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概要と使いどころ
Cross-platform keyword research for e-commerce. Discover high-converting keywords across Amazon, Shopify, Etsy, Google Shopping, TikTok Shop, and Walmart. Analyzes search volume signals, competition indicators, commercial intent, and long-tail opportunities.
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。
Trace relationships between semantic models and downstream reports across Fabric workspaces. Automatically invoke when the user asks to "find downstream reports", "show report lineage", "impact analysis", "what depends on this dataset", "cross-workspace lineage", "which reports are connected", "get model dependencies", or mentions model-to-report dependency tracing.
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 plan content marketing across channels, define content types and formats, or create a content repurposing strategy. Also use when the user mentions "content marketing strategy," "content types," "content formats," "content repurposing," "content calendar," "content mix," "owned content," "content distribution," "content funnel," or "content planning across channels." For SEO calendar, use content-strategy.
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 responsive, mobile-first layouts using fluid containers, flexible units, media queries, and touch-friendly design that works across all screen sizes. Use this skill when creating or modifying UI layouts, responsive grids, breakpoint styles, mobile navigation, or any interface that needs to adapt to different screen sizes. Apply when working with responsive CSS, media queries, viewport settings, flexbox/grid layouts, mobile-first styling, breakpoint definitions (mobile, tablet, desktop), touch target sizing, relative units (rem, em, %), image optimization for different screens, or testing layouts across multiple devices. Use for any task involving multi-device support, responsive design patterns, or adaptive layouts.
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。
Given a Japanese NLP GitHub repo/model/dataset (URL / owner/repo / tool name) OR a topic, find what's already in awesome-japanese-nlp-resources and discover related resources NOT yet listed (contribution candidates). Mines the bundled dataset, then expands via web research across GitHub and Hugging Face. Use when the user names a SPECIFIC repository, model, or tool and wants alternatives/equivalents, OR wants to discover Japanese NLP resources for a topic that are NOT yet in the list, OR wants to prepare a contribution. Trigger phrases include 'mecabに似たツール', 'fugashiの代替', 'alternatives to fugashi', 'repos like manga-ocr', 'what else is like sudachi', 'リストに無い新しい日本語NLP', 'awesome-japanese-nlpに追加できそうな', '最近公開された日本語NLPツール', 'find unlisted Japanese NLP repos', 'new Japanese models on Hugging Face', 'contribute a new resource'. For a simple lookup of what already exists, use the search skill instead.
Compare several Japanese NLP libraries, models, or datasets for a keyword (a specific tool name, or a function/task like '形態素解析') across a handful of criteria chosen for that comparison, rendered as a ○/△/✕ table. Use when the user wants a side-by-side comparison of multiple Japanese NLP tools/libraries/datasets, not just the single best one. Trigger phrases include 'X と Y と Z を比較して', '形態素解析ライブラリを比較', 'MeCab と Sudachi どっちがいい', 'どのツールを使うべき', 'compare japanese tokenizers', 'mecab vs sudachi vs janome', 'which embedding model should I use', '日本語NERライブラリの比較表', 'pros and cons of japanese OCR tools'. For a single ranked list use search; for alternatives to one specific tool (or contribution candidates) without a multi-axis table, use discover.
Inspects and configures the security headers a Power Pages site sends to browsers — Content Security Policy, frame and clickjacking protection, cross-origin sharing, cookie behavior, and related site settings. Identifies gaps and walks the user through fixes. Use when the user wants to review headers, fix CSP errors, allow embedding in another site, control cross-origin access, harden cookie settings, or asks "are my browser settings safe?", "fix my CSP", "set up CORS" — even if they only mention a specific header name without saying "security headers".
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。
Validate a third-party control repo across four automated layers plus one printed manual recipe. Layer 1 asserts native-source structure (Android getName() and iOS +moduleName to manifest nativeModule; @ReactMethod / RCT_EXPORT_METHOD to methods; no @ReactModule) plus load/init readiness (ReactPackage public no-arg constructor, iOS [cls new] no-arg init, requiresMainQueueSetup NO, non-throwing eager construction), so launch-time crashes surface before any build. Layer 2 validates the committed `./manifest.json` against the ppmplugin-format rules. Layer 3 asserts request/response/error-code agreement across native and PCF. Layer 4 compiles the PCF (auto-skipped if absent). Layer 5 prints a device end-to-end recipe. Native compile belongs to /build-android-binary and /build-ios-binary — this is the cheap structural pre-flight before those slow builds. Reports pass/fail per layer with a fix hint and updates .extension-state.md.
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 preparing an AgentSys release, checking cross-platform compatibility, or changing the installer, transforms, adapters or marketplace pins. Maintainer skill for this repo only.
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。
Adversarial "perfection" review — hold a change to an *ideal* bar, not just a correct one, assuming eternal time, unlimited energy, and no ship pressure. Use when the user asks to review "for perfection", to make a defect "impossible to express", or to hunt where a defect "relocates" across review rounds. Grounds every claim in the diff, fans out adversarial verifiers via Workflow, and reports residual surfaces with a structural fix for each. ONLY invoke when the user explicitly asks for a perfection / ideal-bar review.
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。
Triage an outage, loss of stakeholder trust or a failing engagement direction. Use for urgent recovery; distinguish diagnosis from authorized production mitigation.
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。
Expert India Digital Personal Data Protection Act, 2023 (DPDPA) compliance advisor. Use this skill whenever a user asks about the DPDPA, DPDP Act, DPDP Rules 2025, India data privacy law, Data Fiduciary obligations, Data Principal rights, Significant Data Fiduciary, Data Protection Board of India, consent under DPDPA, notice requirements, breach notification India, children's data India, cross-border data transfer India, India privacy compliance, DPDPA gap analysis, DPDPA vs GDPR, or any obligation under India's personal data protection framework. Also trigger for: "Section 6 consent", "Section 7 legitimate uses", "Section 9 children's data", "Section 10 SDF", "Section 16 cross-border", "Rule 6 breach notification", "Rule 13 SDF obligations", "Data Protection Board complaint", "verifiable parental consent India", "DPDPA compliance roadmap", or "India privacy law global company".
