geo-tracking
Measure AI visibility without paid tools or API keys. Input: your site (GA4 and server logs) and a buyer prompt panel. Output:…
Get a prioritized SEO + GEO action plan for any website. Input: a URL (and optional competitors). Output: ranked fixes across technical health, content, keywords, e-commerce, local, backlinks, and AI visibility (ChatGPT, Perplexity, Google AI Overviews), or a plain-language
Claude Code
$ npx -y skills add Thibaultbm/claude-seo-geo --skill seo-geo-audit --agent claude-code
Codex
$ npx skills add Thibaultbm/claude-seo-geo --skill seo-geo-audit -a codex -y
Cursor
$ npx skills add Thibaultbm/claude-seo-geo --skill seo-geo-audit -a cursor -y
Gemini CLI
$ npx skills add Thibaultbm/claude-seo-geo --skill seo-geo-audit -a gemini-cli -y
GitHub Copilot
$ npx skills add Thibaultbm/claude-seo-geo --skill seo-geo-audit -a github-copilot -y
OpenCode
$ npx skills add Thibaultbm/claude-seo-geo --skill seo-geo-audit -a opencode -y
Windsurf
$ npx skills add Thibaultbm/claude-seo-geo --skill seo-geo-audit -a windsurf -y
name: seo-geo-audit description: "Get a prioritized SEO + GEO action plan for any website. Input: a URL (and optional competitors). Output: ranked fixes across technical health, content, keywords, e-commerce, local, backlinks, and AI visibility (ChatGPT, Perplexity, Google AI Overviews), or a plain-language summary. Use to audit or review a site, find why it does not rank or is not cited by AI, compare against competitors, or set a baseline before a bigger SEO project." license: MIT metadata: author: "Sorank (https://sorank.com)" version: "1.0.0"
Audit a website the way a senior practitioner does: collect verifiable facts first, judge them against a field-tested checklist, benchmark against the competitors that actually rank, and deliver a short list of fixes ordered by impact. The methodology was distilled from 115+ real agency audit calls and updated with sourced 2025-2026 evidence on AI search.
If the working environment contains an Obsidian vault or any local knowledge base (a folder of .md notes, often with a .obsidian directory), read the relevant notes before acting: brand and product facts, target keywords, competitors, and the SEO action log of what was already tried. Ground every recommendation in that context instead of asking the user for facts the vault already holds. At the end of the session, append the actions taken to the vault's SEO action log so the next session starts informed.
1. Facts before judgment. Every claim in the final report must trace back to something measured (by the bundled script, a browser check, or data the owner provided). Never guess a title length or assume a sitemap exists. 2. Competitors over abstract thresholds. "Your page has 400 words" means nothing alone. "The three sites outranking you average 1200 words on this query" is a finding. When competitors are known, benchmark page versus page. 3. Two search worlds at once. Every category is checked twice: does it help Google rankings, and does it help the site get retrieved and cited by ChatGPT, Perplexity and Google AI Overviews. These overlap but do not coincide (AI Mode answers show only about 32 percent URL overlap with the top 10 organic results). 4. The audit is a pricing tool for agencies. The counted error volume doubles as a quote: "500 errors on this site, that is roughly 3000 euros of work" scopes the project the prospect is buying. The score is a ratio of errors to pages, so one template-level error repeats across every page using that template and a single fix corrects it everywhere, which is why a low score is rarely a catastrophe. Frame both up front so the owner reads the report as a plan, not a verdict (field heuristic from 115+ agency audits).
Competitors first, always: before collecting anything, ask the user for up to 5 known competitors and whether you should find them on your own instead (the exact question is Step 0 of seo-competitor-analysis; ask it once, even when the vault lists competitors, and show those as a proposal). The answer feeds the benchmark here and the sitemap gap of seo-competitor-analysis.
Then ask only for what is missing; proceed with what exists:
| Input | Required | Note | |---|---|---| | Homepage URL | yes | The script also discovers robots.txt, sitemap, llms.txt | | 1-2 key pages | recommended | Top service, product or article page | | Competitors (up to 5, or "find them") | asked every time | Page-versus-page benchmarking and the sitemap gap (seo-competitor-analysis) | | Goals and market | helpful | Local vs national, language, business model | | GSC / GA4 access | optional | Indexation ratio and traffic facts; otherwise ask the owner for screenshots |
Run the bundled collector on the homepage plus key pages, and on each competitor page cited later in the report:
python3 scripts/seo_audit.py https://example.com /services /blog/top-article
It returns, per page: a page kind (homepage, article, blog hub, product, page), an SEO score and a GEO score out of 100 with a ranked FINDINGS list (same thresholds and weights as the Sorank SEO Analyzer extension, codes explained in checklist section 15), and the facts behind them: HTTP status, HTTPS, platform fingerprint, title (with generic-title flag), meta description length, full heading hierarchy with level jumps, image alt coverage and weight sample, internal versus external link counts, Open Graph, canonical, JSON-LD types, visible word count, meta robots, an em dash and en dash count with samples (checklist 6.4: an AI-writing tell to find-and-replace with commas), a `likely_js_rendered` flag, bold count and samples, top terms with density and two-word phrases, the keyword placement matrix (title and H1 terms across title, H1, H2-H6, meta, URL, intro, bold, alt), readability (sentence-length buckets, Flesch), citability counts (statistics, definitions, quotes, question headings, snippable paragraphs), link quality (nofollow, sponsored, ugc, empty and generic anchors), social profiles and placeholder links, head tags (charset, favicon, Twitter card, Open Graph keys, meta refresh, hreflang validation), URL hygiene, mixed content, text-to-HTML ratio, DOM size, image format, dimensions and LCP lazy-loading, JSON-LD parse errors and missing or empty required properties per type,
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