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「designing」の検索結果

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概要と使いどころ

Full-stack backend architecture and frontend-backend integration guide. TRIGGER when: building a full-stack app, creating REST API with frontend, scaffolding backend service, building todo app, building CRUD app, building real-time app, building chat app, Express + React, Next.js API, Node.js backend, Python backend, Go backend, designing service layers, implementing error handling, managing config/auth, setting up API clients, implementing auth flows, handling file uploads, adding real-time features (SSE/WebSocket), hardening for production. DO NOT TRIGGER when: pure frontend UI work, pure CSS/styling, database schema only.

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。

vibeeval/vibecosystem5332026年8月9日 更新

Builds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.

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。

ancoleman/ai-design-components5252025年12月11日 更新

Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.

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。

ancoleman/ai-design-components5252025年12月11日 更新

Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.

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。

ancoleman/ai-design-components5252025年12月11日 更新

Builds form components and data collection interfaces including contact forms, registration flows, checkout processes, surveys, and settings pages. Includes 50+ input types, validation strategies, accessibility patterns (WCAG 2.1), multi-step wizards, and UX best practices. Provides decision trees from data type to component selection, validation timing guidance, and error handling patterns. Use when creating forms, collecting user input, building surveys, implementing validation, designing multi-step workflows, or ensuring form accessibility.

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。

ancoleman/ai-design-components5252025年12月11日 更新

Builds tables and data grids for displaying tabular information, from simple HTML tables to complex enterprise data grids. Use when creating tables, implementing sorting/filtering/pagination, handling large datasets (10-1M+ rows), building spreadsheet-like interfaces, or designing data-heavy components. Provides performance optimization strategies, accessibility patterns (WCAG/ARIA), responsive designs, and library recommendations (TanStack Table, AG Grid).

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。

ancoleman/ai-design-components5252025年12月11日 更新

Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.

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。

ancoleman/ai-design-components5252025年12月11日 更新

Guide incident response from detection to post-mortem using SRE principles, severity classification, on-call management, blameless culture, and communication protocols. Use when setting up incident processes, designing escalation policies, or conducting post-mortems.

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。

ancoleman/ai-design-components5252025年12月11日 更新

Selecting and implementing AWS services and architectural patterns. Use when designing AWS cloud architectures, choosing compute/storage/database services, implementing serverless or container patterns, or applying AWS Well-Architected Framework principles.

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。

ancoleman/ai-design-components5252025年12月11日 更新

Designs layout systems and responsive interfaces including grid systems, flexbox patterns, sidebar layouts, and responsive breakpoints. Use when structuring app layouts, building responsive designs, or creating complex page structures.

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。

ancoleman/ai-design-components5252025年12月11日 更新

Design production-ready SDKs with retry logic, error handling, pagination, and multi-language support. Use when building client libraries for APIs or creating developer-facing SDK interfaces.

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。

ancoleman/ai-design-components5252025年12月11日 更新

Design comprehensive security architectures using defense-in-depth, zero trust principles, threat modeling (STRIDE, PASTA), and control frameworks (NIST CSF, CIS Controls, ISO 27001). Use when designing security for new systems, auditing existing architectures, or establishing security governance programs.

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。

ancoleman/ai-design-components5252025年12月11日 更新

Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

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。

mxyhi/ok-skills4962026年10月9日 更新

ALWAYS USE when asked to migrate, replace, or make testable existing C# static calls with a named wrapper or built-in abstraction: DateTime.UtcNow/Now or DateTimeOffset.UtcNow to TimeProvider/IClock, File.* to IFileSystem or an existing store such as ITextFileStore, and Environment.* to an existing reader such as IEnvironmentReader. Covers scoped files/projects, constructor injection, replacing temp-file or process-environment tests with fakes, "already registered" abstractions, and static classes whose callers/signatures must stay unchanged. Preserves DateTimeKind and call count. DO NOT USE for finding statics (detect-static-dependencies), choosing/designing a new wrapper (generate-testability-wrappers), behavior tests with no chosen seam (testability-obstacle), or test-framework migration.