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。
Expert Vietnam Personal Data Protection Law (PDPL) compliance advisor for Law No. 91/2025/QH15 and implementing Decree 356/2025/ND-CP (effective January 1, 2026). Use this skill for gap analysis against the Vietnam PDPL, data subject rights fulfilment workflows, cross-border data transfer impact assessments, privacy notices and internal policies, breach notification procedures, sector-specific obligations (finance, AI, cloud, blockchain), and DPO qualification reviews. Trigger whenever a user mentions Vietnam data privacy, VN-PDPL, Nghị định 356, Vietnamese personal data, or cross-border transfers involving Vietnamese citizens' data.
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。
EU AI Act (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus, Reg. (EU) 2026/1744, in force July 27, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of August 2026. GPAI enforcement powers active since August 2, 2026.
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。
Cross-framework infrastructure security audit across cloud, network, and CI/CD. Use for infrastructure and cloud security audits, security posture assessment, and validating technical controls for SOC 2, ISO 27001, and NIST CSF.
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。
Design systems leadership across component libraries, design tokens, and design-dev collaboration. Use when architecting a token system, building a component library, defining governance, or generating cross-platform tokens.
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。
Convert authored markdown into a polished self-contained HTML document with a table of contents, numbered figures and tables, cross-references, footnotes, and print-ready CSS. Use when publishing a report, whitepaper, or memo.
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。
Cross-functional organizational health check scoring 8 dimensions on a traffic-light scale. Use when assessing company health, preparing for board reviews, identifying at-risk functions, or diagnosing cross-functional problems.
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。
Verify a BibTeX file for hallucinated or fabricated references by cross-checking every entry against CrossRef, arXiv, and DBLP. Reports each reference as verified, suspect, or not found, with field-level mismatch details (title, authors, year, DOI). Use when the user wants to check a .bib file for fake citations, validate references in a paper, or audit bibliography entries for accuracy.
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。
Expert Vietnam Personal Data Protection Law (PDPL) compliance advisor for Law No. 91/2025/QH15 and implementing Decree 356/2025/ND-CP (effective January 1, 2026). Use this skill for gap analysis against the Vietnam PDPL, data subject rights fulfilment workflows, cross-border data transfer impact assessments, privacy notices and internal policies, breach notification procedures, sector-specific obligations (finance, AI, cloud, blockchain), and DPO qualification reviews. Trigger whenever a user mentions Vietnam data privacy, VN-PDPL, Nghị định 356, Vietnamese personal data, or cross-border transfers involving Vietnamese citizens' data.
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。
Guide to analyze multiple documents (PDF, DOCX) against user-defined columns and produce a structured Excel output with citations. Use when the user wants to: (1) Extract specific information from multiple documents into a table, (2) Compare clauses or provisions across contracts, (3) Create a document review matrix with source citations. Triggers on: 'tabular review', 'document matrix', 'extract from documents', 'compare across documents', 'review multiple contracts'.
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。
Sanctions and export control analysis tool for Claude Desktop. Real-time individual screening across 30+ official lists (UN, EU, OFAC, OFSI, French DGT...), sectoral analysis, dual-use goods (EU Reg. 2021/821), US/China extraterritorial regimes (EAR, ITAR, FDPR, ECL), USD/SWIFT risk and jurisdictional mapping across 30+ countries. All results sourced and verified in real time — no memory-based answers. Responds in French, English, German, Spanish, Russian and Chinese. Updated 19 May 2026 (EU 20th package, UK SEUC, Cuba EO). Indicative guidance only — not legal advice.
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。
Génération de sujets d'entraînement complets pour l'épreuve de NOTE DE SYNTHÈSE (5 heures, coefficient 3) de l'examen d'accès au CRFPA : choix du sujet, dossier documentaire de 16 à 20 documents assemblé à partir de sources réelles vérifiées (textes, jurisprudence, travaux parlementaires, doctrine) avec contrôle du verbatim, puis grille de notation au modèle de la Commission nationale (résumé de chaque document, problématique, thèmes cotés sur 20, plan). Livraison en deux fichiers .docx distincts, le sujet et la grille. Si l'utilisateur n'impose pas de sujet, en chercher un dans l'actualité juridique. Mode aveugle pour l'étudiant qui compose sans rien voir du corrigé. Déclencher dès que l'utilisateur demande un sujet de note de synthèse, un dossier documentaire, une note de synthèse blanche, un entraînement à l'épreuve de 5 heures du CRFPA ou de l'IEJ, ou une grille de note de synthèse. NE PAS déclencher pour les épreuves de 3 heures (sujet-crfpa-toutes-matieres) ni pour la correction de copies.
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。
Turns a legal question, a set of draft questions, or source material such as a statute, jury instruction, rubric, contract checklist, or coding manual into a battery of narrow questions that TypeSafe's Jev, a fast classifier that returns probabilities rather than prose, can answer reliably across many documents. The skill breaks conclusions such as negligence, felony murder, or standing into element-level questions, writes the governing rule into each question, and delivers the questions alone, in Jev's request format or in plain English. Built for legal work that asks the same questions of many texts: coding statutes across jurisdictions, coding opinions or case files for empirical research, checking contracts against a review checklist, screening intake narratives, and grading student work against a rubric. Use when asked to write Jev questions, build a classifier for legal documents, or turn a legal test into yes-or-no questions. Running the questions requires Jev access; writing them does not.
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。