Audit a website, investigate its real search opportunities, and deliver a short data-backed report on the few changes most likely to grow organic traffic that converts.
Instructions your experts follow when using this skill.
Packaging adaptation by AutoGPT, 2026-09-25. Original authorship and licence are retained. Changes are limited to the recorded name, metadata and file references; see ATTRIBUTION.md in this skill package.
Find the work that would most improve a site's useful organic traffic, then explain it so a non-expert can act on it. Research broadly; recommend selectively. The report leads with one to three recommendations that either capture meaningfully more qualified search demand or stop a real loss.
Use this when asked for an SEO audit or review of a domain, especially for a shareable report. For expert-facing analysis of a competitor or market, use competitor-analysis or competitive-landscape instead.
projectId (list_projects; if no project matches, create_project).get_project_context first. This skill needs business_overview. If it is empty, infer what the business does from the site, confirm it with the user in one question, write it back with update_project_context, and continue. Suggest seo-project-setup at the end for the rest; never front-load the full interview.update_project_context (a corrected business_overview, the pages the report names via addKeyPages) and append { appendResearchLog: { summary: "Site audit: <domain>. Verdict: <conclusion>" } }.Deliver using the bundled seo-report instructions in , saving with skill: "seo-audit". If those instructions are unavailable, say so and stop before writing HTML.
whoami: confirm the connection and credits before spending. If OpenSEO is not connected, stop and ask the user to connect it.run_site_audit, then get_audit_status (wait a minute or two between checks), get_audit_issues, get_audit_pages. Leave Lighthouse off unless the user asked for performance depth. Crawl reads are free.get_backlinks_overview and get_domain_overview: orientation only. Provider traffic and keyword counts are estimates with no single observation date; they are not measured visits.get_ranked_keywords: which queries send which pages traffic. Start with one domain-level call with resultTypes: ["organic"]; use scope: "exact_url" for the specific pages you compare. A page missing from a limited domain sample is not proof it has no rankings. Ranking rows carry their own last_updated_time; keyword metric dates are not ranking dates.get_serp_results: the live check behind every ranking claim in the report. The returned rank counts every result block, so count organic (unpaid) listings yourself and report the spot with its page, ten spots per page: "#10 (page 1)", "#11 (page 2)". Request depth 20; a page not seen is "not in the first 20 results". Record the exact query, country, language, date, how many organic listings came back, and the matching URL; those details go in the evidence appendix, not the tables. A failed lookup is unknown, not "not in the first 20 results".get_search_console_performance: when connected, first-party clicks and impressions separate low visibility from low click-through. Missing access is a coverage gap, not a blocker.get_keyword_metrics and research_keywords: demand for the queries a candidate page targets. One focused metrics batch usually suffices; one research call with 1–3 seeds when a demand gap could change the decision.Research until another lookup is unlikely to change which opportunities lead. Respect an explicit user budget and say which comparison it prevented.
whoami, resolve the project, start run_site_audit. While it crawls: backlinks overview, domain overview, the domain-level ranked-keyword sample, and the sitemap plus navigation. Write down the site's page families from the sitemap, not just the crawl sample: product, pricing, comparison or alternative, tools and templates, guides, categories, services, locations, whatever the site actually has.
If the crawl is broken or nearly empty (certificate error, 5xx, one page), investigate before anything else. Check redirects and certificate variants yourself and search for the business; a dead domain with a live successor flips the whole recommendation to "redirect the old domain".
For each family that could bring buyers, read at least two pages' main content (ignore navigation and shared templates): the page performing best in the ranking data and one performing worst or typical. For each page ask: what decision or question does its searcher have, and does the page answer it with specific, accurate, sourced information, or does it substitute a name, location, or keyword into a shared answer? Compare against what the leading results for that query provide.
A common SaaS pattern worth checking directly: competitor comparison or alternative pages and competitor pricing pages are two separate families, each answering a different buying question. Read siblings side by side. Investigate uneven visibility between siblings (intent, content specificity, links, authority); a sibling that already ranks near the top is something to protect rather than rewrite.
Check the basics for any page you might name: status, canonical (the URL the page declares as its preferred version), index directives, and how visitors reach it internally. Broaden when a family is missing from the crawl, when siblings perform very differently, when a tool or template page turns out to rank, or when a live query returns a different page than expected.
Run the live checks now, not after drafting: the query cluster each candidate page serves (the head term plus the variants buyers actually use), including both sides of any stronger-versus-weaker comparison. Re-run the queries that decide the leading recommendation before writing. If two checks disagree, write the later one and the earlier in brackets, for example "#10, page 1 (first check: not in the first 20 results)"; that spread is same-day variation, not a trend. One snapshot is not a baseline.
