me pull data directly from platforms like Google Search Console and Google Analytics.

APIs can sound intimidating, but LLMs make the learning curve easier. I can use them to help with authentication, JSON parsing, and the basic structure of repeatable data workflows.

Once I can connect to APIs, I can stop waiting on manual exports and start building faster reporting, monitoring, and analysis systems around the data I already use.

Lightweight scripts

Python scripts are now within reach for many SEOs, especially with tools like Claude Code and similar coding support inside ChatGPT or Gemini. I do not need to be a full-time developer to automate repetitive SEO work.

I can create scripts that pull top pages from Google Search Console, compare title tags against character limits, flag 30-day performance changes, or generate a clean CSV output for review.

Instead of waiting for a vendor to add the exact feature I need, I can build a small script that removes a bottleneck. A hundred-line script can replace hours of manual work without requiring another SaaS license.

I also like that scripts make the logic visible. If I hand the workflow to another teammate, they can inspect what the script does and understand how the output was created.

Notebooks and local workflows

SEO teams usually have data scattered across shared folders, Google Sheets, Notion docs, monthly CSV dumps, and long-running audit trackers. I have seen how quickly that fragmentation slows decisions down.

Notebooks and local workflows help me turn scattered files into a working system. A script can pull the data, an API can surface the signal, and an LLM can help interpret the results before the output lands in a notebook or spreadsheet.

The value is consistency. I get cleaner data formats, shared access, and documented logic instead of rebuilding the same process every time someone needs a report or audit update.

As search optimization becomes more connected to generative AI, I need workflows that scale. Local workflows help me keep data consistent while giving the team a faster way to act on what we find.

Creating hybrid workflows that mix old and new SEO stacks

I do not think the old SEO stack is obsolete. I also do not think the new tools replace everything. The strongest approach is a hybrid workflow that keeps proven SEO fundamentals while adding AI, APIs, scripts, and notebooks where they create real leverage.

Tool + custom script + AI layer

To build a practical hybrid workflow, I would start with a familiar audit tool such as Screaming Frog, then run a Python script that joins the crawl data with Google Search Console data.

From there, I could flag pages with high impressions and low clicks, send those pages to an LLM for title and intent analysis, place the output into a notebook or spreadsheet for editors, and turn approved recommendations into change logs.

Work like this used to take weeks, so many teams pushed it aside. At enterprise scale, the amount of data could easily become overwhelming. With a hybrid SEO stack, I can complete larger projects in a fraction of the time.

For me, the goal is not to chase every new tool. The goal is to build a more agile SEO stack that can handle today’s massive datasets, identify AI search signals, and help teams move faster without losing the core SEO basics.

Inspired by this post on Search Engine Land.

July 3, 2026

How I Use Vibe Coding to Build Practical SEO Tools

I see vibe coding as one of the most accessible ways to create small pieces of software with AI tools like ChatGPT, Cursor, Replit, and Gemini. Instead of writing code line by line, I describe what I want in plain language, receive working code in return, paste it into an environment like Google Colab, run it, and test the result.

Collins Dictionary named “vibe coding” word of the year in 2025, defining it as “the use of artificial intelligence prompted by natural language to write computer code.”

In this guide, I’ll explain how I approach vibe coding, where I think it works well, where it breaks down, and which SEO examples can inspire practical projects of your own.

Vibe coding variations

I use “vibe coding” as a broad term, but it helps to separate it from nearby approaches:

Type	Description	Tools
AI-assisted coding	AI helps write, refactor, explain, or debug code. I usually associate this with developers or engineers who already understand the systems they are building.	GitHub Copilot, Cursor, Claude, Google AI Studio
Vibe coding	AI handles most of the work after I provide the idea or prompt.	ChatGPT, Replit, Gemini, Google AI Studio
No-code platforms	Platforms handle what I ask for through visual interfaces, drag-and-drop workflows, or background automation. Many now use AI, but they existed before AI became mainstream.	Notion, Zapier, Wix

For this guide, I’m focusing only on vibe coding.

The barrier to entry is low. In most cases, I only need a ChatGPT account, free or paid, and access to a Google account. Depending on the project, I may also need API access or subscriptions to SEO tools such as Semrush or Screaming Frog.

I also like to set expectations early: by the end of this kind of workflow, I’m usually aiming to run a small program in the cloud. If I want to build a SaaS product or software I plan to sell, I treat AI-assisted coding as the more realistic path because it usually requires more technical knowledge, more testing, and more budget.

Vibe coding use cases

I find vibe coding most useful when I’m working with clear buckets of data and need a helpful outcome, not a perfect one. That might mean finding related internal links, adding pre-selected tags to articles, comparing groups of URLs, or building something playful where the output does not need to be exact.

