「structure」の検索結果

Use when designing organizational structure (team topologies, Conway's Law alignment), mapping stakeholders by power-interest for change initiatives, defining team interface contracts (APIs, SLAs, decision rights, handoffs), assessing capability maturity (DORA, CMMC, agile maturity models), planning org restructures (functional to product teams, platform teams, shared services), or when user mentions "org design", "team structure", "stakeholder map", "team interfaces", "capability maturity", "Conway's Law", or "RACI".

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Restructure existing code without changing its behavior, through a test-gated refactoring loop: a named target, a green suite over that target before any edit, a planned sequence of small named refactorings, and the full suite re-run after every step. Use when the user wants to refactor, restructure, clean up, simplify, or improve the design of existing code, or to apply refactoring recommendations from a code-review or architectural-analysis report. This skill changes code; it does not review code (use code-review), assess architecture (use architectural-analysis), plan a multi-module restructure before executing it (use plan-a-change), map domain boundaries before restructuring along them (use ddd-analysis), or build new behavior test-first (use tdd). Do not use it on code inside an active tdd loop; the refactor step of tdd owns that cleanup. Runs its planned sequence to completion without pausing for review; to review each named refactoring as it lands, use pairing.

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Creates visual representations of system structure including architecture diagrams, data flow diagrams, deployment diagrams, and sequence diagrams. Use when Claude needs to visualize system components, infrastructure, data flows, or interactions. Supports Mermaid (recommended for Markdown/GitHub), PlantUML (for detailed UML), and Graphviz/DOT (for complex networks). Trigger when users request diagrams, visualizations, or ask to "show", "diagram", "visualize", or "map out" system architecture, infrastructure, data flows, or component interactions.

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Reorganizes markdown documents into well-structured, consistent format while preserving content and improving readability. Use when Claude needs to: (1) Fix heading hierarchy issues (skipped levels, multiple h1s), (2) Generate or update table of contents, (3) Standardize formatting (lists, code blocks, emphasis, links), (4) Improve grammar and spelling, (5) Add missing standard sections (installation, usage, etc.), (6) Remove redundant or duplicate content, (7) Restructure technical docs, READMEs, or long-form content for better organization and flow.

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Define, deploy, and manage cloud infrastructure as code using tools like Terraform, Pulumi, CloudFormation, and CDK, ensuring consistency, repeatability, and version control. Use when the user requests infrastructure as code or provides relevant inputs for this workflow.

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Access and analyze AlphaFold protein structure predictions. Use when predicted structures are needed for proteins without experimental structures, or for confidence scores (pLDDT).

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Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.

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ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-qc.

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Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.

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AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.

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Facilitate a structured conversation to define requirement standards for a project — epic and feature definitions, scenario structure, AC format, priority notation, status workflow, and naming conventions. Produces a formal requirement-standards.md that the requirement-quality atom reads via config resolution, customising its embedded defaults for the team's product process. Use when setting up a new project, defining product standards, or when the user says 'set up requirement standards', 'define feature standards', 'configure requirement forge', 'define how features should be structured', or 'requirement forge refiner'.

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Drift is the divergence between declared infrastructure and live infrastructure.

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Connect user journeys to screens, defining the UI structure and navigation paths during PRD v0.4 User Journeys. Triggers on requests to define screens, design screen flows, map UI structure, plan navigation, or when user asks "what screens do we need?", "define screens", "screen flow", "UI structure", "information architecture", "navigation design", "wireframe planning". Consumes UJ- (User Journey Mapping), FEA- (Feature Value Planning), BR- (constraints). Outputs SCR- entries for screens and DES- entries for design system elements. Feeds v0.5 Red Team Review.

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Build cloud backend applications with Encore — type-safe backend framework with built-in infrastructure. Use when someone asks to "build a backend", "Encore", "type-safe API framework", "backend with built-in infra", "auto-provision cloud resources", or "backend framework with databases built-in". Covers API definition, databases, pub/sub, cron, and auto-provisioned infrastructure.

