Claude Skills by oegeyilmaz9

Use when a request concerns AEO/GEO demand research, AI-answer or generative-search query-corpus research, entity or source-landscape mapping, citation-source or competitor benchmarking, bilingual TR/EN research, missing-access planning, evidence freshness or classification, or pressure to treat unproven tactics as universal rules; do not use for page audits, content rewriting, technical SEO, schema, hreflang, implementation, or longitudinal visibility monitoring.

Measure repeatable AI-search mention, citation, answer accuracy, cited-source share, referral, retrieval traces, citation-to-claim support, uncertainty, and non-causal drift from a frozen, hash-pinned Query Corpus and contract-valid Research Pack. Use for AI visibility baselines, repeated engine/surface observations, citation monitoring, grounding-query or consulted-source capture when disclosed, access-gap reporting, or comparison runs. Do not use for live research, query discovery, optimiza...

Use when explicitly authorized to implement a bounded, approved SEO action in a codebase or content system, verify the exact change, and report its limitations; do not use for broad audits, self-approved production changes, or outcome guarantees.

Use when turning validated SEO, AEO, GEO, technical, or measurement findings into an approval-ready, evidence-linked action plan without claiming guaranteed ranking, citation, traffic, or conversion outcomes.

Audit supplied pages, documents, or content sets for direct-answer completeness, question and intent coverage, clarity, extractability, claim support, and visible-content consistency against a pinned AI-search Research Pack. Use for AEO, answer-engine optimization, answer readiness, answer extraction, featured-answer preparation, or requests asking whether content clearly and supportably answers researched questions. Do not use for generative-engine mention/citation strategy, live research, c...

Use when auditing whether an AI agent can safely understand and complete a web task through the rendered interface, DOM, accessibility tree, forms, authentication, consent, state changes, and documented protocol capabilities such as UCP, ACP, MCP, or A2A; treat this as task readiness and accessibility, not a ranking factor or guaranteed agent usage.

Use when auditing or designing site architecture, internal-link graphs, crawl depth, orphan pages, hubs, navigation, contextual anchors, pagination, facets, taxonomy, template links, or URL-to-query coverage at scale; require a bounded captured graph and business/user rationale rather than arbitrary click-depth, link-count, or PageRank-like score targets.

Use when a site needs a bounded multi-lane SEO assessment with explicit scope, evidence capture, findings, ownership, and limitations; coordinate specialists rather than produce a universal audit score or unverified implementation claim.

Use when auditing backlinks, referring domains, source citations, brand mentions, unlinked mentions, linkable assets, digital PR evidence, or off-site source landscapes from authorized first-party, Bing, vendor, or public evidence; preserve vendor metric definitions, detect manipulative risk, and never treat a proprietary authority score as universal truth or recommend link schemes.

Use when auditing or planning ecommerce search visibility across product/category pages, Product or MerchantListing structured data, Google Merchant Center, Bing shopping feeds, price/availability/shipping/returns parity, IndexNow freshness, or documented AI product-discovery and commerce integrations; require visible-page and feed truth, policy evidence, and no eligibility, ranking, recommendation, or sales guarantee.

Use when researching, briefing, or drafting fair comparison, versus, or alternative pages using dated, attributable product evidence; avoid unsupported superiority claims, copied competitor content, and artificial SEO comparisons.

Use when auditing, briefing, rewriting, or reviewing site content for a defined audience, page purpose, locale, and evidence set; produce human-reviewable content improvements without formulaic SEO or AI-citation guarantees.

Audit supplied pages, entity records, source captures, and citation observations for generative-engine entity consistency, evidence traceability, citation suitability, cited-source alignment, and documented engine controls against a pinned AI-search Research Pack. Use for GEO, generative-engine optimization, AI citations, brand or entity mentions in generative answers, source alignment, or documented AI-search control audits. Do not use for direct-answer formatting or extractability, live res...

Use when auditing or planning language-region targeting, hreflang clusters, localized URL mappings, canonical alignment, or x-default behavior from an explicit locale and URL inventory; provide safe implementation guidance without inventing translations or markets.

Use when reviewing image accessibility, discoverability, metadata, responsive delivery, image sitemaps, or visual performance from a page/media inventory; provide evidence-linked improvements without keyword, format, or ranking guarantees.

Use when auditing or planning local search visibility for a storefront, service-area business, practitioner, or multi-location brand across Google Business Profile, Bing Places, NAP/hours/categories, location pages, LocalBusiness data, reviews, duplicates, service areas, and location-aware query evidence; require authorized profile data and never promise map rankings, calls, visits, or revenue.

Use when auditing or planning publisher visibility for Google News, Discover, news search, or comparable freshness-sensitive surfaces across editorial policy, article pages, dates/bylines, corrections, large images, NewsArticle markup, news sitemaps, feeds, paywalls, and first-party reports; require current policy evidence and never promise inclusion, Top stories, Discover traffic, subscriptions, or revenue.

Use when one URL needs a bounded mixed SEO review across reader task, content, metadata, links, technical delivery, accessibility, and structured data; produce an evidence map and route each fix to its real owner instead of a page score.

Use when establishing or comparing conventional SEO performance from authorized Google Search Console, Bing Webmaster Tools, analytics, server-log, indexation, or Core Web Vitals/RUM evidence, or when designing and evaluating a controlled SEO experiment; preserve dimensions and data-quality limits, keep metric families separate, and never turn observational movement into a causal or composite SEO score.

Use when sequencing validated SEO findings and approved action-plan items into an owner-based roadmap with dependencies, review gates, measurable outcomes, and risk controls; do not use it to invent work, fixed uplift forecasts, or a one-size-fits-all calendar.

Use when designing, auditing, or improving scaled/template-driven SEO pages, data-backed landing-page systems, or large URL inventories; require source data, usefulness, variation, indexing controls, and rollout safeguards rather than scale or keyword coverage targets.

Use when researching conventional search intent, audience questions, query families, content gaps, site coverage, cannibalization, or competitor/page evidence for a bounded SEO decision; produce a provenance-aware query corpus when the work will drive measurement or implementation, retain source dates and uncertainty, and route formal multi-engine AI-search evidence to ai-search-research.

Use when detecting, validating, planning, or generating truthful Schema.org markup tied to visible page content and current search-feature documentation; do not use it to promise rich results, AI citations, or rankings.

Use when auditing, generating, or planning XML sitemap changes from a verified canonical URL inventory, discovery evidence, and current protocol guidance; do not promise crawling or indexing from sitemap inclusion.

Use when investigating crawlability, indexability, canonicalization, rendering, performance, redirects, directives, sitemaps, crawler controls, site migrations, sudden traffic/index incidents, manual actions, hacked-site recovery, or when evaluating, generating, validating, publishing, and maintaining an evidence-scoped llms.txt publisher guide; return safe, testable implementation recommendations rather than a generic technical score.

Use when auditing or planning video discovery and indexing across watch pages, embedded players, thumbnails, VideoObject markup, video sitemaps, transcripts, captions, Key Moments, livestreams, locale variants, and first-party video indexing reports; require captured player/page evidence and never promise video indexing, rich results, views, citations, or watch time.

The autonomous front door and end-to-end orchestrator for any SEO, AEO, GEO, or AI-search request. Translate ordinary user goals into a complete 27-skill coverage screen, execute every applicable specialist workflow, carry evidence and handoffs internally, and return one consolidated result to the authorized boundary; direct specialist invocation remains optional. Do not create a universal score, unsupported control, causal uplift claim, or placement guarantee.