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tlc-generative-engine-optimization

Generative Engine Optimization (GEO) specialist — the technical, on-page publishing work that makes a given page or site discoverable, understandable, trustworthy, quotable, and fresh for AI answer engines (Google AI Overviews, ChatGPT Search, Bing Copilot, Perplexity)

Install / Use

npx skills add tech-leads-club/agent-skills --skill tlc-generative-engine-optimization

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

88/100

Supported Platforms

GitHub Copilot

Our assessment of tlc-generative-engine-optimization

tlc-generative-engine-optimization scores 88/100 on our quality scale, 294th of 811 AI & Machine Learning skills we index (top 37%).

Its SKILL.md is 9.8 KB long, well organised into 15 sections and no code examples: a thorough specification that gives an agent plenty to work with.

With 6,832 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
13/20
Description
15/15
Adoption
16/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 7 days ago, so tlc-generative-engine-optimization is actively maintained.
  • No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
  • Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

tlc-generative-engine-optimization compared with similar skills

All 4 of these similar skills score higher than tlc-generative-engine-optimization; compare them before choosing.

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tlc-generative-engine-optimization (this skill)by tech-leads-club886.8k7d agoSKILL.md
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Frequently asked questions

How do I install tlc-generative-engine-optimization?
Run npx skills add tech-leads-club/agent-skills --skill tlc-generative-engine-optimization. The install tabs above show the steps for each supported agent.
Which AI agents does tlc-generative-engine-optimization work with?
It is written for GitHub Copilot, as a SKILL.md file. Other agents that read the same format can often use it too.
Is tlc-generative-engine-optimization safe to use?
It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is tlc-generative-engine-optimization still maintained?
The repository was last updated 7 days ago, so tlc-generative-engine-optimization is actively maintained.

name: tlc-generative-engine-optimization description: "Generative Engine Optimization (GEO) specialist — the technical, on-page publishing work that makes a given page or site discoverable, understandable, trustworthy, quotable, and fresh for AI answer engines (Google AI Overviews, ChatGPT Search, Bing Copilot, Perplexity). Use when asked to 'optimize this page/site for GEO', 'optimize for AI search / answer engines', 'get my page cited by ChatGPT/Perplexity', 'improve AI visibility/citability', 'write an llms.txt', 'add citation-ready structure or schema for AI answers', 'otimizar para busca com IA', or to audit/create/improve a codebase for generative search. Do NOT use for AI-driven SEO content strategy or programmatic pages at scale (use ai-seo), classic keyword/SERP ranking (use seo), accessibility (use web-accessibility), or multi-area site audits (use web-quality-audit)." metadata: version: '1.0.0' author: Fernando Paladini - github.com/paladini license: MIT

GEO Specialist

Expert in Generative Engine Optimization — making pages discoverable, understandable, trustworthy, quotable, and fresh for AI answer engines.

Philosophy

Treat GEO as documentation quality, not a trick. AI engines cite pages they can parse, trust, and quote. The work is the same as writing clearly for humans: correct metadata, honest structured data, authoritative prose, stable URLs. Never promise rankings or AI citations — those are engine decisions outside your control. Do the technical work well; citations follow as a byproduct.

When to use / not use

Use this skill when the goal is making a specific page or site more visible, citable, or understandable to AI answer engines — technically and at the page level.

Do NOT use for:

  • AI-driven content strategy or programmatic pages at scale → use ai-seo
  • Classic keyword/SERP ranking work → use seo
  • Accessibility audits → use web-accessibility
  • Multi-area site health audits → use web-quality-audit

The Six GEO Pillars

Load references/pillars-and-workflow.md for the full deep-dive. Summary:

| # | Pillar | Core check | | --- | ------------------ | ------------------------------------------------------------------------------- | | 1 | Discoverable | robots.txt allows AI crawlers; sitemap exists; canonical tags correct; HTTPS | | 2 | Understandable | Semantic HTML; page title matches H1; language declared; one topic per page | | 3 | Useful | Content answers a specific question; content in static HTML (not JS-only) | | 4 | Trustworthy | Author bio; citations/sources linked; publication + update dates visible; HTTPS | | 5 | Quotable | One answer per section; short-answer paragraph before elaboration; FAQ schema | | 6 | Fresh | dateModified in JSON-LD and meta; content reviewed when topic changes |

Operating Modes

Mode 1 — Create (new GEO-ready page)

  1. Plan page structure: one topic, one H1, question-based H2s/H3s.
  2. Apply templates/page-metadata.html (canonical, hreflang, meta description).
  3. Add templates/techarticle.jsonld (or faqpage.jsonld for FAQ pages).
  4. Write content in the quotable outline pattern (templates/quotable-article-outline.md): short direct answer → supporting detail → sources.
  5. Update robots.txt to allow AI crawlers (templates/robots-ai-crawlers.txt).
  6. Add or update llms.txt if the site wants to guide AI agents (templates/llms.txt).
  7. Run the GEO page checklist (in references/pillars-and-workflow.md).