For example, I built an app that creates a daily drawing for my daughter. I type a short phrase about something she told me, such as “I had carrot cake at daycare.” The app uses examples of drawing styles I like and a few pictures of her, then generates a drawing as the final output.

When I ask for precise changes, the tool often gets worse. I once asked it to remove a mustache, and instead it recolored the image. That is exactly the kind of limitation I expect with this approach.

If my daughter were a client reviewing every detail, I would need someone with Photoshop or similar skills to make exact edits. For this use case, though, the result is good enough, and that is where vibe coding shines.

I would be cautious about building commercial applications solely through vibe coding. Some companies may even need vibe coding cleaners to clean up AI-generated work. But for demos, MVPs, internal tools, and quick experiments, I see vibe coding as a useful shortcut.

How I create SEO tools with vibe coding

When I create an SEO tool with vibe coding, I usually follow three steps:

I write a prompt describing the code I need.
I paste the code into a tool such as Google Colab.
I run the code and check whether the results match what I expected.

Here’s a real prompt example from a tool I built to map related links at scale. After crawling a website with Screaming Frog and extracting vector embeddings through the crawler’s OpenAI integration, I vibe coded a tool to compare topical distance between the vectors for each URL.

This is exactly what I wrote in ChatGPT:

I need a Google Colab code that will use OpenAI to:

Check the vector embeddings existing in column C. Use cosine similarity to match with two suggestions from each locale (locale identified in Column A).

The goal is to find which pages from each locale are the most similar to each other, so we can add hreflang between these pages.

I’ll upload a CSV with these columns and expect a CSV in return with the answers.

After ChatGPT generated the code, I pasted it into Google Colab, which is a free Jupyter Notebook environment for running Python in a browser. I then used “Run all” to test whether the program produced the output I wanted.

That is the clean version of the process. In practice, AI can make the workflow look perfect while still producing code that does not behave the way I need.

I expect issues along the way, and most of them are simple to troubleshoot if I keep the prompt and testing process clear.

First, I always state the platform I’m using. If I want code for Google Colab, I say that directly in the prompt.

Sometimes I still get code that depends on packages that are not installed. When that happens, I paste the error back into ChatGPT and ask it to fix the code or suggest an alternative. I do not need to fully understand the missing package to move forward. I can also ask Gemini inside Google Colab to identify the problem and update the code directly.

I also check outputs carefully because AI can sound confident while inventing data. One time, I forgot to specify that the source data would come from a CSV file, so the tool created fake URLs, traffic, and graphs. “It looks good” is not the same as “it is correct.”

If I connect to an API, especially a paid API from a provider such as Semrush, OpenAI, Google Cloud, or another platform, I need to request my own API key and keep usage costs in mind.

A Semrush subscription screen highlights 2 million Standard API units and a masked API key, underscoring the setup step needed for SEO automation and vibe-coded tools.

If I want an even lower execution barrier than Google Colab, I can use Replit.

With Replit, I can prompt what I want, and the platform can generate the code, design the interface, and let me test everything in one place. That reduces copy-and-paste work and gives me a shareable URL quickly. I still need to review poor outputs and keep iterating until the app behaves properly.

The tradeoff is cost. Google Colab is free unless I use paid API keys, while Replit charges a monthly subscription and usage-based API fees. The more the app runs, the more expensive it can become.

SEO vibe-coded tools that inspire me

Google Colab is the easiest place for me to start, but SEOs are taking vibe coding much further. I’ve seen people create Chrome extensions, Google Sheets automations, and even browser games.

I’m sharing these examples because they show what is possible when useful SEO ideas meet practical AI tooling. If I see a tool and wish it had a different feature, that is often a sign that I could try building a version for myself.

A Replit vibe-coding session turns a simple prompt for toddler-friendly daily jokes into a published web app, illustrating how AI tools can quickly prototype playful ideas.

GBP Reviews Sentiment Analyzer by Celeste Gonzalez

After vibe coding SEO tools in Google Colab, Celeste Gonzalez, Director of SEO Testing at RicketyRoo Inc, pushed the idea further by creating a Chrome extension. “I realized that I don’t need to build something big, just something useful,” she explained.

Her extension, the GBP Reviews Sentiment Analyzer, summarizes sentiment analysis from reviews over the last 30 days and shows review velocity. It also exports the information to CSV and works on Google Maps and Google Business Profile pages.

Instead of relying only on ChatGPT, Celeste used Claude to create stronger prompts and Cursor to turn those prompts into code.

