seo-aeo-audit

name	seo-aeo-audit
version	2.0.0
description	Audit any website (any framework) for SEO and AEO (Answer-Engine Optimization) quality, producing a prioritized improvement report with framework-specific code fixes. Holistic whole-site audit, not just blog posts. Use when the user says "audit X.com", "SEO audit on...", "AEO audit", "check SEO/AEO gaps on...", or asks for an improvement/architectural report on a live website. Works on Next.js, Astro, Nuxt, SvelteKit, Gatsby, Remix, WordPress, Shopify, or generic HTML.

SEO + AEO Audit Skill

You are running a holistic SEO & AEO audit of a live website. This skill combines a deterministic Python analyzer with your own judgment to produce a prioritized, strategic improvement report with framework-specific code fixes.

When to invoke

Trigger when the user asks to:

• "audit [domain]" / "run an SEO audit" / "AEO audit" / "check SEO gaps"
• "analyze [URL] for search optimization"
• "generate an improvement report for [site]"
• produce "architectural improvements" or "gaps" for search/AI visibility

Process (follow in order)

Step 1 — Verify environment

CRITICAL: This skill installs its Python deps into a venv at ~/.claude/skills/seo-aeo-audit/venv/. Always use the venv's Python — NEVER plain python3 or python. Plain python3 is the system Python and won't have the skill's deps, so it will always report missing modules and send you down a false install path.

The single Python binary to use everywhere in this skill is:

~/.claude/skills/seo-aeo-audit/venv/bin/python

First, check if the venv is installed:

ls ~/.claude/skills/seo-aeo-audit/venv/bin/python 2>/dev/null && echo "venv present" || echo "venv missing"

If the venv is present, verify deps (using the venv's Python):

~/.claude/skills/seo-aeo-audit/venv/bin/python -c "import playwright, httpx, bs4, tldextract, click, docx" 2>&1

(extruct is optional. docx means python-docx — required for the .docx output step.)

Only if the venv is missing or the import check fails, run the install ladder:

# 1. If pip is missing (Ubuntu/WSL often lacks it):
sudo apt-get install -y python3-pip python3-venv

# 2. Create the skill's venv:
python3 -m venv ~/.claude/skills/seo-aeo-audit/venv

# 3. Install Python deps (use the venv's pip, NOT system pip):
~/.claude/skills/seo-aeo-audit/venv/bin/pip install -r ~/.claude/skills/seo-aeo-audit/requirements.txt

# 4. Install at least one Playwright browser (uses the venv's python):
~/.claude/skills/seo-aeo-audit/venv/bin/python -m playwright install chromium
# or firefox: ~/.claude/skills/seo-aeo-audit/venv/bin/python -m playwright install firefox
# or webkit:  ~/.claude/skills/seo-aeo-audit/venv/bin/python -m playwright install webkit

Offer to run the install for the user if needed. Do not proceed with the analyzer until the venv's Python can import the modules.

Step 2 — Run the analyzer

Always invoke via the venv's Python:

~/.claude/skills/seo-aeo-audit/venv/bin/python ~/.claude/skills/seo-aeo-audit/audit.py <URL> --out ./audits/

Common flags:

• --max-pages N — cap sampled templates (default 15)
• --no-render — skip Playwright fallback (faster, misses SPAs)
• --focus {seo,aeo,strategic,all} — narrow the analyzer output
• --browser {auto,chromium,firefox,webkit} — Playwright browser. auto probes in order chromium → firefox → webkit and uses the first available. Default: auto.
• --compare URL — produce a delta vs. reference site (use with caution, doubles runtime)

Expect 30-90 seconds. The analyzer writes to ./audits/<domain>/<YYYY-MM-DD>/:

• findings.json — structured findings (this is your source of truth)
• screenshots/ — desktop + mobile PNGs per sampled URL
• pages.json — raw parsed data per page (useful for citations)

Step 3 — Read findings.json and screenshots

# Structure:
# findings.json
#   - run: { url, date, pages_sampled, framework_detected, scores }
#   - findings: [ { id, severity, category, template, affected_urls, evidence, why, recommendation, framework_fix, impact_score } ]
#   - templates: { template_name: [sample_urls] }
#   - schema_matrix: { template: { BlogPosting: bool, FAQPage: bool, ... } }

Then look at 3-5 key screenshots (home, primary product/pricing, blog post, key conversion page). The strategic/design 20% layer of the report comes from YOUR read of these screenshots — the analyzer cannot judge visual hierarchy or positioning.

