Playbooks your agent follows, not improvises

Start here for any SEO or organic search question, including vague ones: something feels off with our Google numbers, how is our site doing in search, audit our SEO, give me an honest summary of SEO for leadership, why did traffic or rankings drop, what should we fix or write next. Covers Search Console and GA4 organic data, keywords and rankings, CTR, cannibalization, content decay, competitors and backlinks, keyword gaps, indexing and sitemaps, technical audits, redirects, metadata, internal links, AI crawlers, llms.txt and AI search visibility, and which organic pages convert or earn revenue. Connects the right accounts, routes to the focused playbook, and sets the evidence rules every SEO answer follows.

See which pages perform best in organic search: the landing pages that earn the most clicks and impressions, the queries behind each one, and how they compare with competitor estimates from Ahrefs.

Audit the site's own internal linking: crawl navigation and body links between the site's pages to find valuable pages with too few inbound internal links, orphan pages, and the handful of cross-links that would lift striking-distance rankings.

Check whether the site has an llms.txt, whether it follows the spec and points at the pages that matter, and if it is missing draft one from the pages Search Console shows people actually find, so AI assistants get a curated map of the site instead of guessing.

Follow every redirect on the URLs Search Console still sends traffic to, on the sitemap, and on backlinked pages, to find chains, loops, redirects to the homepage, and temporary redirects that should be permanent, so link equity and crawl budget stop leaking.

Grade the domains linking to a site by authority, traffic and link count, spot concentration risk and link-farm patterns, and separate the referring domains that carry weight from the ones that are noise.

Compare the title and meta description of each top page against the queries it actually ranks for in Search Console, and find missing, duplicate, truncated or misaligned metadata on the pages where a rewrite would earn the most clicks.

See who links to the site and whether the backlink profile is healthy: referring domains, authority distribution, anchor text mix, dofollow share and concentration risk from Ahrefs, optionally compared against a competitor.

Check whether ChatGPT, Claude, Perplexity, Gemini and other AI assistants can crawl and read the site or docs, and whether anything needs to be done so they pick it up: parse robots.txt and meta robots for GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended and Bingbot, so allowing or blocking them is a deliberate decision rather than an accident.

Check that the site's XML sitemaps are submitted, fresh, error-free and complete: compare submitted vs indexed counts in Search Console, find sitemap URLs that redirect or return errors, and find pages with search traffic that the sitemap does not list.

Find which pages get search traffic but few signups, orders or purchases: join Search Console pages to GA4 landing-page engagement and conversions to see which organic pages, product pages and category pages actually convert, which attract visits that bounce, and where the gap between ranking and outcome is widest.

Compare what competitors rank for against your own coverage to find high-value keywords you're missing entirely.

Identify pages whose search traffic is declining over time so refreshes go to the content that's actually slipping.

Find out why a specific page is missing from Google, or used to show up in search and has now disappeared: inspect its index status and coverage state, the canonical Google chose, robots.txt and noindex blocks, fetch errors and sitemap membership, then check the live page, and end with the one fix that applies.

Find the new backlinks a competitor has earned recently, where they came from and how they got them, then turn the pattern into a prospect list: which sites are linking to competitors in your space right now, which competitor pages attract links, and which of those sites already link to a rival but not to you.

Find the new keywords the site started ranking for this month and the search queries it stopped appearing for, by comparing two periods, so you can see which new content is winning and where visibility quietly disappeared.

Find the queries and pages that already rank on Google's first page but get fewer clicks than their position should earn, so the fastest wins come from rewriting titles and descriptions instead of building new content.

List the search queries that recently dropped out of Google's top ten results between two periods, with the pages that lost them, so ranking losses are caught while they are still cheap to recover.

Find the keywords the site almost ranks on page one for: queries sitting at positions 8 to 20 with real impressions, where a small push produces the biggest traffic gain for the least work.

Compare the pages the site already ranks for organically against the landing pages receiving paid search clicks in GA4, to find where ads are buying visits organic would deliver anyway, and where paid traffic reveals demand organic has not captured.

Find out why organic clicks or traffic fell, exactly when it started, and whether the cause is a ranking loss, a demand change, a technical problem, a Google update or a tracking gap, using Search Console and GA4 evidence instead of guesswork.

Find out whether two or more of your own pages or blog posts cover the same ground and compete for the same keyword, cannibalizing each other and splitting clicks, and whether to merge them, differentiate them or leave them alone.

Measure how many sessions ChatGPT, Perplexity, Claude, Gemini, Copilot and other AI assistants send to the site in GA4, which pages they land on, whether those visitors convert, and how the trend compares with Google organic.

Find the backlinks a site recently lost, work out why each one disappeared (the linked page was removed, the link was edited out, a redirect broke), and build a prioritised list of links worth recovering with a fix on your side or a short outreach.