AI tools used: Claude (Sunner 4.5 model) and Cursor

APIs used: Google Business Profile API (free)

A vibe-coded GBP Sentiment Analyzer turns review data into a quick snapshot, showing negative sentiment trends, key topics, and an export option for SEO workflows.

Platform hosting: Chrome Extension

Knowledge Panel Tracker by Gus Pelogia

I became obsessed with the Knowledge Graph in 2022, when I learned how to create and manage my own knowledge panel. Later, I discovered that Google’s Knowledge Graph Search API lets me check the confidence score for any entity.

That led me to build a vibe-coded tracker that checks entity scores daily, or at any frequency I choose, and returns the results in a Google Sheet. I can track multiple entities at once and add new ones whenever I need to.

The Knowledge Panel Tracker runs entirely in Google Sheets, and the Knowledge Graph Search API is free to use. This guide explains how to create and run it in your own Google account, or you can see the spreadsheet here and update the API key under Extensions > App Scripts.

AI models used: ChatGPT 5.1

A spreadsheet-based Knowledge Panel Tracker turns entity searches into structured SEO data, comparing names, entity types, descriptions, and confidence scores at a glance.

APIs used: Google Knowledge Graph API (free)

Platform hosting: Google Sheets

Inbox Hero Game by Vince Nero

I also like the idea of vibe coding a link building asset. That is what Vince Nero from BuzzStream did with the Inbox Hero Game. The game asks players to use the keyboard to accept or reject a pitch within seconds, and it ends if they accept too many bad pitches.

Inbox Hero Game is more complex than running a small script in Google Colab, and it took Vince about 20 hours to build from scratch. “I learned you have to build things in pieces. Design the guy first, then the backgrounds, then one aspect of the game mechanics, etc.,” he said.

The game was built with HTML, CSS, and JavaScript. “I uploaded the files to GitHub to make it work. ChatGPT walked me through everything,” Vince explained.

A retro arcade-style Inbox Hero screen turns PR pitch triage into a fast keyboard game, challenging players to accept or reject emails before time runs out.

He also found that longer prompt threads became less useful over time, “to the point where [he’d] have to restart in a new chat.”

That became one of the hardest parts of the project. Vince would add a feature, such as a score, and ChatGPT would “guarantee” it had found the error, update the file, and still return the same problem.

In the end, Inbox Hero Game shows that it is possible to create a simple game without coding knowledge. It also shows where a developer becomes valuable when the goal shifts from “working prototype” to polished product.

AI models used: ChatGPT

APIs used: None

Platform hosting: Webpage

How I think about vibe coding with intent

I do not expect vibe coding to replace developers, and I do not think it should. What I do see is a practical way for SEOs to prototype ideas, automate repetitive tasks, and explore creative experiments without a heavy technical lift.

The key is realism. I use vibe coding where precision is not mission-critical, I validate outputs carefully, and I stay alert for the moment when a project grows beyond “good enough” and needs stronger technical support.

When I approach vibe coding thoughtfully, it becomes less about shipping perfect software and more about expanding what I can test. For internal tools, proofs of concept, and SEO side projects, the best results come from pairing curiosity with restraint.

Inspired by this post on Search Engine Land.

July 3, 2026

Deciding to Build or Buy Your Next SEO Tool with AI Insights
Before I consider requesting a new SEO tool, I always ensure that I understand the trade-offs between custom solutions, SaaS platforms, and hybrid approaches that utilize both.

AI has empowered SEO teams, including mine, to become more ambitious about automation. Tasks that once required engineering support are now tackled easily with tools like Claude or ChatGPT.

This is thrilling, yet it brings a new challenge: the assumption that everything can be automated. In today’s language, it boils down to a single question: Do we build or buy the tool?

The build-versus-buy dilemma is intricate, made even more so by AI advancements. It isn’t merely about cost; it’s about security, maintenance, data access, internal capabilities, workflow fit, and whether a custom solution can stay reliable and useful as time progresses.

How AI Lowers the Barrier to Building

AI has drastically lowered the barrier to experimentation. Even those of us without technical know-how can now create custom GPTs, build workflows, connect data sources, or craft an internal AI assistant.

However, maintaining a tool over the years remains a challenge, even if I managed to build it initially with AI support.

AI significantly aids SEO teams in data analysis, pattern recognition, summarizing information, and recommending actions, saving us a lot of time. Ignoring AI would surely leave us trailing behind.

It’s essential to acknowledge that AI still hasn’t reached the level of human creativity. It excels at working from established patterns and predicting outputs. This could evolve in the coming years.

AI tools also come with unseen costs. Internally developed tools may appear free since their invoices typically bypass our SEO teams, but expenses from token usage, API calls, infrastructure, engineering time, security reviews, and maintenance do exist.

