Technical Seo Audit logo

Technical Seo Audit

Organization
LeadMagic
technical-seo-audit

Audit a website's technical SEO across crawl access, indexing controls, canonicalization, sitemaps, internal links, rendered content, structured data, status codes, and Core Web Vitals. Use when organic pages are missing from search, a redesign or migration is planned, Search Console reports coverage problems, or the user asks for a prioritized technical SEO audit.

Overview

PublisherLeadMagic
Repositorygtm-skills
Skill nametechnical-seo-audit
Stars
72
Forks
18
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Technical 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/LeadMagic/gtm-skills/tree/main/skills/content-seo/technical-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/LeadMagic/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/content-seo/technical-seo-audit .claude/skills/technical-seo-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Technical 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 Technical 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 Technical 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.

Technical SEO Audit

Overview

A useful technical SEO audit proves where discovery, crawling, rendering, indexing, canonicalization, or page experience breaks. It does not produce a hundred-tool checklist with no evidence or promise rankings from passing a score.

When to Use

  • Important pages are not indexed or organic traffic dropped unexpectedly.
  • A site migration, domain change, redesign, CMS change, or URL rewrite is planned.
  • Search Console reports indexing, structured-data, sitemap, or Core Web Vitals issues.
  • Programmatic pages need crawl-budget and duplicate-control review.
  • The user asks for a technical SEO audit, crawl audit, indexation audit, or SEO migration QA.

Authoritative Foundations

  • Google Search Essentials: evaluate technical eligibility, spam-policy risks, crawlable links, and people-first page signals without claiming that compliance guarantees indexing or ranking.
  • Google Search Central — Crawling and Indexing: separate crawl controls from index controls; robots.txt manages crawling, while noindex or access control manages index exclusion.
  • Google Search Central — Canonicalization and Sitemaps: treat redirects and rel=canonical as stronger canonical signals than sitemap inclusion, and include only preferred canonical URLs in sitemaps.
  • Google Search Central — Structured Data Guidelines: validate only markup that represents visible page content and report rich-result eligibility rather than promising presentation.
  • web.dev — Core Web Vitals: evaluate field data at the 75th percentile against LCP ≤2.5s, INP ≤200ms, and CLS ≤0.1, segmented by mobile and desktop where data exists.

Prerequisites

  • Site or representative URL set, target market, and business-critical page types.
  • Search Console access when available; otherwise state that index evidence is limited.
  • Crawl permission and a bounded request rate suitable for the site.
  • Deployment, framework, CMS, CDN, and migration context.
  • A clear distinction between production, staging, authenticated, and blocked surfaces.

Step-by-Step Process

1. Define the audit universe

Build a sample by page type and business value. Record hostname variants, protocols, locales, parameters, staging hosts, and known legacy paths. Do not infer sitewide health from the homepage.

2. Test access and response behavior

Check DNS/TLS, redirect chains, final status, HTML response, robots directives, X-Robots-Tag, and authentication. Flag soft 404s, redirect loops, mixed canonical targets, and indexable staging pages.

3. Trace discovery and crawling

Review robots.txt, XML sitemaps, internal links, orphan candidates, pagination, faceted URLs, and rendered link elements. Distinguish blocked crawling from excluded indexing.

4. Reconcile indexing and canonical signals

Compare declared canonical, sitemap URL, internal-link target, redirect target, hreflang cluster, and Search Console-selected canonical. Group conflicts by template so fixes address root causes.

5. Inspect rendered content and metadata

Compare raw HTML with rendered output for title, description, canonical, headings, primary content, links, and structured data. Note client-side delays or errors that hide decisive content.

6. Validate structured data

Check syntax, required properties, visible-content agreement, URL consistency, and feature-specific eligibility. Use the Rich Results Test or equivalent evidence when available.

7. Evaluate performance with field-first evidence

Prefer CrUX or Search Console field data; use lab tests for diagnosis. Report LCP, INP, and CLS by template and device. Do not average away a failing 75th percentile.

8. Prioritize fixes

Score each issue by affected valuable URLs, severity, confidence, effort, and dependency. Separate blocking indexation defects from enhancements and monitoring work.

9. Create verification cases

For every priority fix, define a pre-change failing URL, expected observable result, test method, owner, and post-deploy monitoring window.

Output Format

Deliver:

  1. Executive status and evidence limits.
  2. Crawl/index funnel with counts by page type.
  3. Issue register with example URLs and reproduced evidence.
  4. Canonical/sitemap/internal-link conflict matrix.
  5. Rendered metadata and structured-data findings.
  6. Core Web Vitals table using field and lab evidence separately.
  7. Prioritized remediation backlog.
  8. Release verification and monitoring plan.

Quality Check

  • Every issue includes at least one reproducible URL or source report.
  • Crawl, indexing, ranking, and rich-result eligibility are not conflated.
  • robots.txt is not recommended as a guaranteed deindexing mechanism.
  • Canonical recommendations align redirects, internal links, and sitemaps.
  • Field and lab performance data are labeled separately.
  • Core Web Vitals thresholds use LCP 2.5s, INP 200ms, and CLS 0.1 at p75.
  • Fixes are prioritized by business impact and affected templates, not tool severity alone.
  • No ranking or rich-result outcome is guaranteed.

Common Pitfalls

PitfallWhy it failsFix
Treating a crawler score as the auditScores hide evidence and business impactShow affected URLs, templates, and search-state evidence
Blocking a URL in robots.txt to remove itA blocked URL can remain indexed without a snippetAllow crawling long enough to process noindex, or require authentication
Fixing canonicals only in HTMLConflicting sitemaps and links keep sending mixed signalsAlign redirects, canonicals, sitemaps, and internal links
Using only Lighthouse lab dataLab runs do not represent the user distributionLead with field data and use lab traces for diagnosis
Adding schema unrelated to visible contentViolates eligibility guidelinesMark up accurate, visible, feature-supported content only

Execution Artifacts

  • references/framework-notes.md — Current technical thresholds and source routing
  • templates/output-template.md — Evidence-first technical SEO audit
  • scripts/check-output.py — Audit completeness validator

Related Skills

  • seo-strategy for keyword, content, and authority planning.
  • pseo-strategy for template-scale indexation and duplicate control.
  • pillar-pages for internal-link and topic-cluster architecture.
  • tracking-plan for analytics measurement that remains separate from indexation evidence.

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 Technical Seo Audit AI skill do?

Audit a website's technical SEO across crawl access, indexing controls, canonicalization, sitemaps, internal links, rendered content, structured data, status codes, and Core Web Vitals. Use when organic pages are missing from search, a redesign or migration is planned, Search Console reports coverage problems, or the user asks for a prioritized technical SEO audit.

Why use Technical Seo Audit on TypingMind?

Because you install it once and use it with any model. Technical 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 Technical Seo Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LeadMagic/gtm-skills/tree/main/skills/content-seo/technical-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 Technical 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 Technical Seo Audit?

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

Is the Technical Seo Audit AI skill free?

Yes. It is published on GitHub by LeadMagic 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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