Mode 2 — Audit (score an existing page or site)

  1. Crawl check: read robots.txt — are OAI-SearchBot and BingBot allowed?
  2. Structured data: validate all JSON-LD against the Rich Results Test and Schema Markup Validator.
  3. Pillar sweep: for each of the six pillars, mark pass / partial / fail.
  4. Produce a prioritized findings table (Pillar → Finding → Severity → Fix).
  5. Identify quick wins (metadata, schema, robots) vs. content rewrites.

Mode 3 — Improve (apply fixes)

  1. Apply fixes in severity order: blockers first (crawl access, broken schema), then quick wins (metadata, dates), then content improvements.
  2. Re-validate structured data after every schema change.
  3. After changes, point to measurement tools (see references/measurement-and-tools.md) so the user can track AI visibility over time.

Guardrails

  • Never promise that changes will cause a specific AI engine to cite the page. Citation is an engine decision.
  • Structured data must match visible page content exactly. Mismatches violate Google's policies and can suppress the page.
  • llms.txt is optional. It is a community convention, not a crawler-control file, and not a citation guarantee. Recommend it only when the site wants to guide AI agent navigation.
  • robots.txt is the only authoritative crawler-control file. llms.txt has no effect on crawling.
  • Do not add noindex or Disallow for AI crawlers unless the user explicitly wants to block AI indexing.
  • Prefer primary platform documentation (Google Search Central, Bing Webmaster Tools, Schema.org) over third-party summaries.

Examples

Example 1 — Audit request

User: "Can you audit my blog for AI search visibility?"

Actions:

  1. Check robots.txt → OAI-SearchBot is missing a Disallow but also missing an explicit Allow — confirm default is allow.
  2. Validate JSON-LD on the homepage → datePublished is missing, author has no url.
  3. Run pillar sweep → Trustworthy: partial (no author bio page); Quotable: fail (no FAQ schema on FAQ page).
  4. Return findings table with three priority tiers.

Result: Prioritized list: fix techarticle.jsonld, add author bio, add FAQPage schema. Clear, actionable, no ranking promises.

Example 2 — Create request

User: "Create a new GEO-optimized article page for my Next.js blog."

Actions:

  1. Draft <head> from templates/page-metadata.html.
  2. Generate templates/techarticle.jsonld filled with real title, author, dates.
  3. Structure content using templates/quotable-article-outline.md: direct-answer intro, H2/H3 sections, sources list.
  4. Confirm robots.txt allows OAI-SearchBot.
  5. Run checklist — all eight items pass.

Result: Ready-to-deploy page with correct metadata, valid schema, and citation-ready prose.

Example 3 — llms.txt request

User: "Write an llms.txt for my documentation site."

Actions:

  1. Inventory the three or four most useful pages for an AI agent.
  2. Apply templates/llms.txt format: H1 site name → blockquote description → ## Key pages with Markdown links → optional ## Technical files.
  3. Remind the user that llms.txt is not a crawler-control file and doesn't guarantee citations.

Result: A concise, standards-compliant llms.txt with honest caveats.

Troubleshooting

| Symptom | Likely cause | Fix | | ---------------------------------------------------- | ------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- | | Rich Results Test shows no schema | JSON-LD is in a JS-rendered <script> tag loaded after DOMContentLoaded | Move JSON-LD to a static <script type="application/ld+json"> in server-rendered HTML | | Schema validation error: "required property missing" | datePublished, author, or headline absent | Add all required fields; check Schema.org/TechArticle for the full list | | OAI-SearchBot not crawling | User-agent: * Disallow: / in robots.txt blocks all bots | Add explicit Allow: / for OAI-SearchBot above the wildcard rule | | llms.txt not picked up by agents | File not at https://example.com/llms.txt (must be root) | Move file to domain root; verify it returns Content-Type: text/plain | | Content visible in browser but not cited | Content rendered by client-side JS only | Render content server-side so crawlers receive it in the initial HTML response |

References and Templates

Load these files on demand — only when the task requires the detail.

| File | Load when | | --------------------------------------- | ------------------------------------------------------------------------------------------------- | | references/pillars-and-workflow.md | You need the full pillar deep-dive, four-step page workflow, or the eight-item GEO page checklist | | references/measurement-and-tools.md | User asks how to measure GEO results, which tools to use, or what to track after publishing | | templates/page-metadata.html | Creating or fixing <head> metadata (canonical, hreflang, meta description, open graph) | | templates/techarticle.jsonld | Adding TechArticle structured data to an article page | | templates/faqpage.jsonld | Adding FAQPage structured data to a FAQ section | | templates/robots-ai-crawlers.txt | Updating robots.txt for AI crawler controls (OAI-SearchBot, GPTBot, BingBot) | | templates/llms.txt | Writing or updating the site's llms.txt | | templates/quotable-article-outline.md | Structuring article content for AI citation |

Related Skills

View on GitHub
GitHub Stars6.8k
CategoryAI
Updated7d ago
Forks551

Languages

TypeScript

Trust signals

88/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

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