Step 4 — Synthesize the technical report

Write the report to ./audits/<domain>/<YYYY-MM-DD>/report.md using templates/report-template.md as scaffold. The report MUST contain these sections in this order:

1. Executive Summary — scores (SEO, AEO, Strategic 0-100), 3 biggest wins, 3 biggest gaps, recommended focus
2. Critical Issues — blockers that lose traffic/ranking today. Filter severity: critical from findings.
3. High-Impact Improvements — 30-day list, ordered by impact_score
4. Per-Template Findings — one section per sampled template
5. Schema Coverage Matrix — render the matrix as a markdown table (✓/✗)
6. AEO Readiness — answer-engine posture: schema depth, AI crawler access, entity linking (Wikidata etc.), direct-answer structure, Speakable schema
7. Strategic & Design Layer (the 20%) — YOUR judgment from screenshots. Cover: above-the-fold 3-second test, positioning sharpness, primary CTA visibility, social proof, IA clarity, conversion path, visual hierarchy, brand voice distinctiveness, topical authority gaps, mobile impression
8. 30/60/90 Roadmap — prioritized action plan
9. Framework-Specific Code — for each critical/high finding, show the fix in the detected framework. Load frameworks/<detected>.md for patterns.
10. Methodology — one paragraph + link to raw findings.json

Step 5 — Apply framework-specific fixes

The analyzer populates run.framework_detected (one of: astro, nextjs, nuxt, sveltekit, gatsby, remix, wordpress, shopify, unknown).

For the Framework-Specific Code section:

1. Read frameworks/<detected>.md — contains canonical code snippets per issue class
2. For each critical + high finding, cross-reference its id against the framework file
3. If the framework is astro, reference references/astro-gold-standard-patterns.md for the full-depth pattern library (Organization schema depth, Speakable schema, combined @graph utility, AI bot detection, etc.)
4. Fall back to frameworks/generic-html.md when framework is unknown

Step 6 — Write the technical report

Keep the report actionable. For every finding, include:

• What — one-line statement of the issue
• Why it matters — SEO/AEO impact in plain language
• Fix — HTML-level description
• Code — framework-specific snippet
• Evidence — sampled URL(s) + what was observed

Step 7 — Write the layman summary (for non-technical stakeholders)

Every audit also produces a plain-English summary aimed at founders, marketing leads, and non-technical stakeholders who don't read code. Write to ./audits/<domain>/<YYYY-MM-DD>/summary.md using templates/summary-template.md as scaffold.

Rules for the summary:

• No jargon without definition. Any technical term gets expanded in the glossary.
• Business impact, not technical detail. Frame every gap as lost traffic, fewer AI citations, lower conversion, worse first impression.
• Traffic-light health section. Green (what's working), yellow (could be better), red (broken / costing visibility).
• Top 5 things to do first — each with title, what, why, effort (day/week/month), who to assign.
• Ideas & guidance section — this is the "so what" for a strategic reader. Go beyond just fixing what's broken:
  • Content areas/topical authority the site should own
  • Positioning / hero-headline tightening (suggest actual rewrites when you can)
  • Trust signals to add (logos, case studies, counts, testimonials)
  • Pages to create (comparisons, tools/calculators, guides) that capture commercial-intent traffic
  • Distribution plays (newsletter, Wikipedia/Wikidata presence, community, podcast outreach, brand mentions on the platforms AI engines cite most)
• Glossary — define every acronym and technical term used.
• Tone: confident, specific, kind. Not condescending. Assume the reader is smart but not a web developer.

Keep summary.md to roughly 800-1500 words.

