「eu」の検索結果

Price financial instruments in MATLAB using the Financial Instruments Toolbox. Route to the appropriate numerical method reference based on the user's request: Monte Carlo simulation (AssetMonteCarlo, IRMonteCarlo, RoughVolMonteCarlo), FFT / Numerical Integration (Vanilla European options), or Interest-Rate Trees (option-embedded bonds with IRTree). Use when the user asks to price financial instruments or compute Greeks using any of these methods. Use one of the following models depending on the pricing method and instrument: Black-Scholes, Bachelier, Heston, Bates, Merton, Hull-White, Black-Karasinski, Black-Derman-Toy, Cox-Ingersoll-Ross, Linear Gaussian 2 Factor (G2PP), Brace-Gatarek-Musiela (BGM), SABR-BGM, RoughBergomi, RoughHeston.

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。

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。

Cross-border e-commerce expansion advisor. Scores target markets on 8 weighted dimensions (market size, ecommerce penetration, competition, regulatory complexity, logistics infrastructure, payment ecosystem, cultural distance, IP protection), compares 5 fulfillment models with cost and transit data, provides country-by-country tax/duty compliance guides (EU VAT/IOSS, UK VAT, US sales tax, CA GST, AU GST, JP consumption tax), maps local payment preferences by market, and builds a phased expansion roadmap. No API key required.

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。

Configure Redis for caching and data storage. Set up clustering, persistence, and Sentinel. Use when implementing Redis caching or queues.

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。

Auto-scale LLM inference clusters on Kubernetes using KEDA, custom GPU metrics, and horizontal pod autoscaling. Handle traffic spikes, implement queue-based scaling, and optimize cost with spot instances for AI workloads.

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。

Implement GDPR data protection requirements. Configure consent management, data subject rights, and privacy by design. Use when processing EU personal 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。

Configure CircleCI workflows and orbs for continuous integration and deployment. Create config.yml pipelines, use orbs for reusable configurations, and optimize build performance. Use when working with CircleCI for CI/CD automation.

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。

Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs. Use when implementing distributed tracing, service-level troubleshooting, or vendor-neutral observability.

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, manage, and deploy Helm charts for Kubernetes package management. Build reusable chart templates, manage releases, configure values, and use Helm repositories. Use when packaging Kubernetes applications or managing K8s deployments with Helm.

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, standardize, validate, and sync locally maintained skills into agent skill directories with a `skills` CLI-aligned workflow. Use this skill when Codex needs to turn ad-hoc prompt or rules folders into reusable `SKILL.md`-based skills, install or sync one or more local skills from `./skills` into Claude, Cursor, CodeBuddy, Codex, or similar agent directories, or manage local skill path mappings and symlink or copy installation behavior.

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, improve, and evaluate reusable agent skills with high-quality SKILL.md files, precise trigger descriptions, progressive disclosure, and testable behavior. This skill should be used when users ask to create a new skill, rewrite or review an existing skill, audit a skill collection such as `config/source/skills` for redundancy or overlap, improve skill trigger quality, organize skill references, or evaluate whether a skill should trigger and behave correctly.

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。

CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers build-plan-apply flow (deployBuild local build → deployPlan dry-run → deployApply confirm=true), hosting build-output neutralization, envId resolution priority, only/skip filtering, concurrency, and continueOnError. Prefer deployBuild (when hosting declares a buildCommand) and deployPlan before deployApply; do not confuse with per-resource tcb CLI deploy or single-function deploy.

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。

Reusable git delivery workflows derived from local slash commands (commit, push, PR, release notes, GitHub Actions failure triage with worktree-based fixes, and isolating your own changes from a shared dirty workspace).

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 on a mailbox export to sort mail into needs reply, updates, promotional, sales and spam — which messages are addressed to the person at all. For a support queue use jev triage.

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 reusable skills from conversation context

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。

Browse, inspect, and pick reusable CAD parts with the Bits & Bolts plugin.

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。

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。

Exhaustively extract UX patterns from a reference web app. Walks every screen, captures screenshots of every state, records interaction patterns, copy verbatim, keyboard shortcuts, responsive treatments, motion, and empty/error/loading states. Produces a reusable pattern library that other audits can compare against. The inverse of ux-audit — asks 'what is the bar?' rather than 'does this match the bar?'. Trigger with 'learn from X', 'extract patterns from X', 'study X's UX', 'reverse engineer the UX of X', 'build a pattern library from X'.

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。

Deep research and discovery before building something new. Explores local projects for reusable code, researches competitors, reads forums and reviews, analyses plugin ecosystems, investigates technical options, and produces a comprehensive research brief. Three depths: focused (30 min), wide (1-2 hours), deep (3-6 hours). Triggers: 'research this', 'discovery', 'explore the space', 'what should I build', 'competitive analysis', 'before I start building', 'research before coding'. Not for cited fact-checking research reports (a separate harness does those); this is pre-build product discovery.

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。

Review changed code for reuse, quality, and efficiency, then fix any issues found. Also scans project files for size limit violations.

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。

Review changed code for reuse, quality, and efficiency, then fix any issues found. Also scans project files for size limit violations.

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。

Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals.

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。