1.5万 件 ・ 関連度順
概要と使いどころ
Discover and choose Cloudflare products for apps, APIs, AI agents, storage, networking, and security. Use for architecture and product selection, including when the user describes a need without naming a Cloudflare product; then find the relevant skill or documentation.
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。
Discover, configure, and connect to Arduino boards from MATLAB using the Arduino Support Package. Use this skill when the user wants to set up an Arduino board, connect to Arduino hardware, find connected boards, scan serial ports, configure Arduino libraries, or any task that requires an Arduino connection as a prerequisite (e.g., blink LED, read sensor, read digital pin, read analog pin, write pin, plot sensor data, scan I2C, use ultrasonic sensor, control servo, read temperature, read voltage, data logging from hardware). Triggers on: arduino, board setup, COM port, serial port, hardware connection, Arduino Nano, Arduino Uno, Arduino Mega, Arduino Micro, Grove, sensor setup, I2C scan, ultrasonic, servo, readDigitalPin, writeDigitalPin, readVoltage, writePWMVoltage, temperature sensor, air pressure, pin read, pin write, analog input.
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 this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced 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。
Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.
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。
E-commerce warehouse and inventory optimization advisor. Analyzes inventory health, calculates safety stock and reorder points, performs ABC analysis, evaluates fulfillment costs, and provides actionable recommendations for improving efficiency. Supports all major fulfillment models: Self-fulfillment, Amazon FBA/FBM, Walmart WFS, 3PL, Shopify Fulfillment, TikTok Shop, Dropshipping, and Hybrid setups. No API key required. Use when: (1) reducing stockouts or overstock, (2) calculating safety stock levels, (3) optimizing warehouse costs, (4) improving Amazon IPI score, (5) analyzing inventory KPIs.
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 and deploy edge functions with Cloudflare Workers and Wrangler. Use for APIs, cron jobs, and edge middleware.
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。
Hit the Cloudflare REST API directly for operations that wrangler and MCP can't handle well. Bulk DNS, custom hostnames, email routing, cache purge, WAF rules, redirect rules, zone settings, Worker routes, D1 cross-database queries, R2 bulk operations, KV bulk read/write, Vectorize queries, Queues, and fleet-wide resource audits. Produces curl commands or scripts. Triggers: 'cloudflare api', 'bulk dns', 'custom hostname', 'email routing', 'cache purge', 'waf rule', 'd1 query', 'r2 bucket', 'kv bulk', 'vectorize query', 'audit resources', 'fleet operation'.
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 a full-stack TanStack Start app on Cloudflare Workers from scratch — SSR, file-based routing, server functions, D1+Drizzle, better-auth, Tailwind v4+shadcn/ui. Use whenever the user mentions TanStack Start, asks to scaffold a full-stack Cloudflare app with SSR, wants an SSR dashboard, or asks for a React 19 + Cloudflare Workers app with file-based routing and server functions — even if they don't name TanStack Start specifically. No template repo — Claude generates every file fresh per project.
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。
Scaffold and deploy Cloudflare Workers with Hono routing, Vite plugin, and Static Assets. Describe project, scaffold structure, configure bindings, deploy. Use whenever the user wants to create a Worker project, set up Hono on Cloudflare, configure D1 / R2 / KV / Queues bindings, or troubleshoot Worker export syntax, API route conflicts, HMR issues, or deployment failures.
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。
Compare UX patterns across multiple reference apps using pattern libraries produced by ux-extract. Reads 2+ pattern-library.md files, walks them category by category, identifies where apps converge (strong signal), where they diverge (genuine design choice), what's unique to one app, and what's absent across the set. Produces an opinionated comparison document with recommendations for a new build. No browser needed — pure markdown analysis. Trigger with 'compare UX patterns', 'how do top apps handle X', 'ux comparison', 'pattern comparison across reference 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。
When the user wants to optimize brand search for a company with multiple domains (e.g. parent company.com vs product.ai). Ensure the parent/company domain ranks first for brand queries. Also use when the user mentions "brand search," "multi-domain SEO," "company domain first," "parent vs product domain," "hub-spoke domain," "brand SERP control," or "differentiate company and product domains." For domain structure, use domain-architecture.
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。
Deprecated compatibility entry for Social Share construction. This component is now maintained in Bricks; install and use component-builder to build a new Social Share.
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。
Deprecated compatibility entry for Careers Page construction. This page is now maintained in Pagina; install and use page-builder to build a new Careers Page.
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。
Assemble the final `.ppmplugin` binary bundle for a PAM native extension and verify its contents. First **reconciles** the manifest's declared `entrypoints` against the binaries actually staged — if the manifest declares a platform with no built binary it gates (build it / ship without it / stop) rather than shipping a broken bundle. Then re-runs the plugin's upload-compatibility checks on the reconciled manifest, zips the manifest plus whichever of `android/<PascalName>Plugin.dex` and `ios/<PascalName>Plugin.framework/` are present into `ppmplugin/<name>.ppmplugin`, and verifies the archive layout with `jar tf` (exactly the manifest + the shipped binaries — nothing missing, nothing extra). Output: a statically verified `.ppmplugin` file on disk. Prereqs: `jar` (JDK). Run after /generate-ppmplugin-manifest and the build skill(s).
