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Seo

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mblode
seo

Audits and fixes technical SEO, researches search demand, creates content briefs, and measures SEO/AEO performance. Use when asked to "audit SEO", "fix indexing", "improve AI visibility", "check keyword volume", "write an SEO brief", or "why did organic traffic drop". For Mintlify Agent Score, AFDocs, or Is Agentic reports use agent-ready. For writing the article use ghostwriter; for visual redesign use ui-design.

Overview

Publishermblode
Repositoryagent-skills
Skill nameseo
Stars
118
Forks
11
Bundled files
12
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.

  • 12 bundled files

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

  • Open source

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

Installation

Install the Seo 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.
  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/mblode/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/seo .claude/skills/seo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Seo 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 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 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

  • IS: one entry point for search visibility: evidence-led audits, implementation, demand research, writer briefs, and performance measurement across search and answer engines.
  • IS NOT: writing the article (ghostwriter), standalone marketing copy (copywriting), visual redesign (ui-design), tenant infrastructure (multi-tenant-architecture), or implementing AFDocs / Is Agentic / Is It Agent Ready scorecards (agent-ready). Fix SEO copy and markup when they are part of the requested implementation.

Route the task

RequestLoadDeliverable
Audit a site, diagnose indexing, review a migrationreferences/audit.md and references/validation-evidence.mdPrioritized findings with URLs, observed evidence, impact, and correction
Fix or implement SEOAudit references, then applicable implementation references belowCode changes, scoped checks, and served-page evidence for the changed behavior
Research demand, choose a target, brief a pagereferences/research-protocol.md; references/brief-template.md for a briefSourced demand table and decision, or a durable writer brief
Explain traffic movement, measure AI visibility, monitor SEOreferences/monitoring.mdProperty-scoped diagnosis or configured monitoring with explicit data gaps

For a combined request, reuse one property and URL inventory across modes. An audit request produces findings; a request to fix them proceeds through implementation and verification within the user's authorized scope. A research-only request does not require crawling every URL or changing code.

Establish the evidence boundary

Identify the exact public host, URL-prefix or domain property, production environment, project/subpath, audience, and business outcome. On a shared domain, filter each project's path separately. In analytics, verify hostname, path, conversion event and attribution window before interpreting a number.

Discover connected tools and existing project bindings first. Prefer the established reporting source, then an available equivalent, then a signed-in browser. No vendor is required. Ownership verification, API consent and a successful scoped query are separate states. An export for a neighboring property is not a fallback.

Use No data with its reason for inaccessible, missing, unsupported or unmeasured metrics; distinguish these from a measured zero. Label historical exports by their actual dates. Do useful public checks while an authenticated metric is unavailable.

Working sequence

For substantial mixed work, track only the applicable steps:

  • Confirm property, scope, baseline and intended outcome.
  • Inspect live evidence and map affected route patterns or research questions.
  • Prioritize blocking defects and high-value opportunities; separate observations from hypotheses.
  • Implement authorized corrections or deliver the requested research/brief.
  • Recheck the changed behavior, identify the tested environment, and report remaining gaps.

Prioritize crawl/index defects before cosmetic metadata changes. Page titles, headings and content structure follow the page's purpose and reader intent. Descriptive non-brand terms help category discovery; brand-led portfolio and product pages can be appropriate. Neither a fixed title character count nor question-shaped headings are ranking requirements.

Original evidence, useful comparisons and honest product limitations earn their place ahead of manufactured keyword permutations. Research likely reader questions; do not present invented query fan-outs as engine telemetry or create a page for every variation.

References

FileRead when
references/audit.mdCrawling, diagnosing or prioritizing a site audit
references/indexing-policy.mdCanonicals, redirects, exclusions, duplicate or programmatic pages
references/nextjs-implementation.mdImplementing App Router metadata, sitemaps, rendering, status codes, Markdown twins, or schema; verify APIs against the installed Next.js docs
references/answer-engines.mdAI visibility, crawler policies, Markdown alternatives or llms.txt
references/internationalisation.mdLanguage/region variants and hreflang
references/technical-hardening.mdCDN access, response headers, errors or preview environments affect discovery
references/validation-evidence.mdVerifying findings, a built page or a deployed correction
references/research-protocol.mdKeyword/prompt metrics, opportunity selection and question maps
references/brief-template.mdProducing a writer brief
references/monitoring.mdSearch/AI performance, attribution, anomalies or recurring work
references/sources.mdA claim depends on current engine behavior, supported reports, or vendor research

Delivery

Findings go in chat unless a file is requested. Writer briefs go to the requested project destination; retain prior dated briefs when producing a new revision. Keep scratch crawls and exports out of the repository. No em dashes in authored reports or briefs.

Report what changed, why, exact verification evidence, and what remains unmeasured. A successful local build, ready deployment, public URL probe, Google index record and measured conversion are different evidence. Name the strongest state actually verified.

Sending recaps to others, scheduling recurring work, spending on tools and production deployment follow the user's authorization and host rules. Reuse an existing matching monitor rather than creating another owner or schedule.

Gotchas

  • A sitemap can parse while listing /studio that intentionally returns 404. Check its URLs, not just XML validity.
  • A canvas gated on mounted state can leave the homepage and category hubs with only navigation in initial HTML. Inspect those hubs as well as deep pages; use client alone does not imply missing server-rendered content.
  • lastModified: new Date() or a build timestamp describes generation, not necessarily a significant page update. Use content dates or omit the field.
  • A canonical tag on each URL does not resolve two copies that both self-canonicalize. Compare content and choose the preferred URL.
  • Two consistent JSON-LD scripts are not an error. Verify entity identity, eligible properties and visible supporting content instead of script count.
  • DNS verification needs no invented meta token. Confirm scoped data access separately.
  • A crawler user-agent probe does not authenticate a bot IP or establish index inclusion. Treat CDN logs and engine inspection as separate evidence.
  • Prompt demand, measured citations, mentions, AI impressions and referral sessions are different quantities. Preserve engine, source, match type, scope and window.
  • A vendor Search Console connector may under-report. Reconcile property, filters and aggregation against native Search Console before diagnosing a drop.

Maintenance only: evals/evals.json holds behavioral scenarios and routing prompts. It is loaded when changing the skill, not during ordinary SEO work.

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 AI skill do?

Audits and fixes technical SEO, researches search demand, creates content briefs, and measures SEO/AEO performance. Use when asked to "audit SEO", "fix indexing", "improve AI visibility", "check keyword volume", "write an SEO brief", or "why did organic traffic drop". For Mintlify Agent Score, AFDocs, or Is Agentic reports use agent-ready. For writing the article use ghostwriter; for visual redesign use ui-design.

Why use Seo on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mblode/agent-skills/tree/main/skills/seo. 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?

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?

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

Is the Seo AI skill free?

Yes. It is published on GitHub by mblode 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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