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「systems」の検索結果

2,991 件 ・ 関連度順

概要と使いどころ

Implements feedback and notification systems including toasts, alerts, modals, progress indicators, and error states. Use when communicating system state, displaying messages, confirming actions, or showing 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。

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

Design and implement disaster recovery strategies with RTO/RPO planning, database backups, Kubernetes DR, cross-region replication, and chaos engineering testing. Use when implementing backup systems, configuring point-in-time recovery, setting up multi-region failover, or validating DR procedures.

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

Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.

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

Authentication, authorization, and API security implementation. Use when building user systems, protecting APIs, or implementing access control. Covers OAuth 2.1/OIDC, JWT patterns, sessions, Passkeys/WebAuthn, RBAC/ABAC/ReBAC, policy engines (OPA, Casbin, SpiceDB), managed auth (Clerk, Auth0), self-hosted (Keycloak, Ory), and API security 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。

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

Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.

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

Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).

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

Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.

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

Configure security information and event management (SIEM) systems for threat detection, log aggregation, and compliance. Use when implementing centralized security logging, writing detection rules, or meeting audit requirements across cloud and on-premise infrastructure.

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

Evaluate LLM systems using automated metrics, LLM-as-judge, and benchmarks. Use when testing prompt quality, validating RAG pipelines, measuring safety (hallucinations, bias), or comparing models for production deployment.

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

Generate comprehensive technical documentation including API docs (OpenAPI/Swagger), code documentation (TypeDoc/Sphinx), documentation sites (Docusaurus/MkDocs), Architecture Decision Records (ADRs), and diagrams (Mermaid/PlantUML). Use when documenting APIs, libraries, systems architecture, or building developer-facing documentation sites.

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

Implements onboarding and help systems including product tours, interactive tutorials, tooltips, checklists, help panels, and progressive disclosure patterns. Use when building first-time experiences, feature discovery, guided walkthroughs, contextual help, setup flows, or user activation features. Provides timing strategies, accessibility patterns (keyboard, screen readers, reduced motion), and metrics for measuring onboarding success.

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 and maintain compliance with SOC 2, HIPAA, PCI-DSS, and GDPR using unified control mapping, policy-as-code enforcement, and automated evidence collection. Use when building systems requiring regulatory compliance, implementing security controls across multiple frameworks, or automating audit preparation.

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

Implements navigation patterns and routing for both frontend (React/TS) and backend (Python) including menus, tabs, breadcrumbs, client-side routing, and server-side route configuration. Use when building navigation systems or setting up routing.

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

Monitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.

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

Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).

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

Assembles component outputs from AI Design Components skills into unified, production-ready component systems with validated token integration, proper import chains, and framework-specific scaffolding. Use as the capstone skill after running theming, layout, dashboard, data-viz, or feedback skills to wire components into working React/Next.js, Python, or Rust projects.

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

Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.

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

Download a small corpus of open-access arXiv survey/review PDFs about agentic systems and extract text for style learning.

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。

WILLOSCAR/research-units-pipeline-skills5142026年10月5日 更新

You MUST invoke this skill before saying "I don't know," guessing, or treating any topic as new, no matter how trivial the question seems. It supplements other memory systems, which only hold partial records. Searching past conversations is the only way to recover what was actually said.

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。

obra/episodic-memory4902026年9月11日 更新

Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.

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。

tavily-ai/skills4882026年9月5日 更新

Algorithmic and generative art with Canvas 2D - particles, flow fields, noise, fractals, L-systems.

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。

AThevon/genjutsu4872026年10月6日 更新

Own concrete MSBuild ItemGroup and item-expression questions. USE FOR: Include, Remove, Update, item identity and metadata, transforms, filtering, batching, duplicate or overlapping items, and reviews that verify whether those operations are correct. Generated items stay in scope when the central defect is item identity, metadata, batching, duplicate declarations, or glob/Remove/Update semantics. For a generated artifact missing from compilation or output, wrong target timing/path, or FileWrites clean tracking without a broader item-semantics defect, use including-generated-files. The item operation may be broken, suspected, or already correct. Exclude property-only issues, general incrementality with no item question, broad reviews with no concrete item concern, and non-MSBuild systems.

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-skills4872026年10月15日 更新

DO NOT INVOKE when the primary request explicitly asks to convert, migrate, modernize, or rewrite a legacy/old-style project to SDK style; use msbuild-modernization. Migration prompts often mention ToolsVersion, explicit Compile/Reference entries, packages.config, or Microsoft.CSharp.targets, but merely reviewing or auditing a file that contains those patterns remains in scope here. USE FOR broad review, audit, lint, or maintainability/correctness checks of project/build files, including custom targets; prioritized cross-cutting findings; discrete anti-patterns; F# ordering/FS0039; cross-platform paths; and focused Import safety verdicts. Review/audit is analysis-only unless fixes are requested. For concrete property/item defects use property-patterns/item-management; use target-authoring for implementation and extension-points for NuGet auto-import/layout discovery. Exclude non-MSBuild systems.

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-skills4872026年10月15日 更新