Many organizations, as noted by Reuters, are experiencing “AI sticker shock,” finding themselves unable to forecast usage-based AI costs accurately. Companies like Uber, reported by TechCrunch, have even established AI spending caps after exceeding their annual budget in only a few months.

Currently, marketing teams, including mine, aren’t the largest AI consumers compared to engineering teams. Yet, this could shift rapidly.

When this happens, our expenditures will undoubtedly rise, prompting organizations to evaluate which AI tools and processes genuinely add value as opposed to simply consuming our budget.

Start by Defining What You Need

Before choosing whether to build or buy, SEO teams must define their true needs.

Different Ways to Use AI and Automation

I’ve noticed that many teams, including ours, lump various solutions together, yet they differ in cost, complexity, and maintenance.
- A custom tool: Generally a complex internal system necessitating engineering support, often focusing on automation and potentially incorporating AI aspects.
- A custom workflow: A repeatable process built with numerous tools like a custom GPT, spreadsheets, and automation, usually with an AI layer.
- A custom layer on SaaS: Leveraging data from existing tools to shape personalized reporting, prioritization, or recommendation processes.
- A true AI agent: A system capable of taking more autonomous actions, such as scanning Slack and following up on pending communications.
Though similar, these are often misidentified. Overgeneralizing terms like “AI agent” can lead to cost and complexity misjudgments.

Look for Repetitive, Context-Rich Tasks

Our team is still exploring AI capabilities. So far, we have concentrated on daily tasks involving substantial manual work.

For instance, we developed a custom GPT to assess whether our content aligns with our personas and addresses their pain points. The aim is not to replace our copywriters or reviewers, but to ensure that content isn’t generic and suggest pertinent enhancements.

We’ve also leveraged AI for translations, monthly reporting, and creating a weekly summary that integrates meeting notes, Slack, and Jira to identify outstanding tasks or follow-ups.

One of our newest workflows converts internal meeting recordings into structured landing page briefs.

Such tasks are ideal candidates for AI-powered custom workflows, given their dependence on internal context, repeatability, and specific company knowledge.

Not Everything Should Be Built

A case from our team involved a colleague who vibe-coded a prompt tracking tool. Although a good start, data presentation required manual steps for trend graphing, soon becoming a maintenance hassle due to changes in LLM tools.

The core issue was reliability. For AI visibility and prompt tracking, we needed stable data presentation, leading us to switch to a specialized platform like Peec AI, rather than maintain our own version.

This experience was insightful, enhancing our understanding of the problem, complexities, and necessary features when considering external solutions.

Here’s my advice: whether opting to build or purchase a tool, always explore existing market solutions. It helps to narrow down the essential features, preventing reliance on non-essential ones.

Especially for business-critical tools like rank tracking and website crawling, smaller SEO teams without technical support should be cautious of building from scratch. Reliability should be prioritized when data is crucial for decision-making.

Use AI Where Your Data Already Lives

Consider buying a crawler, rank tracker, or AI visibility platform and focus on linking these with custom data like GA or GSC accounts, or CRM data. This integration allows comprehensive analysis in a single view.

MCP connections also warrant consideration. The Model Context Protocol is a standard for linking AI applications with external systems, enhancing current workflows.

Though not necessary to learn coding, understanding enough to ask the right questions is beneficial.

If sensitive data is involved, like proprietary research or customer details, it’s crucial to assess security risks. It may be safer to allocate engineer support to avoid compromising sensitive information.

Deciding on a custom tool requires acknowledging the full cost, including engineering time, security reviews, and API usage, despite invoices not being SEO-related.

Before requesting any tool, SEO teams should articulate the problem, expected value, cost comparison between building and buying, and potential consequences of taking no action.

Effective requests should not start with tool needs, but with the problem, its significance, tested solutions, and the proposed optimal solution.

How to Prioritize What to Build First

No one-size-fits-all matrix exists for prioritizing builds.

Tools vary; from website crawlers to content evaluation systems, each can’t be judged by identical criteria.

In doubt, start by mapping current workflows versus the ideal ones. Patterns often emerge, highlighting primary priorities.

The first group involves tools that aid revenue generation, like identifying content opportunities or improving conversion. Marketing, including SEO, seeks visibility and leads, thus revenue-centric tools can be higher priorities.

The second category concerns tools minimizing repetitive tasks. While they may not directly create revenue, they free up valuable team time for strategic work.

Quick wins should not be ignored. Stakeholders value timely results, thus a small project with potential returns within weeks can build trust and support larger initiatives.

Also, consider cross-team value in your decision. SEO problems often extend beyond one team. Collaborating with other teams can strengthen the business case for shared solutions.

