「aeo」の検索結果

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.

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。

AI Engine Optimization - semantic triples, page templates, content clusters for AI citations

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。

AI-powered diary generation for agents - creates rich, reflective journal entries (400-600 words) with Quote Hall of Fame, Curiosity Backlog, Decision Archaeology, and Relationship Evolution. Generates personal, emotional entries from the agent's perspective. Works best with Claude models (Haiku, Sonnet, Opus).

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。

Use when you want an SEO and AEO optimized blog post from source material, including feature announcements, launches, tutorials, or thought leadership.

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。

Generates SEO and AEO optimized blog posts from source content. Supports feature announcements, product launches, thought leadership, cookbooks/tutorials, report conversions, and news analysis. Outputs complete posts with TLDR, FAQ, schema markup, and internal linking recommendations.

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。

Build an exhaustive first-growth-hire distribution operating system across B2B and B2C channels: LinkedIn, events, AI outbound, AEO, blogs, long-form writing, YouTube, webinars, X, influencers, AI UGC, organic TikTok, paid social, sponsorships, partnerships, lifecycle, referral, and community. Use when "growth hacker", "first growth hire", "distribution hire", "cover every channel", "channel hacking", "0 to ARR growth system", "growth operator", "growth hacker OS", or any request to fan out channel operators and plug the result into Kai workflows.

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。

CRISP session manager — detects project state and routes to the right entry point. Use /crisp to start or resume any CRISP engagement. Handles new projects (discovery), existing codebases (archaeology), active sprints (execute), and validation (prove). Reads docs/crisp-state.json to know where you are.

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。

Generate a beautiful single-file HTML report from the project's docs/ folder. Works after any CRISP phase, after Archaeology, or at sprint close in Execute. Triggers on "generate report", "export docs", "create HTML report", "share docs", or automatically at sprint close in CRISP Execute.

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。

Use when asked to audit, score, or health-check a WordPress site or page. Runs SEO, AEO, readability, accessibility, performance, RankMath, and Core Web Vitals analyzers and presents a unified report with prioritised fixes.

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。

Use when the user wants to get found in Google or AI engines (ChatGPT, Perplexity, AI Overviews) — keyword and content strategy, technical or content SEO audits, schema markup, link building, topical clustering, E-E-A-T, AI citation (AEO), or deciding whether SEO is even the right channel for their startup. Calibrated for early-stage, low-authority sites. Not for paid acquisition or channel-mix/CAC analysis (use channel-expert), or writing the page copy itself (use copywriter-skill).

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Perform controlled dismantling of rigid system structures while preserving essential capabilities (imaginal discs). Covers rigidity mapping, dissolution sequencing, knowledge extraction, interface archaeology, and safe decomposition of technical debt and organizational calcification. Use when assess-form classified the system as PREPARE or CRITICAL, when a system is so calcified that incremental change is impossible, when technical debt blocks all forward progress, or before adapt-architecture when the current form must be softened before it can be reshaped.

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Builds, measures, and improves an Answer Engine Optimization program across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and Copilot. Use for AEO, GEO, LLMO, AI search visibility, AI citations, brand mentions, answer-engine monitoring, llms.txt, AI crawler audits, or questions like 'what does AI say about us?' and 'why are competitors cited instead of us?'. Produces a durable evidence report and prioritized gameplan, not a one-shot content checklist.

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。

Generative Engine Optimization plus agent readiness. Use this skill whenever the user asks about GEO, AEO, LLMO, optimizing content for AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, Copilot), getting cited by LLMs, brand mentions in AI responses, llms.txt, robots.txt for AI crawlers, Schema.org JSON-LD, FAST framework, agent readiness, MCP server discovery, Web Bot Auth, OAuth for agents, API Catalog, agentic commerce (x402, ACP, UCP), Markdown content negotiation, or auditing a site or page for AI visibility. Also trigger when the user says "AI SEO", asks how to "rank in ChatGPT", "show up in Perplexity", "appear in AI Overviews", or wants a site to "speak to AI agents". Trigger even when the request is implicit, such as "review this article so AI search picks it up" or "make our docs agent-friendly".

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。

Audit technical, on-page, and AI-search SEO for pages, sites, and content. Use when the user asks for `/seo-audit`, `/ai-seo`, SEO review, keyword targeting, heading structure, metadata, internal linking, content gaps, AEO, GEO, LLMO, or competitor SEO comparisons.

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。

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".

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。

Generate MLS-compliant property listing copy in five length variants (tagline, MLS-short ≤250 char, MLS-long ≤4000 char, marketing description ~600-800 words, social caption) PLUS schema.org RealEstateListing JSON-LD for SEO/AEO syndication..

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。

Comment-archaeology sweep - find work items hiding in TODO/FIXME/for-now comments and plan-doc ledgers, judge them, file issues, queue trivial fixes. Good idle-loop activity.

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。

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

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。

Apply this opinionated workflow when reverse-engineering legacy or undocumented systems: scope, explore the code, trace data flows, document in EARS format, flag uncertainties. For code archaeology, onboarding, and requirements extraction.

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.

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。

Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.

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。

Run an infrastructure-first security audit of a repository - secrets archaeology, dependency supply chain, CI/CD pipeline, LLM/AI attack surface, agent skill supply chain, OWASP Top 10, STRIDE threat model, and data classification - with a confidence gate and a pre-emit verification gate that suppress speculative findings. Use when asked for a security audit, threat model, pentest-style review, OWASP pass, or a pre-release security check. Not for reviewing a single diff - use review-changes or requesting-code-review for that.

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。

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for Ch...

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。

Systematically explore an unfamiliar codebase and build a reusable architecture summary. Use for repo onboarding, understanding legacy or inherited code, mapping architecture before a migration or refactor, tracing a bug or feature path, or answering "what does this project do?". Documentation-first, then data flow — ends in a concise technical architecture summary with file:line evidence, not a raw file dump.

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。