「ara」の検索結果

Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.

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。

Codified expertise for customs documentation, tariff classification, duty optimization, restricted party screening, and regulatory compliance across multiple jurisdictions. Informed by trade compliance specialists with 15+ years experience. Includes HS classification logic, Incoterms application, FTA utilization, and penalty mitigation. Use when handling customs clearance, tariff classification, trade compliance, import/export documentation, or duty optimization.

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 articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility 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。

Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold. Includes adversarial review gate, dependency graph, parallel step detection, anti-pattern catalog, and plan mutation protocol. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex multi-PR task, or describes work that needs multiple sessions. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, or user says "just do it".

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。

Cache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation.

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。

Master Bevy's Entity Component System (ECS) in Rust, covering Systems, Queries, Resources, and parallel scheduling.

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。

Amplify safe or boring designs to make them more visually interesting and stimulating. Increases impact while maintaining usability. Use when the user says the design looks bland, generic, too safe, lacks personality, or wants more visual impact and character.

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。

Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.

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。

AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g

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 articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility 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。

Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).

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 comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and best practices

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。

You are an API mocking expert specializing in realistic mock services for development, testing, and demos. Design mocks that simulate real API behavior and enable parallel development.

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。

Estratega de Inteligencia de Dominio de Andru.ia. Analiza el nicho específico de un proyecto para inyectar conocimientos, regulaciones y estándares únicos del sector. Actívalo tras definir el nicho.

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。

Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.

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 when building analytics dashboards with nuxt-ui-tools as a package consumer. Covers schema and view functions (defineDashboardSchema, defineDashboardView) that take their context as typed params, useDashboard with a getter that rebuilds on input change, typed injection (useDashboardView, injectDashboard, InferDashboard, InferDashboardView), filters declared inline with the f builder and synced with the URL, memory, or an external store (one value per key across views), headless filters, filter controls and the shipped UI (UiDashboardPage, UiDashboardFilters, UiDashboardFilter, UiDashboardViewTabs) with slot overrides, remote paginated pickers and remoteTableOptions, staged queries (essential / background / deferred) with select and dependent requires, lazy enabled conditions on views, queries, and filters (blocks of disabled queries hidden, grids closing up), derived values, views (tabs), query-scoped filters, number formats (presets, Intl options, useDashboardFormat), and the dashboard blocks (stats with trends, goals, and comparisons, stat groups, gauges, unovis charts with highlights, value labels, and totals, lists with progress rings, bars, funnel, alerts, activity feeds, sortable tables, custom widgets) with their automatic loading, error, empty, and refresh states, card menus, header and footer actions, row actions, drill-down and selected state, card tabs, split cards, comparison periods, auto-refresh, and data freshness.

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。

Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

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。

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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 complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

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。

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

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。

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

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 wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.

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 when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.

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。

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

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。