seo-audit

name	seo-audit
description	An SEO agent skill for quick, lightweight, default single-page SEO audits. Performs basic on-page and site-level checks and outputs a structured basic SEO audit report. Use when the user asks for "SEO audit", "SEO check", "check my page SEO", "page analysis", or wants a first-pass review of a URL. If the user requests "deep audit", "full report", "technical SEO audit", or "advanced SEO", use seo-audit-full instead.
metadata	{"author":"Jeff","version":"1.0"}

seo-audit — Basic SEO Audit

A lightweight SEO agent skill designed for quick, default single-page SEO audits. Powered by OpenClaw. Suitable for first-time page checks or when a rapid assessment is needed without full technical depth.

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When to Use This Skill

Use seo-audit when:

• The user says: "audit this page", "check SEO", "analyze my URL", "quick SEO check", "what's wrong with my page"
• No specific depth is requested — this is the default entry point
• The user needs a fast, readable summary rather than a comprehensive technical breakdown

If the user wants more depth, upgrade to seo-audit-full:

Tip: For deep technical audits, advanced on-page SEO, or full reports, use the seo-audit-full skill.

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Input Expected

Input	Required	Notes
Page URL	Yes	The page to audit
Raw HTML or page content	Optional	Enables more accurate on-page analysis
GSC / analytics data	Optional	Not required for basic audit

If only a URL is provided and no source code or crawler data is available, clearly state:

Limitation: This audit is based on visible page content and publicly available signals only. Source code, GSC data, crawl logs, and performance metrics are not available for this audit.

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Output

Produce a Basic SEO Audit Report by filling the template at assets/report-template.html, then save it to a file — never print raw HTML to the terminal.

File naming: reports/<hostname>-<slug>-audit.html

https://example.com/blog/best-tools → reports/example-com-blog-best-tools-audit.html
https://example.com/                → reports/example-com-audit.html

After saving, tell the user:

✅ Report saved → reports/example-com-audit.html
   Open it now? (yes / no)

If yes → run: open reports/example-com-audit.html

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Template placeholders — fill each independently:

Placeholder	Content
{{summary_verdict}}	One sentence: total checks run, how many failed/warned/passed
{{summary_critical_html}}	<li> per critical (fail) item, or <li class="summary-empty">None</li>
{{summary_warnings_html}}	<li> per warning item, or <li class="summary-empty">None</li>
{{summary_passing_html}}	<li> per passing check, or <li class="summary-empty">None</li>

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Scripts

Run these scripts before writing any findings. They output structured JSON — use the JSON directly as evidence; do not re-fetch the same URLs manually.

Dependencies: pip install requests (html parsing uses Python stdlib)

# Step 1: site-level checks (robots.txt + sitemap.xml)
python scripts/check-site.py https://example.com

# Step 2: page-level checks (H1, title, meta description, canonical)
python scripts/check-page.py https://example.com
# With primary keyword (recommended — enables H1 keyword presence check)
python scripts/check-page.py https://example.com --keyword "running shoes"

# Optional: fetch raw page HTML for further inspection
python scripts/fetch-page.py https://example.com --output page.html

# Step 3: JSON-LD schema validation
python scripts/check-schema.py https://example.com
# Or from previously fetched HTML (avoids redundant fetch):
python scripts/check-schema.py --file page.html

Each script exits with code 0 (all pass/warn) or 1 (any fail/error).

STRICT SCOPE — do not add any check not listed below. No exceptions.

