「structured」の検索結果

Assign items to a CLOSED label vocabulary that is too large to put in a prompt — product taxonomies, category hierarchies, tag vocabularies, routing tables, ICD/SIC-style code lists. A cheap model writes the label it thinks the vocabulary would use, and an embedder snaps that writing onto the nearest legal value, so the schema is never transmitted and the output is always in-vocabulary. Use for "classify these into our taxonomy", "tag these against the existing tag list", "map these queries to categories", "the enum is too big to send", or a Literal/enum that hits a provider cap. NOT for a vocabulary that fits in a prompt — structured output measured 0.701 acc@1 there against this pattern's 0.564. NOT for open-ended labelling with no fixed vocabulary, and not for ranked retrieval over documents (bm25).

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Break out of a locked problem frame by picking one disciplined move — reframe, provocation (Po), random stimulus, SCAMPER, inversion, structured analogy, constraint play, or family traversal — and committing to it before evaluating. Use when stuck, when options feel narrow or obvious, when iterations produce variations of the same idea, or when the user says "widen this", "break out of", "think differently", "I'm stuck", "feels too obvious", "stress-test the framing", "what am I missing", or holds two related examples and asks what lies between or beyond them. Complements challenging (which evaluates) and convening-experts (which synthesizes viewpoints); this skill generates distance, not judgment.

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Use this skill when the user explicitly asks for TDD or a tests-first workflow, or when developing a new feature with a test coverage requirement. Provides a structured red-green-refactor workflow with unit, integration, and E2E test guidance.

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Structured code review through the shared OpenClaw agent-skills installation.

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Route HugeGraph MCP graph data extraction and controlled import/delete tasks to stable public tools. Use when the user asks to extract graph data from text, import structured vertices or edges, verify an import, or asks whether table, SQL, update, or delete graph writes are available.

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Route HugeGraph MCP structured graph query tasks to stable read-only tools. Use when the user asks graph questions, asks to generate Gremlin, provides Gremlin to explain, or needs read-only graph exploration.

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Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed, structured data.

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Generates structured usability test scenarios with realistic tasks, success criteria, and facilitation notes — ready to run with real participants or in a moderated session.

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。

Technical SEO audit across 9 categories: crawlability, indexability, security, URL structure, mobile, Core Web Vitals, structured data, JavaScript rendering, and IndexNow protocol. Use when user says "technical SEO", "crawl issues", "robots.txt", "Core Web Vitals", "site speed", or "security headers".

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Summarize a user interview transcript into structured insights with key themes, quotes, and action items. Use after conducting user interviews to extract and share findings efficiently.

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When the user wants to plan, map, or restructure their website's page hierarchy, navigation, URL structure, or internal linking. Also use when the user mentions "sitemap," "site map," "visual sitemap," "site structure," "page hierarchy," "information architecture," "IA," "navigation design," "URL structure," "breadcrumbs," "internal linking strategy," "website planning," "what pages do I need," "how should I organize my site," or "site navigation." Use this whenever someone is planning what pages a website should have and how they connect. NOT for XML sitemaps (that's technical SEO — see seo-audit). For SEO audits, see seo-audit. For structured data, see schema.

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When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.

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Build a structured PRD that connects problem, users, solution, and success criteria. Use when turning discovery notes into an engineering-ready document for a major initiative.

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Create a structured user interview script with warm-up, core exploration, and wrap-up sections. Use when preparing for user research interviews to ensure consistent, insightful conversations.

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Generate structured documentation templates for components, patterns, or guidelines within a design system.

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Facilitate structured design critiques with clear feedback frameworks and actionable outcomes.

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Conduct a structured competitive analysis comparing UX patterns, features, strengths, and gaps across rival products.

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Define what content a product needs, how it should be structured, and who owns it.

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When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.

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Generates a fully structured, project-specific CLAUDE.md by scanning the repo for signal files (package.json, Makefile, .github/workflows/, .env.example, etc.), inferring stack, commands, architecture, and conventions, then asking at most 3 targeted questions for what cannot be inferred. Use whenever the user says 'create a CLAUDE.md', 'my CLAUDE.md is blank', 'generate project context', 'initialize CLAUDE.md', 'set up Claude context for this repo', 'fill in my CLAUDE.md', or any variation where someone needs their project context documented for Claude. If a CLAUDE.md already exists with content, runs a diff-and-merge flow before writing.

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Establish durable brand and voice context for cross-skill consumption. Generates BRAND.md (audience, positioning, do/don't editorial rules, taboo phrases, competitor differentiation) and VOICE.md (existing persona JSON re-expressed as readable prose), both written to the project root. When present, all blog sub-skills auto-load these files before writing or reviewing. Pairs with blog-persona, which manages the structured persona JSON. Use when user says "blog brand", "create brand context", "brand voice doc", "BRAND.md", "VOICE.md", "establish editorial brand", "brand guidelines for blog".

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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,' or 'optimize for Claude/Gemini.' 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.

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AI citation readiness audit ONLY (does not touch Google rankings, use blog-rewrite for combined Google+AI work). Use whenever the user wants their content to rank in ChatGPT, Perplexity, Claude, Gemini, or Google AI Overviews. AI citation optimization audit scoring blog posts for ChatGPT, Perplexity, and Google AI Overview citability. 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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Prospects companies across developer-focused tech verticals for developer marketing agency outreach. Use this skill whenever a user provides a vertical (e.g. AI Agentic, IAC, DevTools, Observability, DevOps, FinOps, AI/SDLC, AI Orchestration, or any other tech vertical) and wants a list of prospect companies to target. Also trigger when the user says "prospect for", "find companies in", "give me leads for", "build a prospect list", or provides a vertical name alongside funding stage and headcount filters. Always use this skill — never attempt prospecting without following this structured workflow. The skill finds exact-fit companies only (never adjacent), maps a real outreach signal and email pain point for each, sources every data point with a URL, and outputs a unified nine-column table: Company, URL, LinkedIn, Headcount, Signal, Signal Sources, Geography, Pain Point, Pain Point Sources.

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。