Often, the best tool isn’t the most complex. Starting small could be the strategy for smarter progress.

Remember, effective scoping leads to good decisions. Even with AI easing the build process, proper scoping of what to build remains essential.
- Define the problem, expected value, user base, and post-launch maintenance.
- Engage with your team and other departments, identifying whether it’s solely an SEO issue or a broader business challenge.
- Avoid building for AI’s sake, or being swayed by impressive demos.
Neglecting scoping risks acquiring costly tools that don’t integrate with workflows or building internal tools beyond maintenance capabilities.

Thoughtful consideration of scope is crucial before opting to build, buy, or customize a solution.

Inspired by this post on Search Engine Land.
June 23, 2026
Google’s New SEO Guidelines: A Personal Take on Third-Party Tools & AI
When I heard that Google had added a new help document to its search developer documentation, I knew I needed to dive in. This new document, “Google Search’s guidance on using third-party SEO tools, services, and advice,” provides updated insights into the world of SEO, especially revolving around the hot topic of generative AI optimization.

Google also revamped its “Do you need an SEO?” guide, adding fresh content around generative AI topics. The intent behind these updates, as stated by Google, is to highlight what to consider when evaluating third-party tools and to simplify existing documentation. They want us to be cautious about trusting these tools and advice without proper verification.

Reading through Google’s new guidance, I found some valuable advice on thoughtfully evaluating third-party SEO services. Here’s how they suggest approaching it:

Evaluate external SEO advice against Google’s official guidelines, think critically about third-party tools, and always verify the claims made by these services.
- Evaluate and verify external SEO advice against official Google guidelines
- Think critically about using third-party SEO tools and services
  - Assisting in sitemap generation
  - Establishing indexing directives
  - Offering to generate “SEO-optimized” content for you
  - Providing advice to improve the ranking of existing content
  - Promising improvements for AI experiences and search formats (“AEO” or “GEO” tools)
While Google doesn’t endorse any third-party tools, they emphasized using Google Search Console for credible data directly from Google Search. We need to be wary of tools claiming to guarantee success since they lack access to Google’s internal ranking data.

With the updated “Do you need an SEO?” document, Google has also covered topics like Optimizing for generative AI. It includes essential reminders that if an SEO uses a third-party tool, one should not assume it’s approved by Google, and during audits, access to Search Console should be limited initially.

In essence, before making any site changes based on third-party audits, it’s crucial to cross-reference their advice with Google’s official resources, especially when it comes to AI optimization strategies.
- If your SEO offers an audit, scrutinize what’s involved and avoid granting write access to Search Console at first.
- Ensure recommendations align with Google’s SEO starter guide and critically evaluate their claims.
- If optimizing for AI, verify alignment with official AI optimization guidance.
Understanding these updates helps us not only in improving our own SEO strategies but also in promoting ethical and effective use of tools.

The document updates come as a reminder for us to regularly check Google’s official documentation. Staying informed about new guidelines ensures that we’re always on the right path in our SEO journey.

Inspired by this post on Search Engine Land.
June 7, 2026
Boost Your AI Visibility: Discover Top 5 Sources for FAQ Content

Have you ever wondered where to find the best questions to boost your AI visibility? Trust me, you’re not alone. In this guide, I’m going to share five amazing places to uncover FAQ content that can significantly enhance your AI search presence.

Gone are the days when FAQs were hidden away on support pages. Now, they play a crucial role across AI Overviews, People Also Ask results, and more. Did you know more than 80% of AI Overview queries are informational, with most having search volumes under 1,000? This highlights the rising importance of longer-tail queries for AI visibility.

With search evolving to be more conversational, refining FAQ strategies based on quality questions is key. However, many brands still rely on outdated sources for FAQ insights. Let me show you five sources to prioritize more relevant FAQ opportunities.

1. Google Search Console data

We often overlook the wealth of information available in Google Search Console. Before brainstorming new FAQs, audit what’s gaining traction. Google Search Console is underutilized because many filter for high impressions or clicks rather than intent-driven queries.

Start by filtering for question-based search patterns using regex:

^(who|what|where|when|why|how|which|whose|whom|is|are|was|were|do|does|did|can|could|will|would|should|has|have|had)b

Check the average position against CTR to find FAQs worth fleshing out. Looking for long-tail queries? Use this regex to filter for lengthy queries:

^(S+s+){8,}S+$

2. People Also Ask data

The People Also Ask feature is invaluable for understanding audience queries. Tools like AnswerThePublic help map these question trees, offering insights into related FAQs that can enhance existing content.

3. Customer-facing teams and internal data

Your internal data, especially from customer service teams, is a goldmine for FAQ ideas. They hear real questions daily, providing insights into what drives or hinders conversions.

