Tlc Generative Engine Optimization

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:

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

References and Templates

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