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Firm-matter matching, matter instruction drafting, firm onboarding checklist, and instruction audit for in-house legal ops teams. Match a new matter to the right panel firm by practice area, jurisdiction, complexity, and cost tier. Produce a structured matter instruction with scope, timeline, budget, staffing, and reporting requirements. Generate an onboarding checklist covering conflict clearance, engagement letter, OCG acknowledgment, e-billing setup, and platform access. Audit an existing instruction for completeness and produce a remediation note. Trigger on: 'which firm should handle this', 'instruct the firm', 'write the instruction', 'matter instruction template', 'onboard the firm', 'set up the matter', 'conflict check', 'e-billing setup', 'is our instruction complete', 'review our instruction', 'instruction gap', 'allocate this matter'.

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。

Produces properly-formatted legal documents in Word (.docx). The user provides the substantive content (instructions, attached files, project knowledge); the skill handles document architecture and formatting. * No precedent or template required * Seven document families: agreements, corporate documents, litigation, memos, employment, policies, correspondence * Jurisdiction-agnostic — works across any legal system the user specifies * Multi-Chain numbering, defined-terms convention, structured recitals, atomic signature blocks * Also reformats existing .docx to a consistent house style * Works in Claude.ai, Cowork, and Claude for Word

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。

Elite incident response and legal compliance guidance for data breaches under GDPR Articles 33 & 34. Use when: (1) User reports a data breach or security incident — including "is this even a personal data breach?" triage, (2) User asks about breach notification obligations or deadlines, (3) User mentions "72 hours", Art. 33, Art. 34, or notification requirements, (4) Discussion involves security incidents affecting personal data, (5) User needs breach risk assessment using ENISA methodology, (6) User mentions "Data Breach" or "Incident" or "Data Leakage" or "Ransomware" or "Exfiltration", (7) User needs to determine Controller vs Processor obligations, (8) Cross-border breach scenarios requiring Lead SA determination, (9) User needs a mitigation playbook or immediate response recommendations, (10) User needs audit-ready breach documentation (.docx) or an EDPB-template-aligned breach notification / evidence file — including follow-up and withdrawal notifications, (11) Breach involves an AI system requiring AI Act Art. 73 serious incident screening.

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 Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-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。

Guide for drafting cookie policies compliant with GDPR and the ePrivacy Directive. Includes CNIL 2020 recommendations, a reference template, and best practices. Use when drafting or revising a cookie policy for a website or application.

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。

Collaboration platform configuration methodology for legal matter sites. Site architecture, workflow identification, dashboard design, data quality governance, and user adoption for SharePoint, Teams, and equivalent platforms. M365 is the reference implementation — outputs are platform-agnostic enough to brief IT or build simple automations without becoming a Power Automate manual. Use when setting up a matter site, identifying workflows to automate, designing reporting dashboards, managing platform data quality, or driving user adoption. Trigger on: 'set up the matter site', 'configure SharePoint', 'build a dashboard', 'what should we automate', 'brief IT on this workflow', 'nobody is using the platform', 'data quality is poor', 'set up Teams channel', 'matter site structure', 'alerts and notifications', 'user training', 'platform governance', 'status dashboard', 'what workflows can we automate', 'matter site template'.

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。

Classify a console screen (list/record/form), build it on @temps-sdk/ds templates and the shared shadcn primitives in @temps-sdk/ui, and follow the console's status vocabulary and status colour rules. Use for any new or changed console UI in web/src, and when building or extending web/packages/ds itself.

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。

Turn a photo of handwriting into a real installable font (TTF/WOFF/WOFF2). Use when the user shares a photo of handwritten letters and wants a font, asks to "make my font", "handwriting font", "turn my handwriting into a font", "font from this photo/image", asks for a handwriting font template, or wants to refine a font made earlier (smoother, thicker, fix a letter). Not for choosing, recommending, or identifying existing fonts - only for creating a font from the user's own handwriting.

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。

Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.

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。

Strategic SEO planning for new or existing websites. Industry-specific templates, competitive analysis, content strategy, and implementation roadmap. Use when user says "SEO plan", "SEO strategy", "SEO planning", "SEO best practices for <topic>", "content strategy", "keyword strategy", "content calendar", "site architecture", "SEO roadmap", or asks for organic growth guidance without a specific URL.

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。

Analyze existing XML sitemaps or generate new ones with industry templates. Validates format, URLs, and structure. Use when user says "sitemap", "generate sitemap", "sitemap issues", or "XML sitemap".

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。

Guides you through creating well-structured Claude Code skills with proper modularization, templates, and best practices. Use when creating new skills or improving existing ones.

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 test templates for unit tests, integration tests, and UI tests using Swift Testing and XCTest. Use when adding tests to iOS/macOS apps.

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 Custom Product Page metadata templates with screenshot strategies, keyword targeting, and audience-specific messaging for up to 35 page variants. Use when creating targeted App Store pages for different audiences, ad campaigns, or traffic 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。

Generates pre-order setup guides and launch timeline templates for App Store pre-orders. Use when planning a pre-order strategy for a new app launch or major update.

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 In-App Event metadata templates for App Store Connect — event names, descriptions, badge types, image specs, and deep link configuration. Use when creating events for App Store visibility, engagement campaigns, or seasonal promotions.

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。

Write professional, empathetic responses to App Store reviews that build trust and turn critics into fans. Use when responding to negative reviews, drafting templates for common review types, or improving review management 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。

Platform-agnostic skills including skill creation templates and best practices. Use when creating new skills or improving existing ones.

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 create sales collateral, pitch decks, one-pagers, objection handling docs, or demo scripts. Also use when the user mentions 'sales deck,' 'pitch deck,' 'one-pager,' 'leave-behind,' 'objection handling,' 'ROI calculator,' 'demo script,' 'talk track,' 'sales playbook,' 'proposal template,' or 'buyer persona card.' For competitor battle cards and comparison pages, see competitor-alternatives. For marketing website copy, see copywriting. For cold outreach emails, see cold-email.

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。

Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).

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 knowledge for Azure Resource Manager development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when authoring Bicep/ARM templates, using CLI/SDK/REST, deploying stacks/registries, or securing with Key Vault/RBAC, and other Azure Resource Manager related development tasks. Not for Azure Policy (use azure-policy), Azure Resource Graph (use azure-resource-graph), Azure Portal (use azure-portal), Azure Role-based access control (use azure-rbac).

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 knowledge for Azure Notification Hubs development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when integrating FCM/APNS/WNS, targeting devices/users, using tags/templates, IaC config, or Private Link, and other Azure Notification Hubs related development tasks. Not for Azure Event Hubs (use azure-event-hubs), Azure Service Bus (use azure-service-bus), Azure Web PubSub (use azure-web-pubsub).

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 knowledge for Azure Lab Services development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring lab plans, VM templates/schedules, VNet-integrated labs, GPU/nested virtualization, or Canvas/Teams integration, and other Azure Lab Services related development tasks. Not for Azure DevTest Labs (use azure-devtest-labs), Azure Virtual Machines (use azure-virtual-machines), Azure Virtual Desktop (use azure-virtual-desktop).

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 knowledge for Azure IoT Central development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing device templates, data exports, IoT Edge gateways, REST/CLI automation, or IoT Central migrations, and other Azure IoT Central related development tasks. Not for Azure IoT Hub (use azure-iot-hub), Azure IoT (use azure-iot), Azure IoT Edge (use azure-iot-edge), Azure IoT Operations (use azure-iot-operations).

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。