Utilizing site search data also uncovers what visitors really want but can’t find, paving the way for content that meets user intent.

4. Reddit

On Reddit, people discuss products and services in their own words. This platform is a treasure trove for discovering how your audience thinks and what they care about.

5. AI prompt volumes

Leveraging AI prompt data can reveal emerging questions before they reach traditional search. Tools like Writesonic provide insights into what people are asking within AI platforms.

Remember, crafting FAQs is an ongoing process. Continuously updating your FAQ content according to new audience queries will keep you ahead in AI visibility.

Inspired by this post on Search Engine Land.

May 28, 2026
Master Your Brand with a Strategic Martech Stack

Struggling with maintaining brand consistency? I’ve learned that it’s not about having more tools, but rather having the right tools, perfectly aligned with your brand’s goals.

I’ve seen marketing teams overwhelmed with tools. The average B2B company might use up to 20 different martech solutions. Despite this, keeping brand consistency at scale can be tough. Fewer than 10% of brands manage to maintain strong cohesiveness across all products and channels. The core issue? Tools rarely work in harmony to support a unified brand experience.

Managing a brand across various channels, whether through campaigns or social media, can lead to brand elements drifting. It’s those small inconsistencies—a slightly off-color logo here, outdated messaging there—that can gradually erode the hard-earned brand equity.

The solution isn’t about increasing the number of tools. It’s about selecting the right ones and arranging them with deliberate intention.

Start with strategy, then stack

Before diving into an audit of your current software or seeking out new options, it’s crucial to develop a framework for what brand equity means to your organization. David Aaker’s brand equity model—which focuses on loyalty, awareness, perceived quality, and brand associations—is a sound approach. It transforms brand management into a sustainable growth strategy. In terms of a martech stack, this means utilizing tools that both build and protect your brand.

On the strategy side, platforms like Notion, Miro, and Lucidchart are invaluable. They help document positioning, define messaging, and map out customer journeys. These may not be glamorous, but they provide the solid foundation for successful execution. Without such a framework, design and content teams are left guessing.

The core of the stack: Digital asset management

If there’s one tool that differentiates a cohesive brand management stack from fragmented apps, it’s digital asset management (DAM). Unlike typical cloud storage services such as Google Drive or Dropbox, a DAM solution organizes and governs brand assets comprehensively, offering features like approval workflows and version management that cloud storage lacks.

Consistent branding can increase revenue by 10–20%, and a DAM provides the structure needed to maintain this consistency at scale. By ensuring all team members and partners access the same approved asset library, you eliminate brand drift.

Modern DAMs further simplify brand management by integrating AI to speed up content discovery and automated metadata tagging, reducing creative bottlenecks and accelerating go-to-market timelines.

Execution tools that reinforce brand standards

Apart from DAM, execution tools are essential for converting brand strategy into consistent published content. Depending on your team, Adobe Creative Cloud, Figma, or Canva can be used. They offer varying degrees of design flexibility and guardrails to maintain brand standards.

Balancing creativity with adherence to brand guidelines is key. Tools with brand templating features allow teams autonomy while ensuring brand consistency. Alternatively, using brand templates within your DAM offers greater control and tracking capabilities.

For social media and content distribution, platforms like Hootsuite and HubSpot ensure cohesive publishing across channels. It’s crucial these tools connect to your DAM to guarantee only brand-approved content is shared widely.

SEO tools like SEMrush and Ahrefs help reinforce your brand’s voice and authority online. In today’s market, where SEO extends to geo-targeting, it’s vital to ensure your brand is accurately represented from the start of customer interaction.

Governance closes the loop

A martech stack without governance is simply a mix of tools. Governance—including approval workflows and brand monitoring—is what makes your stack effective and protective.

Incorporating workflow tools into project management or your DAM ensures faster and accountable proofing cycles. Tools like Mention help track external brand perception, highlighting areas of potential drift before they escalate.

The takeaway

The aim of a streamlined brand management martech stack is not complexity but efficiency. It should empower any team member or partner to access and create on-brand content swiftly, independently, and without needing constant design team input.

This requires a strategic approach, a robust DAM as the central hub, integration with execution tools, and governance practices that uphold standards. When these elements work together, your brand transforms from a reactive endeavor to a proactive tool for long-term success.

Inspired by this post on Search Engine Land.

May 15, 2026
Mastering Effective SEO Agent Skills: A Personal Journey
I’ve been on a journey to develop over 10 SEO agent skills in just 34 days. Six of these succeeded on the first attempt, while the remaining four taught me invaluable lessons, especially about the overlooked importance of folder structure that many LinkedIn posts on AI SEO skills seem to miss.

