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Seo Audit

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JeffLi1993
seo-audit

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.

Overview

PublisherJeffLi1993
Repositoryseo-audit-skill
Skill nameseo-audit
Stars
765
Forks
99
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by JeffLi1993 on GitHub. Read the source before you install it.

Installation

Install the Seo Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/JeffLi1993/seo-audit-skill/tree/main/seo-audit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/JeffLi1993/seo-audit-skill.git /tmp/seo-audit-skill
mkdir -p .claude/skills
cp -r /tmp/seo-audit-skill/seo-audit .claude/skills/seo-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Seo Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Seo Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Seo Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.


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.


Input Expected

InputRequiredNotes
Page URLYesThe page to audit
Raw HTML or page contentOptionalEnables more accurate on-page analysis
GSC / analytics dataOptionalNot 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.


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


Template placeholders — fill each independently:

PlaceholderContent
{{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>

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)

bash
# 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.

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
    PageRequired
    About UsYes
    ContactYes — see Contact-specific rules below
    Privacy PolicyYes
    Terms of ServiceYes
    Media / PartnersNo — 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

    bash
    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 TypeExpected @typeMin. required fields
    HomepageWebSite + Organizationname, url, logo
    Blog / ArticleArticle or BlogPostingheadline, datePublished, author, image
    ProductProductname, image, offers (price, priceCurrency)
    FAQFAQPagemainEntity[].name, acceptedAnswer.text
    How-toHowToname, step[].text
    Local businessLocalBusinessname, 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


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.


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.


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.


Reference Files

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Seo Audit AI skill do?

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.

Why use Seo Audit on TypingMind?

Because you install it once and use it with any model. Seo Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Seo Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JeffLi1993/seo-audit-skill/tree/main/seo-audit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Seo Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Seo Audit?

As many as you like. As long as a model supports skills, you can use Seo Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Seo Audit AI skill free?

Yes. It is published on GitHub by JeffLi1993 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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