Estimate how well the pages that matter are indexed when the Coverage report is not available through the API: combine Search Console page data with the sitemap and URL inspection of a prioritised sample to find pages Google has dropped, pages with the wrong canonical, and sitemap URLs that never earn an impression.

Check the pages that matter for status codes, canonicals, noindex tags, titles, headings and sitemap coverage, prioritised by the Search Console traffic each page earns, so technical fixes go where they protect the most visits.

Separate branded searches from non-brand organic traffic to see whether SEO is actually growing, how dependent the site is on people already searching for the brand, and which trend the headline Search Console numbers are hiding.

See which of a competitor's pages earn the most organic traffic, what each one ranks for, which formats and topics keep winning for them, and which of those the site has no answer to yet.

Find which keywords and pages bring in paying customers: follow organic search all the way to purchases in GA4 or contacts and closed deals in HubSpot, so SEO is measured in revenue and pipeline instead of clicks.

Find content gaps and plan what to write next from Search Console data: group the queries the site already gets impressions for into topic clusters, map each cluster to the page that serves it, and surface clusters with real demand but no dedicated page.

Compare traffic sources and marketing channels on volume, engagement and conversion rate to decide where to spend more and where to cut: which channel gives the best return, whether to shift budget between paid, organic, referral and email, and how each is trending.

Measure marketing ROI by source: trace channels and campaigns through to closed revenue by joining CRM deals with payment records, so revenue per lead and return on spend are known instead of stopping at lead counts.

Two tools show different numbers for the same metric and nobody knows which to trust. Reconciles the disputed figure against a system of record such as Stripe or the application database, then finds the instrumentation defect behind it: duplicate and near-duplicate event names, events that silently stopped firing, missing properties and system-fired events counted as user actions.

Measure whether a feature we just shipped actually landed: adoption against an eligible denominator of users who could even reach it, breadth, depth and sustained use after the release, a pre and post impact estimate with or without a control group, and a keep, iterate or sunset call.

Discover the aha moment: which early in-product action, taken in the first session or first week, most separates users who stay from users who vanish, how to score candidate first actions by retention lift, and what share of new signups ever reach it. Covers activation metric definition, activation rate and time to value.

Do users keep coming back, or do they drift away after the first week or month? Builds the cohort retention curve for your key action at its natural frequency and judges whether it flattens to a plateau or decays to zero, covering N-day versus unbounded retention, repeat usage, leaky bucket diagnosis and how the plateau compares to published benchmarks.

Is this A/B test result real, or statistically significant only by accident? Runs the validity gates before reporting anything (sample ratio mismatch, peeking, power and minimum detectable effect, novelty), then gives absolute and relative lift with a confidence interval, a ship or kill call, and the winning variant extended through to revenue instead of stopping at the proxy metric.

Find the step where the most users abandon signup or onboarding, and how that varies by segment.

Track chargeback and dispute activity and refund rate against the Visa VAMP and Mastercard ECM thresholds that get a payment account fined or shut off, and find which products, plans or cohorts generate them. Covers dispute rate versus dispute activity, per-network denominators and dispute-fighting economics.

Separate customers who chose to leave from cards that simply declined: failed payments, declined renewals and involuntary churn, and where in the dunning and retry sequence the recovery is being lost. Covers involuntary churn share, first-attempt failure rate, payment recovery rate, decline codes and which accounts are sitting in dunning right now.

We raised prices, so what actually happened? Measures the effect of a price increase or new pricing page on cancellations, plan mix, conversion and revenue per account, using the never-migrated legacy cohort as a control, and sizes the revenue still sitting on old grandfathered prices and what those accounts would be worth at current list price.

Track MRR movement, churn, subscriptions that failed to renew, failed payments and refunds to separate genuine growth from collection problems.

Measure trial to paid and free to paid conversion properly by cohorting on trial start date, then split genuine non-conversion from trials that wanted to pay but whose first charge failed. Covers trial conversion rate, time to convert, trial length comparison and trial abuse screening.

Why are we losing deals, and is the reason in the CRM even true? Separates competitive losses from no-decision and disqualified losses, weights each loss reason by amount and stage reached, and measures how much of the closed lost reason field is actually populated before drawing a single conclusion from it.

Check whether open pipeline is enough to hit the quota or revenue target this quarter: pipeline coverage ratio, weighted pipeline, quota gap, and how many deals still need creating, derived from your own win rate rather than a 3x rule of thumb.

Build a ranked list of self-serve accounts sales should contact this week: join product usage to CRM and billing, exclude accounts already paying or already in an open deal, and attach the named trigger event and the evidence for each row.

Find the open deals that are actually dead or sliding: deal rot, stalled deals, close dates already in the past, time in stage running long, and silent deals propping up the Commit forecast, ranked by the amount at risk.

Measure win rate with a denominator you can defend, narrow and broad, plus close rate, sales cycle length and sales velocity, broken out by segment, deal size, source and owner, so a win-rate change can be attributed to the factor that actually moved.