Step 8 — Render .docx versions of both

Run the bundled converter to produce Word-compatible docx files (again, always via the venv's Python):

~/.claude/skills/seo-aeo-audit/venv/bin/python \
  ~/.claude/skills/seo-aeo-audit/render_docx.py \
  ./audits/<domain>/<date>/report.md \
  ./audits/<domain>/<date>/report.docx

~/.claude/skills/seo-aeo-audit/venv/bin/python \
  ~/.claude/skills/seo-aeo-audit/render_docx.py \
  ./audits/<domain>/<date>/summary.md \
  ./audits/<domain>/<date>/summary.docx

This gives stakeholders shareable .docx files alongside the markdown source.

Step 9 — Final message to the user

End with two lines to the user:

1. Report: ./audits/<domain>/<date>/report.md (+ .docx) — for the web/content team
2. Summary: ./audits/<domain>/<date>/summary.md (+ .docx) — for stakeholders Plus scores and the single top gap: Scores: SEO X/100, AEO Y/100, Strategic Z/100. Top gap: <gap>.

The Strategic 20% — how to read screenshots

Don't just list what you see. For each screenshot, answer:

• Above the fold: In 3 seconds, what does this site do? Who's it for?
• Hero: Is the headline sharp and specific, or template SaaS? ("The ad spend OS for scaling lead gen" is sharp; "The best platform for your business" is not)
• Visual hierarchy: Does the design lead the eye to the primary CTA? Is there ONE primary CTA or competing ones?
• Social proof: Are trust signals (logos, counts, testimonials) visible in the first viewport?
• IA signals: Nav clarity — jobs-to-be-done or internal org chart? How many top-level items?
• Copy voice: Does it sound distinctive (brand-owned) or generic (AI-written template)?
• Mobile: How does the mobile screenshot change the experience? Is content re-ordered well?

AEO is the differentiator — don't skimp on it

Most audits are SEO-only. AEO (how AI answer engines like ChatGPT, Perplexity, Google AI Overview, Claude represent the site) is where sites win or lose in 2026. Heavily emphasize:

• Schema depth — Organization schema with sameAs including Wikidata URL is a 10x entity-recognition signal
• Speakable + QAPage schemas — live AEO surfaces. Note: FAQPage no longer produces rich results (retired for all sites) but is still parsed for AI answer extraction — keep it where it exists, don't chase it for SERP gain. Never recommend HowTo; Google removed its rich results in September 2023.
• AI crawler policy — robots.txt must allow GPTBot, ClaudeBot, PerplexityBot, CCBot, Google-Extended, Applebot-Extended (flag any disallows as gaps unless the user has a reason)
• llms.txt — report presence only. No major AI search system consumes it and it carries no citation weight (see references/aeo-evidence.md). Never rank it above SSR, schema depth, or crawler access in a roadmap.
• Server-side rendering — AI crawlers do not execute JavaScript. A client-rendered page is invisible to every answer engine no matter how good its schema is. This is the highest-priority AEO check there is.
• Direct-answer structure — first ≤50 words of each page should answer the implied question
• Entity density — how many Wikipedia/Wikidata-linkable entities appear as sameAs?

Reference: references/playbook-distilled.md for the full set of AEO principles applied during synthesis.

Tone & calibration

• Be direct. This is a technical audit, not marketing copy.
• Quantify findings when possible ("73% of sampled blog posts missing canonical").
• Never say "comprehensive", "holistic", "leverage", "world-class" in the report — they're hollow filler.
• Lead with the biggest gap. If the home page has no Organization schema, that's worth more than 50 meta description tweaks.
• Be specific about framework-specific fixes — vague "add schema markup" is a failure mode.

Output

Final deliverable the user sees:

./audits/<domain>/<YYYY-MM-DD>/
├── report.md          ← Technical audit (for web/content team)
├── report.docx        ← Same, as Word doc
├── summary.md         ← Layman summary (for stakeholders)
├── summary.docx       ← Same, as Word doc
├── findings.json      ← Raw analyzer output (structured, for tooling)
├── pages.json         ← Raw per-page parsed data
└── screenshots/
    ├── home-desktop.png
    ├── home-mobile.png
    ├── blog-desktop.png
    └── ...