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概要と使いどころ
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
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。
Skill permission analysis, one-time authorization, analyze skill permissions, batch authorization
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 user asks to create/design a big screen (大屏), full-screen data visualization, or says "创建大屏", "生成大屏", "新建大屏", "设计大屏", "做一个大屏", "BI大屏", "数据大屏", "可视化大屏", "监控大屏", "create big screen", "design big screen", "BI visualization big screen". Also triggers when user describes big screen requirements like "做一个销售数据大屏" or mentions full-screen display like "展厅展示", "监控室大屏". Make sure to use this skill for big screens (大屏) — NOT dashboards (仪表盘/看板), which use a completely different layout and styling system.
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 user asks to create/edit Online graph charts, data visualization, or says "创建图表", "生成图表", "新建图表", "做一个图表", "online图表", "数据图表", "柱状图", "折线图", "饼图", "统计图", "可视化", "chart", "graph", "create chart", "generate chart", "bar chart", "line chart", "pie chart". Also triggers when user describes chart requirements like "做一个销售柱状图" or mentions data visualization like "用图表展示男女比例".
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。
Archive, export, and notarize macOS apps using xcodebuild and asc. Use when you need to prepare a macOS app for distribution outside the App Store with Developer ID signing and Apple notarization.
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。
Inspect and edit Xcode String Catalogs (.xcstrings) via bundled l10n.sh and find_unused_keys.sh scripts, and apply Apple localization conventions.
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。
Fetches AI news from smol.ai RSS and generates structured markdown with intelligent summarization and categorization. Optionally creates beautiful HTML webpages with Apple-style themes and shareable card images. Use when user asks about AI news, daily tech updates, or wants news organized by date or category.
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。
WordPress performance code review and optimization analysis. Use when reviewing WordPress PHP code for performance issues, auditing themes/plugins for scalability, optimizing WP_Query, analyzing caching strategies, checking code before launch, or detecting anti-patterns, or when user mentions "performance review", "optimization audit", "slow WordPress", "slow queries", "high-traffic", "scale WordPress", "code review", "timeout", "500 error", "out of memory", or "site won't load". Detects anti-patterns in database queries, hooks, object caching, AJAX, and template loading.
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 paid-ad landing pages for message match, mobile experience, performance, accessibility, trust, forms, consent, tracking, security, and conversion friction. Use for landing-page audit, post-click experience, LP audit, conversion-rate optimization, form optimization, ad-to-page message match, redirects, blocked navigation, or requests involving private, loopback, link-local, or metadata IP destinations.
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。
Automated sequencing quality control and advanced visualization wrapping FastQC, MultiQC, and custom chart generation. Exposes an MCP stdio server for live AI integration alongside a ClawBio CLI runner.
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。
Web performance audits with Core Web Vitals, bottleneck identification, optimization recommendations. Use for page load times, performance reviews, UX optimization, or encountering LCP, FID, CLS issues, resource blocking, render delays.
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。
SEO keyword clustering with topic organization, content hub architecture, internal linking strategies. Use for content strategy, keyword research, pillar page structures, or encountering cluster organization, hub architecture, internal linking errors.
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 recommendation systems with collaborative filtering, matrix factorization, hybrid approaches. Use for product recommendations, personalization, or encountering cold start, sparsity, quality evaluation issues.
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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, refine, compare, and when needed validate distinctive names for startups, AI products, developer tools, protocols, open-source projects, apps, product families, local businesses, services, companies, nonprofits, and other organizations. Use when asked to name or rename a business, brand, product, venture, shop, studio, or organization; derive a name from an existing project or concept; create a company and product naming system; compare candidates; or check company, domain, package, repository, marketplace, or trademark conflicts. Default to a fast no-browse creative pass; use current validation when the user requests availability or research, or is choosing a final name. Do not claim legal clearance, purchase domains, or present stale or unverified availability as fact.
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 expenses by category, department, and tax deductibility from transaction data. Use when the user requests expense categorization or provides relevant inputs for this workflow.
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。
Automatically organizes files in a directory into a clean structure based on configurable rules — by file type, date, project, or priority — with support for duplicate detection, naming conventions, and archival strategies. Use when the user requests file organization or provides relevant inputs for this workflow.
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。
Summarize text using extractive, abstractive, hierarchical, and multi-document techniques, producing concise outputs at configurable detail levels. Use when the user requests summarization or provides relevant inputs for this workflow.
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 compelling, persuasive marketing and sales copy using proven frameworks like AIDA, PAS, BAB, and 4Ps, with tone and voice customization, CTA optimization, and A/B variant generation. Use when the user requests copywriting or provides relevant inputs for this workflow.
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。
Optimize a webpage for search engines by analyzing on-page elements, improving technical performance, and implementing structured data for maximum visibility. Use when the user requests seo optimization or provides relevant inputs for this workflow.
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 clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly. Use when the user requests data visualization or provides relevant inputs for this workflow.
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 user asks to translate videos, dub video content, or localize videos into other languages. Translate videos with AI-powered dubbing using iFlytek (Xfei) Video Translation API. Supports video language translation, voice dubbing, multilingual video localization, 视频翻译, 视频配音, 视频本地化. Features task creation, task list, task details retrieval. Ideal for content creators and localization teams.
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。
Think and work like an expert Geodesist. Use when a task calls for Geodesist judgment. Reasons from coordinates as four-dimensional objects with epoch, velocity, and frame realization (ITRS vs. ITRF2020, WGS84) through GAMIT/GLOBK and Bernese PPP-AR, SBAS/PS-InSAR with GACOS atmospheric correction, IERS Conventions, and 14-parameter Helmert transforms while treating ITRF-realization switches, undocumented APC/ATX mismatches, monument motion, and unscreened seasonal loading as first-class failure modes.
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。
Think and work like an expert Industrial Microbiologist. Use when a task calls for Industrial Microbiologist judgment. Reasons from SmF/SSF physiology, fed-batch μ/OTR–OUR/RQ, DoE media optimization, PAT (Raman soft sensors), ICH Q8/Q7 characterization, bioleaching, SVI/F/M filament ID, and phage (10⁴–10⁶ PFU/mL) plant hygiene; treats antifoam kLa penalty, F₀ CIP/SIP cold spots, DSP mass balance, and golden-batch vs biofilm red herrings as first-class failure modes.
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。