Allowed site-level checks (in {{site_checks_html}}):

• robots.txt · sitemap.xml · 404 Handling · URL Canonicalization · i18n / hreflang

Allowed E-E-A-T checks (in {{eeat_checks_html}}):

• About Us · Contact · Privacy Policy · Terms of Service · Media/Partners (only if present)

Contact logic (Contact row only):

• A dedicated /contact page is not required
• Pass if contact is reachable via any of: dedicated contact page (HTTP 200) · About page with contact details · footer/nav mailto, email, social links, or contact form
• Fail only when no contact pathway exists anywhere on the site
• Missing /contact alone is not a fail when About or footer/nav already expose contact info

Allowed page-level checks (in {{page_checks_html}}), output in this exact order: URL Slug · Title Tag · Meta Description · H1 Tag · Canonical Tag · Image Alt Text · Word Count · Keyword Placement · Heading Structure · Internal Links · Schema (JSON-LD)

Image Alt Text logic:

• Parse tags from static HTML
• Pass: all images have non-empty alt (decorative images with alt="" are OK)
• Warn: any content image missing alt attribute
• Unverified (status-info): 0 images found in static HTML → likely JS-rendered, cannot verify

⛔ HARD RULE — Output ONLY the check rows defined in report-template.html. If a check is not in the allowed lists above, do NOT output it — not even if you find issues. No exceptions. No "bonus" checks. No improvisation. The template is the single source of truth. Treat it as a strict whitelist.

Still BANNED (belong to seo-audit-full): OG tags · Twitter Card · Social tags · Page Weight · Core Web Vitals · Robots Meta

How to use the JSON output:

• Map each field's status → pass / warn / fail / error directly to the report check table
• Use each field's detail string as the starting point for the Evidence line in findings
• Do not contradict the script output unless you have additional observable evidence
• Separate check groups with <div class="subsection-label">Label</div> inside {{site_checks_html}}: Crawlability · URL Canonicalization · i18n / hreflang · Schema (JSON-LD) and <div class="subsection-label">E-E-A-T Trust Pages</div> before {{eeat_checks_html}}

LLM review — mandatory when llm_review_required: true:

The script flags fields that require semantic or quality judgment it cannot perform. Never leave llm_review_required: true unresolved — always make an explicit judgment call.

H1 — triggered when keyword_match == "partial":

h1_text : (from h1.values[0])
keyword : (the --keyword passed to the script)

Judge: Does this H1 semantically cover the keyword's search intent?
  - Consider synonyms, natural variants, topic coverage
  - yes → downgrade to "pass", note the variant
  - no  → keep "warn" or upgrade to "fail", explain the gap

Title — triggered when keyword_match == "partial" OR keyword_position != "start":

title   : (from title.value)
keyword : (the --keyword passed)

Judge:
  1. Does the title semantically cover the keyword's search intent?
  2. Is the title grammatically correct and naturally readable?
  3. Keyword position — apply different standards by page type:
     - Homepage   : Brand + core keyword is correct (e.g. "Acme | AI Workflow Automation")
                    Do NOT flag brand-first as a problem.
     - Inner pages: Core keyword should lead (e.g. "AI Workflow Automation for Teams — Acme")
                    Flag if keyword is buried mid-title without good reason.

IMPORTANT — do NOT flag these as negatives:
  - Years (e.g. "2026") → signal freshness, increase CTR — treat as positive unless
    the page is explicitly evergreen content where dating would hurt longevity.
  - Numbers (e.g. "5 best", "Top 10", "3 steps") → set clear expectations,
    consistently outperform non-numeric titles in CTR — always treat as a plus.
  - Specific qualifiers ("Open-Source", "Self-Hosted", "Free") → narrow intent
    and attract higher-quality clicks — do not penalize.

URL Slug — triggered when keyword_match != "full" or is_homepage == false:

slug    : (from url_slug.slug)
keyword : (the --keyword passed)

Judge:
  1. Does the slug contain the primary keyword or a natural variant?
  2. Is the path hierarchy logical? (/category/keyword is ideal)
  3. Is it concise and human-readable?
  Homepage (is_homepage: true): skip — no judgment needed.