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Extract structured data from any document format using unified document processing. Use when a user asks to extract data from a document, parse a PDF, pull structured data from files, convert documents to JSON or CSV, extract fields from invoices or forms, or scrape data from documents.

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Crossplane for infrastructure as code using Kubernetes CRDs. Use when the user needs to provision and manage cloud resources declaratively through Kubernetes APIs, compose custom infrastructure abstractions, or build internal platforms.

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World-class application logging - structured logs, correlation IDs, log aggregation, and the battle scars from debugging production without proper logsUse when "log, logging, logger, debug, trace, audit, structured log, correlation id, request id, log level, winston, pino, bunyan, log4j, logging, observability, debugging, monitoring, tracing, structured-logs, correlation, aggregation" mentioned.

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Infrastructure and platform specialist for Kubernetes, Terraform, GitOps, and cloud-native architectureUse when "kubernetes, k8s, terraform, infrastructure, deployment, helm, argocd, gitops, service mesh, istio, cloud platform, kubernetes, terraform, gitops, argocd, helm, istio, aws, gcp, azure, infrastructure, platform, devops, ml-memory" mentioned.

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Comprehensive patterns for AI-powered document understanding including PDF parsing, OCR, invoice/receipt extraction, table extraction, multimodal RAG with vision models, and structured data output. Use when "document parsing, PDF extraction, OCR, invoice processing, receipt extraction, document understanding, LlamaParse, Unstructured, vision document, table extraction, structured output from PDF, " mentioned.

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World-class DevOps engineering - cloud architecture, CI/CD pipelines, infrastructure as code, and the battle scars from keeping production running at 3amUse when "devops, infrastructure, deployment, ci/cd, docker, kubernetes, aws, gcp, azure, terraform, cloudflare, vercel, monitoring, alerting, pipeline, container, scaling, downtime, incident, sre, devops, infrastructure, cloud, ci-cd, monitoring, reliability, sre, containers" mentioned.

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侍酒师与葡萄酒 (侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。) Master OS — automated mastery of 侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「侍酒师」「sommelier」「葡萄酒」「wine」「盲品」

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When the user wants to add, fix, or optimize schema markup and structured data on their site. Also use when the user mentions "schema markup," "structured data," "JSON-LD," "rich snippets," "schema.org," "FAQ schema," "product schema," "review schema," "breadcrumb schema," "Google rich results," "knowledge panel," "star ratings in search," or "add structured data." Use this whenever someone wants their pages to show enhanced results in Google. For broader SEO issues, see seo-audit. For AI search optimization, see ai-seo.

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Generates a fully structured SEO content brief for a target keyword and optionally pushes it to a Notion database. Use this skill whenever the user says 'create a content brief', 'brief this keyword', 'run a content brief for', 'generate a brief', 'write a brief for [keyword]', 'content brief on [topic]', or any variation where someone needs a keyword researched and turned into a structured writing assignment with H1, H2 outline, FAQ, internal links, word count target, and writer notes. Also triggers when the user provides a keyword and asks for an SEO brief, editorial brief, or writing spec. Outputs a mandatory structured format that the text parser maps directly to Notion properties. Supports single-keyword and batch (CSV) modes.

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Generates a fully structured SEO content **outline** (not a finished brief) and exports it as a formatted .docx Word document. The output is a skeleton for a writer to fill in — section headings, topic prompts, angles — not pre-written paragraphs. Use this skill whenever a user provides a blog title, focus keyword, domain URL, or any combination of those and asks to generate an outline, brief, blog brief, SEO brief, article structure, or content plan. Also trigger when the user says "create a brief for X", "generate an outline for [topic]", "make a content brief", "I need a brief for [URL or keyword]", or pastes a title and asks what the structure should look like. This skill handles input validation, domain analysis, keyword enrichment, audience inference, archetype-aware section selection, and full .docx generation — always use it rather than writing ad-hoc outlines.

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。