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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). 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).

Quality

85%

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Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

70%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Well-structured operational content with strong workflow sequencing (validation loops, checklists, defined outputs) and honest guardrails, but it is undermined by referencing six template files that are missing from the bundle, which weakens both executability and navigation. Minor duplication of the frontmatter description also costs some token efficiency.

Suggestions

Ship the six referenced template files (templates/page-metadata.html, techarticle.jsonld, faqpage.jsonld, robots-ai-crawlers.txt, llms.txt, quotable-article-outline.md) or inline their minimal formats in the body — Mode 1 and all three examples currently point at files that cannot be loaded.

Remove or compress the "When to use / not use" section and the H1 intro line, which restate the frontmatter description; the trigger routing already lives in the description.

In the References table, mark which files are templates to copy vs. references to read (or split them into two tables), so the load-on-demand intent is clearer per file.

DimensionReasoningScore

Conciseness

The body is mostly operational (pillar table, numbered modes, guardrails, troubleshooting) with no basic-concept padding, but the H1 intro line and the entire "When to use / not use" section duplicate the frontmatter description's trigger list — minor trimming opportunities, matching 4 rather than the fully lean 5.

4 / 5

Actionability

Audit guidance is concrete (specific crawlers like OAI-SearchBot/GPTBot, named validators, pass/partial/fail sweep, severity ordering), but Mode 1 and all three examples hinge on six `templates/*` files (page-metadata.html, techarticle.jsonld, faqpage.jsonld, robots-ai-crawlers.txt, llms.txt, quotable-article-outline.md) that are absent from the bundle — the key execution formats are missing, fitting 3's "incomplete; missing key details" over 4's "minor gaps".

3 / 5

Workflow Clarity

Each operating mode has a clearly numbered sequence, Mode 3 includes an explicit feedback loop ("Re-validate structured data after every schema change") and severity ordering, Mode 1 ends with an eight-item checklist, and Mode 2 defines its output artifact (prioritized findings table) — matching the 5 anchor's validation steps, feedback loops, and checklists.

5 / 5

Progressive Disclosure

The on-demand load table is well signaled and the two `references/` files exist one level deep, but 6 of the 8 referenced paths (`templates/*`) do not exist in the bundle, so navigation breaks for most entry points — more than the minor organization gaps of 4.

3 / 5

Total

15

/

20

Passed

Description

95%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete trigger phrases with synonyms and multilingual variants, explicit use/not-use boundaries that disambiguate it from four neighboring skills, and a clear statement of what the skill does. The only softness is that the core what is framed as a role/domain characterization rather than a complete list of concrete operations.

DimensionReasoningScore

Specificity

Names several concrete actions and deliverables ("write an llms.txt", "add citation-ready structure or schema for AI answers", "audit/create/improve") alongside the five-pillar scope, but the core what is a domain characterization with underlying on-page operations only implied via triggers — a minor coverage gap that fits 4 rather than the comprehensive action list of 5.

4 / 5

Completeness

Explicitly answers both what ("the technical, on-page publishing work that makes a given page or site discoverable, understandable, trustworthy, quotable, and fresh for AI answer engines") and when (an explicit "Use when asked to…" clause with concrete trigger phrases plus negative triggers), exactly matching the 5 anchor.

5 / 5

Trigger Term Quality

Comprehensive natural phrasing users would actually say — "optimize this page/site for GEO", "get my page cited by ChatGPT/Perplexity", "improve AI visibility/citability", "write an llms.txt" — plus named engines and even a non-English variant ("otimizar para busca com IA"), matching the comprehensive-synonyms anchor.

5 / 5

Distinctiveness Conflict Risk

Clear GEO niche with explicit routing away from four adjacent skills ("use ai-seo", "use seo", "use web-accessibility", "use web-quality-audit"), minimizing the risk of triggering for the wrong skill.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

tech-leads-club/agent-skills

Path

packages/skills-catalog/skills/(quality)/tlc-generative-engine-optimization/SKILL.md

Reviewed

Table of Contents

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