The reliability of these agents isn’t about crafting superior prompts; it lies in the architecture that supports them. Here’s my blueprint for building an agent from scratch, testing it diligently, refining it, and deploying it with full confidence.

Here’s why many AI SEO skills don’t make the cut.

A typical AI SEO prompt seen on platforms like LinkedIn usually looks something like this:
```
You are an SEO expert. Analyze the following website and provide a comprehensive audit with recommendations.
```
And that’s where it ends. One simple prompt, often coupled with some formatting directions, is shared with the world. The post then earns hundreds of likes, yet the output—while polished—is often up to 40% inaccurate.

I know because I’ve been there. Initially, I tasked an agent to identify SEO issues on a website, and while it came back with 20 findings, eight were non-existent. The agent hadn’t truly visited many of the reported URLs.

Here are three key issues that doom single-prompt skills:
- No tools: The agent can’t physically verify the website; it relies on training data to guess. Queries about canonical tags, for instance, result in assumptions rather than real-time analysis of HTML.
- No verification: There’s no check on the truthfulness of output. An agent might report missing meta descriptions across 15 pages, but without verification, we don’t know if these pages are even indexed correctly or intentionally set as noindexed.
- No memory: The agent’s feedback varies wildly with each use, showing inconsistency due to the lack of a template or structured history of previous runs.
In essence, if your skill is just a prompt within a lone file, you’ve got a 50/50 chance at best.

Every agent in my system has a dedicated workspace. Consider it akin to a new employee’s desk, equipped with all necessary resources. For example, our agent designed to crawl and map website architecture works within this kind of structured environment:
```
agent-workspace/
  AGENTS.md          instructions, rules, output format
  SOUL.md            personality, principles, quality bar
  scripts/
    crawl_site.js    tool the agent calls to crawl
    parse_sitemap.sh tool to read XML sitemaps
  references/
    criteria.md      what counts as an issue vs noise
    gotchas.md       known false positives to watch for
  memory/
    runs.log         past execution history
  templates/
    output.md        expected output structure
```
The workspace includes six key components services that just one prompt couldn’t dream of covering fully.

Within AGENTS.md, I’ve articulated a meticulous methodology comprising thousands of words. Instead of a simple instruction like “crawl the site,” I detailed each step: “Start with the sitemap; if it doesn’t exist, check various routes like /sitemap.xml, /sitemap_index.xml, and robots.txt for references.”

Scripts represent the tools the agent utilizes. Instead of writing curl commands from scratch for each crawl, the agent can run node crawl_site.js -url to analyze website data, which is far more efficient and reliable.

References consist of criteria that help the agent distinguish between significant issues and noisy false positives, using a wealth of knowledge I’ve amassed over two decades.

To ensure that every execution is informed by the past, I keep meticulous logs under memory, serving as institutional knowledge that empowers consistency across agent runs.

Through templates, I outline the exact format I expect from the output, thereby maintaining high quality across multiple iterations of the same task.

Building from scratch, the first naive attempt involved simple instructions that inevitably failed when confronted with modern CDNs. By iterating and incorporating tools like crawl_site.js, enhancing with rate limiting, and tackling JavaScript rendering, I’ve honed an architecture that delivers consistent outputs across runs.

The path involves a series of iterations where each failure metamorphoses into a permanent lesson, gradually shaping a sophisticated system. This methodically structured approach ensures that what we build is not just technically proficient but measurably better with every successive run.

Inspired by this post on Search Engine Land.
May 1, 2026
Bing Webmaster Tools Unveils Exciting AI Reporting Enhancements

During a recent presentation, I was thrilled to learn about Microsoft’s latest tease regarding new AI reporting features in Bing Webmaster Tools. These updates aim to enhance the existing AI performance reports, offering fascinating insights into citation share, query intent grounding, and GEO-focused recommendations.

I stumbled upon shared screenshots from this intriguing presentation delivered by Krishna Madhavan at SEO Week in the bustling city of New York. Azeem Ahmad captured the essence of this moment, highlighting the growing transparency gap between Bing and Google.

Intriguing Details: The presentation shared several slides showcasing these promising new features. One can feel the excitement building within the SEO community as these innovations hint at a more insightful way to track AI interactions.

Bing Webmaster Tools just dropped some VERY COOL stuff at #SEOWeek 2026

Citation Share, Grounding Query Intent (15 pre-defined intents), and GEO-focused recommendations.

The gap between Bing's transparency and Google's is getting harder to ignore.