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。

managedcode/dotnet-skills4862026年10月14日 更新

mvvm

無料

Implement the Model-View-ViewModel pattern in .NET applications with proper separation of concerns, data binding, commands, and testable ViewModels using MVVM Toolkit. USE FOR: implementing UI separation with Model-View-ViewModel; using MVVM Toolkit (CommunityToolkit.Mvvm) for ViewModels; designing testable UI architecture. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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。

managedcode/dotnet-skills4862026年10月14日 更新

blazor

無料

Build and review Blazor applications across server, WebAssembly, web app, and hybrid scenarios with correct component design, state flow, rendering, and hosting choices. USE FOR: building interactive web UIs with C# instead of JavaScript; choosing between Server, WebAssembly, or Auto render modes; designing component hierarchies and state. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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。

managedcode/dotnet-skills4862026年10月14日 更新

Plan complex Blazor UI features by decomposing them into focused components. USE FOR: building a complex Blazor page with multiple sections, planning component decomposition, designing a multi-section dashboard or layout, breaking down a large UI feature into composable components, pages with sidebars and content panels, any page with 3+ distinct visual sections or multiple interacting sub-features, identifying parent-child relationships and data flow. DO NOT USE FOR: creating new Blazor projects or apps from scratch (use create-blazor-project), implementing a single individual component (use author-component), writing component code with parameters and EventCallback (use author-component), or simple single-component pages.

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。

managedcode/dotnet-skills4862026年10月14日 更新

Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best practices to data output.

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。

nimrodfisher/data-analytics-skills4752026年9月25日 更新

Document column-level mappings between source and target schemas. Use when integrating data from multiple systems, designing ETL transformations, or documenting how raw fields become analytical assets.

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。

nimrodfisher/data-analytics-skills4752026年9月25日 更新

Use when reviewing, designing, or modifying Java enterprise systems that may support intermediary services, hosting services, online platforms, marketplaces, content moderation, recommender systems, advertising delivery, complaint workflows, transparency reporting, or systemic-risk evidence under the EU Digital Services Act. This should trigger for requests such as Review a Java online platform for DSA controls; Design notice-and-action or appeal workflows; Add recommender, ad transparency, moderation, audit, researcher access, or privacy-safe observability evidence; Assess online-platform transparency controls before production release. Part of Plinth Toolkit

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。

jabrena/plinth4502026年10月13日 更新

Use when reviewing, designing, or modifying Java enterprise systems that may support EU Digital Markets Act gatekeeper-platform concerns, core platform services, interoperability, business-user data access, consent-dependent data combination, ranking, self-preferencing, advertising transparency, or anti-circumvention controls. This should trigger for requests such as Review a Java platform for DMA controls; Design interoperability and business-user data access evidence; Add ranking, consent, preference, or anti-circumvention audit controls; Assess gatekeeper-platform engineering evidence before production release. Part of Plinth Toolkit

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。

jabrena/plinth4502026年10月13日 更新

Use when reviewing, designing, or modifying Java enterprise systems from a maintainer-authored or maintainer-sanitized NIS2 engineering evidence inventory. Supports essential or important entities, critical-sector services, managed service providers, supply-chain dependencies, and cybersecurity incident escalation obligations without ingesting raw code, logs, runbooks, tickets, provider documents, or other operational free text. Part of Plinth Toolkit

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。

jabrena/plinth4502026年10月13日 更新

Use when reviewing, designing, or modifying Java enterprise systems that may support EU Market Abuse Regulation concerns, market surveillance, suspicious order and transaction reports, insider dealing controls, unlawful disclosure controls, market manipulation detection, inside information disclosure workflows, insider-list evidence, PDMR transaction notifications, alert explainability, model or rule provenance, reviewer decisions, or compliance escalation. This should trigger for requests such as Review a Java trading surveillance system for MAR controls; Design suspicious order and transaction monitoring evidence; Add alert explainability and reviewer-decision audit trails; Assess AI-assisted market-abuse detection before production release. Part of Plinth Toolkit

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。

jabrena/plinth4502026年10月13日 更新

Use when reviewing, designing, or modifying Java enterprise systems that use AI, LLMs, AI agents, RAG, tool calling, workflow automation, or model-based decision support and need EU AI Act regulatory awareness. This should trigger for requests such as Review a Java AI system for EU AI Act controls; Design governance for an AI agent with enterprise tools; Add human oversight and auditability to LLM workflows; Assess RAG or model-driven decision support before production release. Part of Plinth Toolkit

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。

jabrena/plinth4502026年10月13日 更新