Write opportunities.md in your working folder (working notes, not the deliverable): one row per serious candidate, usually five to ten, drawn from at least three different kinds of opportunity:
Columns: pages | problem observed | evidence (query cluster with US monthly volumes, spot and page or "not in the first 20 results", date) | proposed change | who searches and why they matter to this business | plausible benefit | effort | main uncertainty.
If a row's ranking would change with one more lookup (a missing volume, an unchecked sibling, a query you never ran live), do that lookup before ranking.
Prefer a bounded change that directly fixes a demonstrated problem for searchers likely to become customers, with a credible path to a meaningful gain. A larger raw-volume opportunity with a weaker diagnosis does not automatically outrank it. A genuine access or indexing blocker, a measurable traffic loss, or a dead domain jumps the queue.
None of these decides on its own: the volume of one sampled query; how easy the fix is; a crawler warning; a hypothetical position-one traffic figure; a navigation or redirect repair with no demonstrated traffic loss. Those belong in the checked table, not the top three. Do not recommend rewriting a page that already ranks near the top for its target query.
Every shortlist row ends in one of two places: a recommendation, or a row in the report's "What else we checked" table with a real reason. "Later, if sales asks for it" is not a reason; "demand is a quarter of the leading candidate's and the page already ranks seventh" is. For the runner-up, write one sentence on why the leader beats it; that sentence goes in the report.
Draft the report body (markdown or HTML, not yet saved). Give the reviewer (a second agent or model if your environment can run one, otherwise a fresh self-review) that draft and the shortlist. The reviewer must: argue the case for the strongest rejected row and say whether the draft answers it; confirm the leading recommendation's evidence is in the draft; confirm every material diagnosis from step 2 survived as a recommendation or a table row; check dates, geography, and rank conventions; and flag paragraph-length bullets and jargon. Fix what it finds, verify any new factual claim against the evidence, then write and save through seo-report.
Use the title conventions in seo-report. Sections, in order:
h3 naming the action and the page or small group, then:
seo-report (URL https://openseo.so/docs/skills/seo-audit, text "OpenSEO SEO Audit skill"), a two-line coverage and limits note, then a <details><summary>Evidence and methodology</summary> block, closed by default, holding the crawl sample, page families read, the full live-check table (query, volume, position, organic listings returned, time), calculations, and sources. Keep it self-contained; local file paths are not evidence.Writing rules: short bullets, one idea each, usually 8–20 words. No Problem / Change / Expected effect paragraphs and no repeated summaries. There is no word target; if the main body outgrows about two screens, move supporting detail into the disclosure instead of deleting it. If the research establishes no worthwhile action, say what is working and what the audit could not establish rather than filling the format.
Skeleton for one recommendation and the checked table (keep the seo-report CSS unchanged; every h2 needs an id and a contents-rail entry):
<h2 id="recommendations">Recommendations</h2>
<h3>Make the Northwind comparison answer a switching decision</h3>
<p><strong>Do this</strong></p>
<ul>
<li>Replace the shared table on <a href="URL" target="_blank" rel="noopener">/northwind-alternative</a> with Northwind-specific tradeoffs.</li>
<li>Add a sourced migration section: policies, evidence, audit continuity.</li>
</ul>
<p><strong>Why</strong></p>
<ul>
<li>Same comparison text as two siblings; only the vendor name changes.</li>
<li>Searchers are already evaluating a switch, the closest fit to a demo.</li>
<li>Position-one scenario: about 40–60 total US visits a month. Hypothetical, not a forecast.</li>
</ul>
<div class="tw"><table>
<thead><tr><th>Query</th><th class="n">Est. US searches/mo</th><th>Acme position, US, Sep 18, 2026</th></tr></thead>
<tbody><tr><td>northwind alternative</td><td class="n">50</td><td>#9 (page 1)</td></tr></tbody>
</table></div>
<h2 id="what-else-we-checked">What else we checked</h2>
<div class="tw"><table>
<thead><tr><th>Opportunity</th><th>What we found</th><th>Decision</th></tr></thead>
<tbody><tr><td>Software buying guide</td><td>390 est. US searches/mo; not in the first 20 results; page explains criteria, compares no vendors</td><td>Runner-up. Larger demand, but a weaker diagnosis and a full rewrite; test the comparison page first.</td></tr></tbody>
</table></div>
seo-report is the exception). Severity words only where literally true.Files installed alongside these instructions.
When the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' 'who is my target audience,' 'describe my product,' 'ICP,' 'ideal customer profile,' or wants to avoid repeating foundational information across marketing tasks. Use this at the start of any new project before using other marketing skills — it creates `.agents/product-marketing.md` that all other skills reference for product, audience, and positioning context.
When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.
When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.