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Agent skill

SEO Audit

by every-app in every-app/open-seo

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.

MITAuto-check passedMarketing & SEO

Install SEO Audit

skills CLI
$ npx skills add every-app/open-seo --skill seo-audit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install every-app/open-seo seo-audit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/every-app/open-seo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/seo-audit .claude/skills/seo-audit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
seo-audit
GitHub stars
23k
Token cost
~3.8k tokens
SKILL.md length
2,073 words
Files
1
Skills in repo
19
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Works in 6 steps: Orient → Investigate every family that matters to… → Shortlist before you decide → …
  • Tasks that involve SEO audit
  • SKILL.md covers Goal, Inputs and project context, OpenSEO MCP tools and Workflow, plus 2 more sections
  • Reaches openseo.so

What it does

SEO Audit is an agent skill from every-app/open-seo. 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.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering SEO audit. The repository describes itself as: Open source alternative to Semrush and Ahrefs. The licence is MIT.

When your agent uses it

  • Tasks that involve SEO audit

Example prompts

  • “/seo-audit”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Orient
  2. Investigate every family that matters to the goal
  3. Shortlist before you decide
  4. Choose
  5. Size the benefit honestly
  6. Review, then write

What it can do on your machine

Read from SKILL.md and the folder at commit deb4491. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are html).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • openseo.so

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

SEO Audit loads about 3.8k tokens when it runs. Until then it costs about 45 tokens; SKILL.md has 2,073 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~45
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from every-app/open-seo at commit deb4491, republished under its MIT licence (© every-app). 2,073 words, ~3,786 tokens.

Download SKILL.mdSave it as .claude/skills/seo-audit/SKILL.md (or your agent's skills folder).
name
seo-audit
description
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.

OpenSEO SEO Audit

Goal

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.

Inputs and project context

  • Domain to audit and projectId (list_projects; if no project matches, create_project).
  • Call 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.
  • Reuse research-log results under 30 days old for discovery. A ranking claim that drives a recommendation still needs a live check made during this audit.
  • On finish, write back what is durable with update_project_context (a corrected business_overview, the pages the report names via addKeyPages) and append { appendResearchLog: { summary: "Site audit: <domain>. Verdict: <conclusion>" } }.

Deliver through the seo-report skill, saving with skill: "seo-audit". If that skill is unavailable, say so and stop before writing HTML.

OpenSEO MCP tools

  • 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.
  • Web reading (fetch, scrape, or search): the site's own pages, sitemap, the leading results for a query, and competitor pages.

Research until another lookup is unlikely to change which opportunities lead. Respect an explicit user budget and say which comparison it prevented.

Workflow

1. Orient

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".

2. Investigate every family that matters to the goal

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.

3. Shortlist before you decide

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:

  • an existing page underperforming the demand it targets
  • real demand with no page that answers it, including feature, framework, or use-case queries taken from the product's own claims
  • a winning page to protect or correct
  • an access, indexing, or redirect defect that is costing visits
  • helping existing visitors take the next step

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.

4. Choose

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.

Show full SKILL.md (816 more words)Show less
5. Size the benefit honestly
  • Name the mechanism: a new ranking, a higher position on an existing ranking, or more clicks at the current position. A page that already ranks already receives part of the volume, so a scenario on total volume overstates the gain.
  • Size against the cluster the change serves, not one exact term; note overlap instead of adding variants as if they were different people.
  • Demand figures are US unless stated; never multiply into an invented global number.
  • Search volume is not visits. Use a stated click-share assumption and show it in a scenario table; a position-one scenario is allowed when labeled hypothetical, not promised.
  • If the current traffic baseline is unknown, call the figure total potential visits, not additional visits. Do not add overlapping queries.
  • Business relevance can be inferred from intent and product fit; say so and label it. Never invent a conversion rate or revenue.
  • When there is no number, give a directional assessment and its reason ("already third for its main query, so headroom is small").
6. Review, then write

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.

