「aeo」の検索結果

Reverse-engineering specialist that extracts specifications from existing codebases. Use when working with legacy or undocumented systems, inherited projects, or old codebases with no documentation. Invoke to map code dependencies, generate API documentation from source, identify undocumented business logic, figure out what code does, or create architecture documentation from implementation. Trigger phrases: reverse engineer, old codebase, no docs, no documentation, figure out how this works, inherited project, legacy analysis, code archaeology, undocumented features.

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Use when designing or rewriting a high-converting landing page (single-offer page) for SaaS/apps/services. Covers structure, layout patterns, conversion strategies, copywriting, SEO/AEO, and common pitfalls.

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Use when designing or rewriting a high-converting SaaS pricing page (structure, plan design, copywriting, SEO/AEO, FAQs, layout patterns, experiments). Includes checklists, templates, and common pitfalls.

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Performs git archaeology, changelog analysis, and builds a verified factual timeline by cross-referencing git history with source material. REX mode only (skipped automatically in Concept mode). Use when building a REX talk and you need verified commit metrics, release timelines, and contributor data from a git repository.

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Analyze AI config evolution in a git repo. Use when mapping AI adoption history, finding when configs were first introduced, charting commit velocity by month, or identifying maturity phases in a project's AI tooling.

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Measure real AI-engine visibility — traffic from ChatGPT, Perplexity, Claude, Gemini and which pages they cite — from actual GA4 referral data, not prompt sampling. Use when asked "AI visibility", "GEO/AEO check", "do LLMs cite us", "traffic from ChatGPT", or "how do we show up in AI search".

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Rewrite existing pages so AI assistants can extract, quote, and cite them. Claude restructures your content into answer-ready blocks — direct answers, comparison tables, stats with sources, schema — without wrecking the human reading experience. Platform: AI visibility.

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Generative Engine Optimization (GEO) — make content rank in AI search answers from ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Audits existing content, rewrites for AI citation, and produces per-engine strategy. Use when asked to "optimize for AI search", "rank in ChatGPT", "GEO audit", "improve AI citations", "rank in Perplexity", "AI Overview optimization", "AI Overview ranking", "LLM SEO", "answer engine optimization", "AEO", "get cited by AI", "GEO", "generative engine optimization", "show up in ChatGPT", "appear in AI answers", "be cited by Perplexity", "SGE optimization", "Search Generative Experience", or "make my content show up in AI answers". Distinct from regular SEO — this targets generative engines, not traditional Google rankings.

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Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.

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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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Audit whether a website can be found, crawled, and cited by AI answer engines such as ChatGPT Search, Perplexity, Google AI Overviews, and Microsoft Copilot. Use when someone asks why their brand is missing from AI answers, whether AI crawlers can read their site, how to get cited by ChatGPT or Perplexity, or asks for a GEO or AEO (generative / answer engine optimization) review. Produces a citation baseline across buyer-intent prompts, a crawler-access check, a citability review of named pages, and a ranked fix list. Not for keyword rank tracking, paid search, or pages behind a login.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates evidence-backed citability, purpose clarity, entity clarity, structured data, and AI crawler accessibility. Suggests evidence-backed explanations and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation".

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AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation".

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Research any company, product, or service from a URL plus description and build a comprehensive, evidence-bound AEO/GEO/AI-visibility prompt panel across buyer jobs, information acts, journey states, B0-B5 proximity, aided status, roles, locales, variants, partitions, surfaces, and measurement lanes. Use when a user wants prompts to track in ChatGPT, Claude, Gemini, Perplexity, AI search, or answer engines; wants an AI-visibility measurement design; or needs a versioned panel rather than an SEO keyword list.

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Audit, question, suggest, or fact-preservingly revise a press release, blog post, contributed article, or expert explainer so AI answer systems can more easily retrieve, understand, quote, and cite its useful information. Use when someone asks for AI visibility, AI search, answer-engine, AEO, GEO, AI Overview, or ChatGPT citation optimization of supplied writing; when they want an evidence-aware pre-publication audit; or when another Newsjack workflow needs a prose-level AI-discoverability pass. Do not use as a technical SEO audit, rank tracker, publishing system, or promise of rankings, mentions, citations, traffic, or coverage.

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Audit whether AI agents can actually use your product — docs, APIs, onboarding, errors, and discoverability, evaluated from a non-human user's perspective. Use when asked if a product is agent-ready, to audit a site or API for AI usability, to prepare for agentic traffic, or when agents keep failing against your product. Produces a scored readiness report with per-surface findings and a prioritised fix list. For optimising a single article for AI citation use aeo-optimizer; for designing the MCP server itself use mcp-server-spec.

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Audit a content library, docs site, or blog for AI-generated filler that's eroding trust and search performance — and triage what to fix, rewrite, or delete. Use when asked to find slop in a content library, audit AI-written content quality, explain why content engagement or rankings dropped after scaling with AI, or set a quality bar for AI-assisted publishing. Produces an audited inventory with per-piece verdicts, the detection signals used, a triage plan, and a publishing quality gate that prevents recurrence. For a single article's AI-citability use aeo-optimizer; for the strategy itself use content-calendar or seo-content-brief.

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Audits a website codebase and makes code changes so AI engines (ChatGPT, Claude, Perplexity, Google AI Overviews) can better discover, parse, quote, and cite the site. Covers structured data, content structure, technical signals, and freshness.

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Use when deciding whether an anthropology project fits American Anthropologist (AA) and which section to target. AA is the four-field AAA flagship, so the test is significance across anthropology (sociocultural, archaeology, biological, linguistic), not single-subfield novelty. Helps frame the question and pick a section; it does not collect data or conduct fieldwork.

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Use when starting or navigating any American Anthropologist (AA) manuscript as the entry point. Routes to the right AA sub-skill based on lifecycle stage, subfield (sociocultural, archaeology, biological, linguistic), and section type (Research Article, Vital Topics Forum, World/Public/Multimodal Anthropologies, Review Essay). It dispatches; it does not draft content.

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Use when positioning an American Anthropologist (AA) manuscript against the literature so it reads as a four-field contribution. AA readers span sociocultural, archaeology, biological, and linguistic anthropology, so the paper must engage the conversations they expect and decenter a US/Anglophone-only canon. Stakes the contribution and citational politics; it does not write the lit review.

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Use when executing and reporting the analysis/interpretation for an American Anthropologist (AA) manuscript so it survives expert anonymous review — disciplined ethnographic/qualitative inference, honest interpretation, and (for biological/archaeological work) sound quantitative analysis. Guides analysis and interpretation norms; it does not fabricate evidence or quotations.

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Use when defending the research design of an American Anthropologist (AA) manuscript — fieldwork and participant observation, interview and archival design for ethnographic/qualitative work, material/contextual analysis for archaeology, and appropriate lab/quantitative design for biological anthropology. AA judges each mode on its own terms with reflexivity. Strengthens the design; it does not write code or run fieldwork.

日本語の概要は準備中です。原文の説明を表示しています。