Cc @rustybrick pic.twitter.com/kOMhVyQvpQ— Azeem Ahmad (@AzeemDigital) April 27, 2026

Bing webmaster tools owning SEO & GEO @kmadhavan77

Citarion share
Intent
Topics
Geo recomendaciones

Exclusive for #seoweek pic.twitter.com/H2arlFtS8R— MJ Cachón (@mjcachon) April 27, 2026

Stay Tuned: While these features aren’t live just yet, catching a glimpse of them was very promising. It seems Microsoft is ramping up to offer more ways to navigate AI-driven search results.

Why This Matters: Gaining more transparency on how our content performs in AI search results is invaluable. I eagerly anticipate the day when these tools go live, promising greater clarity and control over AI interactions.

At the moment, details on the exact functionality and release timeline remain vague. I will certainly keep my eyes peeled for further updates to better understand their full potential.

Inspired by this post on Search Engine Land.

April 27, 2026
Mastering SEO Reporting: Move Beyond Data Studio

As I delve into the world of SEO reporting, I realize just how much we’ve outgrown platforms like Data Studio. Let me share what I’ve discovered and the exciting changes on the horizon that promise more efficient workflows powered by AI and APIs.

Imagine this scenario: Our team depends on Data Studio for delivering SEO reports. Just as we’re gearing up for a crucial meeting, Data Studio unexpectedly crashes, leaving us with nothing to showcase. It’s frustratingly common and incredibly embarrassing.

Just last year, I was praising Looker Studio (now Data Studio) for its advantages in SEO reporting. Fast forward, and it seems outdated compared to the dynamic coding tools I’m now utilizing. Here’s why rigid dashboards are holding us back and why transitioning to code-driven SEO reporting is essential.

Data Studio once reigned supreme for customizing SEO reports, but technology advanced, revealing its limitations. From dataset crashes to tedious manual interfaces, let me take you through some challenges I’ve faced with Data Studio.

We’re all familiar with the struggle: vast datasets in Data Studio are prone to breaking, often due to the low limits on rows and fields. Hasn’t it been just one too many times when a minor data addition causes everything to crash?

Manual updates in a slow interface make any iteration seem endless. Even the introduction of AI features addresses only a fraction of report-building issues.

Debugging Data Studio reports feels like a never-ending click maze. Unlike code-based systems where agents breeze through files, I’m often left clicking mindlessly within the interface.

Data Studio’s weak API is another stumbling block. It’s representative of Google’s missed opportunities for API-centric platforms. This flaw severely limits external management capabilities.

Despite recent rebranding efforts, these platforms lag behind modern SEO reporting technologies. Let me show you how everything is shifting with AI, APIs, and coding.

The evolution we’re witnessing is astounding. AI-driven coding tools like Claude Code and OpenAI Codex have changed the game. I describe my SEO reporting needs, and these tools take over, executing multi-step workflows efficiently.

Without needing deep coding expertise, I’m able to set up programmatic report workflows from beginning to end. Tools generate code that directly connects to data sources, eliminating reliance on cumbersome dashboard connectors.

Within minutes, comprehensive reports appear as I get accustomed to these tools. Each offers unique advantages, from reasoning to integration speed, transforming manual, rigid processes into infinitely flexible options.

AI coding tools usher in new possibilities for SEO teams by removing barriers between data management and reporting.

Speed is an unmistakable upside. Coding assistants enable SEOs to achieve in hours what once took days, and what took hours, now takes minutes.

Interacting with data directly through coding instead of dashboard interfaces drastically cuts down wait times for refreshes and modifications.

I’m no longer bound by rigid templates. Alongside on-demand data plotting and diverse frameworks, I can tailor reports to perfectly match needs and provide insightful visualizations.

Setting up these tools requires some initial effort but soon transforms the team’s efficiency, offering clearer data constraints and enhanced process transparency.

I’ve discovered how agentic coding assistants can revolutionize real-world SEO applications, from pre-meeting reports to ad hoc stakeholder requests, reducing late-night work and ensuring quick, reliable data access.

AI is reshaping the landscape for all professionals, not just us in SEO. As we adopt this technology, especially in SEO reporting, studies from Stanford and MIT show increased productivity. The shift isn’t optional; it’s imperative.

Teams leveraging AI tools in SEO witness faster iterations and can tackle complex issues more robustly, transforming analysts into strategists with unprecedented capabilities.

Begin this transformation with a small, repeatable project, connect data sources, and slowly expand your use of code-driven reporting. Early adopters are set to lead in SEO efficiency and results.

Traditional SEO reporting tools no longer meet the fast-paced demands of today’s analytics and strategic needs. Through AI and coding, we can leap ahead in reporting accuracy and timeliness, securing a competitive edge.

Inspired by this post on Search Engine Land.

April 21, 2026