Output format

Use the title conventions in seo-report. Sections, in order:

  1. Your next SEO move: two or three bullets. First action, next action if any, and what is already working. These bullets replace the starter template's opening paragraph and its closing "What to do next" section; include neither.
  2. Recommendations: one to three, in priority order. Each is an h3 naming the action and the page or small group, then:
    • Do this: two to four bullets. Start with a verb, name what changes, link the page.
    • Why: two to four bullets. The observed gap, who searches and why they matter, the plausible benefit, the main uncertainty. Benefit and confidence stay together.
    • A small evidence table (demand and current visibility, or stronger-versus-weaker sibling, or observed content versus proposed). Make the table explain itself: put geography and date in the column header, write positions as "#10 (page 1)" or "not in the first 20 results" (never "10/17", arrows, or listing counts), and say "estimated" in the volume header. Add a "How to read this" bullet only for a limit the headers cannot carry. Optionally a two-row scenario table labeled hypothetical.
  3. What else we checked: one table: Opportunity | What we found | Decision. One row per shortlist row that did not become a recommendation, starting with the runner-up and its sentence from step 4, plus one row grouping maintenance. Keep cells to a line.
  4. How this report was made: the fixed skill link line from 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):

html
<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>

Guardrails

  • Calm, plain tone. No exclamation points, drama words, or filler; no em dashes in prose (the report title convention in seo-report is the exception). Severity words only where literally true.
  • Gloss each term of art in plain English on first use: canonical, meta description, crawler, 301, structured data.
  • Observations are not causes. Similar content plus uneven rankings, a crawler warning, or missing provider rows never prove a penalty, an indexing exclusion, or the reason a page ranks where it does.
  • Retrieval date is not observation date. Say when a ranking was observed, or say unknown.
  • Missing backlink or ranking data means "no recorded data", not a problem.
  • Treat difficulty and volume as inputs, not goals. A small query can matter to a high-value business; an easy one is not automatically worthwhile.
  • Separate what the tools reported from what you verified yourself, and say both in the closing section.

© every-app, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/seo-audit of every-app/open-seo.

Open the folder on GitHubat commit deb4491

Compare with similar skills

SEO Audit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

SEO Audit compared with similar skills
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SEO Audit this skillevery-app/open-seo23k—~3.8kAutomated safety check: PassMIT
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Google SEO APIsAgriciDaniel/claude-seo18k1 repos~4.2kAutomated safety check: PassMIT
FLOW SEO FrameworkAgriciDaniel/claude-seo18k2 repos~1.4kAutomated safety check: PassMIT
Broken Link Checkernowork-studio/notfair-plugin3.9k1 repos~507Automated safety check: PassMIT
GEO-First SEO Audit Toolzubair-trabzada/geo-seo-claude11k—~2.8kAutomated safety check: NotesMIT

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Categories

Questions about SEO Audit

What does SEO Audit do?

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. SEO Audit is an agent skill from every-app/open-seo. 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.

When should I use SEO Audit?

SEO Audit fits situations like: tasks that involve SEO audit.

How do I install SEO Audit in Claude Code?

Run `npx skills add every-app/open-seo --skill seo-audit -a claude-code`. Or copy the skill folder (.agents/skills/seo-audit in every-app/open-seo) into .claude/skills/seo-audit in your project. Claude Code loads it when a task matches its description.

How do I install SEO Audit in Codex?

Run `npx skills add every-app/open-seo --skill seo-audit -a codex`. Or copy the skill folder (.agents/skills/seo-audit in every-app/open-seo) into .agents/skills/seo-audit in your project. Codex loads it when a task matches its description.

Can I use SEO Audit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add every-app/open-seo --skill seo-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/seo-audit, .gemini/skills/seo-audit, .github/skills/seo-audit and .opencode/skills/seo-audit in your project.

What does SEO Audit need to run?

SKILL.md names no scripts, command-line tools or credentials: SEO Audit is instructions for the agent only.

Does SEO Audit access the network?

SKILL.md names 1 domain. In commands or code: openseo.so; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is SEO Audit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does SEO Audit use?

SEO Audit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does SEO Audit use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to SEO Audit?

Skills that share tags, products or a category with SEO Audit: Hreflang and International SEO (AgriciDaniel/claude-seo, 18k stars), Google SEO APIs (AgriciDaniel/claude-seo, 18k stars), FLOW SEO Framework (AgriciDaniel/claude-seo, 18k stars) and Broken Link Checker (nowork-studio/notfair-plugin, 3.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains SEO Audit?

every-app (a GitHub organization) maintains it in every-app/open-seo, which has 22,680 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 6, 2026.

Source: every-app/open-seo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.