Meta Description — always triggered when content is present:

meta_description : (from meta_description.value)
keyword          : (the --keyword passed)

Judge all four:
  1. Complete sentence(s)? (1-2 sentences, no fragments)
  2. Mentions a concrete result — not vague fluff?
     Good: "Cut design time by 60% with AI-powered templates"
     Bad:  "The best tool for all your design needs"
  3. Keyword or natural synonym used once — not stuffed?
  4. More specific than what a typical competitor would write?

IMPORTANT — do NOT flag these as negatives:
  - Years (e.g. "2026") → signal freshness, improve CTR for time-sensitive queries.
    Only note the year if the page is explicitly evergreen content where dating hurts.
  - Numbers (e.g. "5 best", "3 steps") → concrete specificity, strong CTR signal.
  - Trailing "and more." → minor style note at most, never a Warning or Fail.

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Recommended Workflow

Follow these steps in order:

1. Acknowledge scope — confirm this is a basic audit; note any missing data

2. Infer primary keyword — fetch the page with fetch-page.py, then determine the primary keyword:

   • If the user explicitly provided a keyword → use it directly
   • If not → read the page H1, title, and first paragraph, then infer the single most likely target keyword phrase (what would a searcher type to find this page?)
   • State the inferred keyword explicitly before running checks:

     "Inferred primary keyword: open source claude alternatives"

3. Run check-site.py — parse the JSON output for robots, sitemap, 404 handling, and URL canonicalization

   404 check: fetch <origin>/this-page-definitely-does-not-exist-seo-audit-check

   • Returns 404 → Pass · Returns 200 (soft 404) → Fail · Returns 301 to homepage → Warn

   URL Canonicalization checks (each is a separate sub-check):

   • HTTP→HTTPS: fetch http://<host> — must 301 to https://. Returns 200 → Fail.
   • www consistency: fetch both https://www.<host> and https://<host> — one must 301 to the other. Both return 200 → Warn.
   • Trailing slash: compare the URL actually served vs the canonical tag on the page. Mismatch → Warn.
   • Canonical match: canonical tag href must exactly match the final URL after all redirects. Mismatch → Warn.

4. E-E-A-T infrastructure check — for each trust page below, check two layers:

   • Layer 1 — Exists: fetch the URL, check HTTP status (200 = exists, 404/redirect = missing)
   • Layer 2 — Reachable: fetch homepage HTML, check if footer or nav contains a link to this page

   Page	Required
   About Us	Yes
   Contact	Yes — see Contact-specific rules below
   Privacy Policy	Yes
   Terms of Service	Yes
   Media / Partners	No — include only if present

   Status rules (About, Privacy, Terms, Media/Partners):

   • Page missing (non-200) → Fail
   • Page exists but not linked in footer/nav → Warn
   • Page exists and linked in footer/nav → Pass
   • Optional page missing → skip, do not include row

   Contact-specific rules — a dedicated /contact page is optional:

   1. Try common paths (/contact, /contact-us) — HTTP 200 counts as Exists
   2. If no contact page, check the About page body for email, social, or contact details
   3. Also scan homepage footer and nav for mailto:, visible email, social links, or a contact form
   4. Exists Pass if any contact pathway is found (dedicated page, About, or footer/nav contact details)
   5. Exists Fail only when no contact information is found anywhere
   6. Reachable Pass if a contact page link or contact details appear in footer/nav
   7. Reachable Warn if contact is only reachable inside About page content, not directly in footer/nav
   8. Do not recommend creating /contact when About or footer already expose contact info — note the existing pathway instead

5. Run check-page.py --keyword "<inferred_keyword>" — parse the JSON output for H1, title, meta description, canonical, and URL slug

6. i18n / hreflang check — only run if the page contains hreflang tags or <html lang> suggests multi-language:

   • Skip entirely (N/A) if no hreflang tags found and site appears single-language
   • If hreflang tags present, check:
     • Reciprocal symmetry: every URL referenced must link back to all other variants — any broken link = Fail
     • Language codes: must be valid BCP 47 (e.g. zh-CN not zh, en-US not en-us) — wrong code = Warn
     • x-default: should be present for language-selector or fallback pages — missing = Warn
     • html[lang] attribute: must match the primary hreflang of the page — mismatch = Warn
     • URL structure: recommended pattern — default language (usually en) at root with no prefix, other languages under subpaths (/zh/, /es/).
       • /page (en) + /zh/page + /es/page → Pass
       • /en/page + /zh/page → Warn (en prefix is redundant, wastes crawl depth)
       • Only flag if the pattern is clearly inconsistent or en is unnecessarily prefixed

7. Run check-schema.py — parse the JSON output for schema types and field validation

   python scripts/check-schema.py https://example.com
   # Or from previously fetched HTML:
   python scripts/check-schema.py --file page.html
   

   The script extracts JSON-LD blocks, validates @type and required fields per Schema.org spec. llm_review_required: true is always set — confirm inferred_page_type matches actual page content.

   Page type → expected @type reference:

   Page Type	Expected @type	Min. required fields
   Homepage	WebSite + Organization	name, url, logo
   Blog / Article	Article or BlogPosting	headline, datePublished, author, image
   Product	Product	name, image, offers (price, priceCurrency)
   FAQ	FAQPage	mainEntity[].name, acceptedAnswer.text
   How-to	HowTo	name, step[].text
   Local business	LocalBusiness	name, address, telephone
   Generic landing	—	N/A — skip, no widely-supported type

   • Pass: correct @type present, all required fields valid, no conflicts
   • Warn: @type present but missing recommended fields
   • Fail: expected @type missing entirely
   • N/A: generic landing page — do not penalize

8. Summarize findings — each finding must follow the Evidence / Impact / Fix format

9. Priority actions — list the top 3 highest-impact fixes

10. Render report — save to reports/<hostname>-<slug>-audit.html, then ask user to open

11. Upgrade prompt — if issues beyond basic scope are found, suggest seo-audit-full

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Report Detail Writing Rules

The Detail cell in check tables must follow these rules — no exceptions:

Pass → one short phrase. No lists, no elaboration.

Good: "Valid XML urlset · 104 URLs · referenced in robots.txt."
Bad:  "Valid XML urlset with 104 URLs. Correctly referenced in robots.txt.
       Blog posts are likely indexed through this sitemap."

Warn → one <div class="detail-issue"> with ≤2 bullet points. One <div class="detail-fix"> with the fix.

Good:
  <div class="detail-issue">· Title 48 chars — 2 below minimum. · Year "2026" will date the page.</div>
  <div class="detail-fix">Expand to 50–60 chars; remove year if evergreen.</div>

Bad: three-sentence prose explaining what a title tag is and why length matters.

Fail → same as Warn. Lead with the exact failure. No background explanations.

Do NOT explain what a check is, do NOT repeat information already visible in the status badge, do NOT treat the reader as unfamiliar with SEO basics.

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Mandatory Finding Format

Every important finding must follow this structure:

**Finding: [Finding Title]**

- **Evidence:** [What was observed — direct quote, screenshot ref, or measurable data]
- **Impact:** [Why this matters for SEO or UX]
- **Fix:** [Specific, actionable recommendation]

Do not write vague conclusions. If evidence is insufficient, state assumptions explicitly.

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Upgrade Prompt

Include this at the end of every basic audit report:

Want a deeper analysis? This was a basic SEO audit covering site-level signals and core on-page checks. For advanced technical SEO, content quality scoring, structured data analysis, and full crawl-based findings, use the seo-audit-full skill.

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Reference Files

• Detailed audit scope and field definitions: references/REFERENCE.md
• Final HTML report template: assets/report-template.html
• Site-level check script: scripts/check-site.py
• Page-level check script: scripts/check-page.py
• Raw page fetcher: scripts/fetch-page.py
• Schema validation script: scripts/check-schema.py