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。
Capture a human-approved UI design lesson as a privacy-minimized local StyleSeed candidate, review it, and prepare an opt-in share package without transmitting project code, prompts, screenshots, or brand data. Use when a person asks StyleSeed to remember, learn from, generalize, review, or prepare a reusable rule from an accepted design correction.
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 CIS Controls v8 (CIS Top 18) advisor — implementation group scoping (IG1/IG2/IG3), control gap assessments, safeguard-level guidance, asset inventory, software inventory, data protection, secure configuration, account management, access control, continuous vulnerability management, audit log management, email and web browser protections, malware defenses, network infrastructure management, network monitoring and defense, application software security, incident response, penetration testing, and CIS Controls mapping to NIST CSF, ISO 27001, SOC 2, and CMMC. Use for any question about CIS Controls, CIS Benchmarks, Implementation Groups, or prioritized cyber hygiene for any organization size.
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。
This skill should be used when the user asks to "optimize Snowflake queries", "analyze Snowflake SQL performance", "size Snowflake warehouses", "review Snowflake data models", or "troubleshoot Snowflake cost 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。
Calculate share of voice by script vs competitors in search, social and AI. "what's our share of voice"
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。
Performance scorecard design, post-matter feedback collection, QBR preparation, and firm comparison for in-house legal ops teams evaluating outside counsel. Design a scorecard framework calibrated to your team's maturity level. Produce post-matter feedback forms for supervising attorneys to complete at matter close. Prepare a QBR pack with agenda, data summary, and talking points for structured business reviews. Produce a side-by-side firm comparison for panel decision-making. Trigger on: 'design a performance scorecard', 'how do we evaluate our firms', 'build us a scorecard', 'post-matter feedback form', 'feedback at matter close', 'collect feedback on the firm', 'QBR', 'quarterly business review', 'prep for the firm review', 'what should I raise with the partner', 'compare the firms', 'which firm is performing better', 'rank our panel', 'firm performance data', 'scorecard framework', 'evaluate outside counsel', 'annual firm review', 'how are our firms performing'.
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。
Builds a high-fidelity interactive legal assessment as a single self-contained HTML artifact. Output includes a live countdown timer, contract review tasks with hover-annotated problem clauses, candidate answer textareas, model answers hidden behind reveal blocks, scenario-based legal memo tasks, strategy and function-building questions, and a pre-submission checklist that encodes the marking criteria. Use when the user needs to (1) assess a legal candidate with a realistic timed exercise, (2) train or onboard junior lawyers using problem sets rather than doctrine, (3) help a candidate prepare for a real take-home assessment they are facing, (4) build educational materials for law students, in-house teams, or compliance training, or (5) produce scenario-based training modules on specific legal topics. Triggers on "legal test", "take-home", "mock exam", "contract redline exercise", "candidate assessment", "legal training exercise", "practice test", or similar phrasing even when informal.
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。
Assesses which of the Art. 50(1)-(5) transparency obligations of the EU AI Act apply to a given AI system's provider or deployer, grounded in the final Code of Practice on Transparency of AI-Generated Content (June 2026) and the Commission's draft Art. 50 Guidelines. Covers AI-chatbot disclosure, deepfake and synthetic-content marking/watermarking, emotion-recognition and biometric-categorisation notices, the machine-readable marking duty, the obviousness exceptions, and the implementation timeline. Outputs a formal mini-report plus a per-obligation compliance checklist with gap flags. For breadth-first tier triage use the EU AI Act System Classifier; for raw Art. 50 text and Q&A use the EU AI Act Knowledge Base; for the full role x tier matrix use the EU AI Act Obligations Mapper.
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 CIS Controls v8 (CIS Top 18) advisor — implementation group scoping (IG1/IG2/IG3), control gap assessments, safeguard-level guidance, asset inventory, software inventory, data protection, secure configuration, account management, access control, continuous vulnerability management, audit log management, email and web browser protections, malware defenses, network infrastructure management, network monitoring and defense, application software security, incident response, penetration testing, and CIS Controls mapping to NIST CSF, ISO 27001, SOC 2, and CMMC. Use for any question about CIS Controls, CIS Benchmarks, Implementation Groups, or prioritized cyber hygiene for any organization size.
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。
Prepare management meeting materials and question lists for A-share investment due diligence. Adapted from the original dd-meeting-prep skill for Chinese business culture and management meeting conventions. Triggers on "A股尽调会议准备", "管理层会议准备", "management meeting China", "DD meeting prep", "管理層访谈准备", or "prepare for management meeting [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。