本文へ移動
cccskills
無料GitHub で公開

cloud-design-patterns

Cloud design patterns for distributed systems architecture covering 42 industry-standard patterns across reliability, performance, messaging, security, and deployment categories. Use when designing, reviewing, or implementing distributed system architectures.

インストール方法を見る

含まれるファイル(10)

  • SKILL.md4.4 KB
  • references/architecture-design.md4.6 KB
  • references/azure-service-mappings.md606 B
  • references/best-practices.md1.6 KB
  • references/deployment-operational.md3.2 KB
  • references/event-driven.md715 B
  • references/messaging-integration.md4.5 KB
  • references/performance.md6.6 KB
  • references/reliability-resilience.md6.2 KB
  • references/security.md1.8 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Cloud Design Patterns

Architects design workloads by integrating platform services, functionality, and code to meet both functional and nonfunctional requirements. To design effective workloads, you must understand these requirements and select topologies and methodologies that address the challenges of your workload's constraints. Cloud design patterns provide solutions to many common challenges.

System design heavily relies on established design patterns. You can design infrastructure, code, and distributed systems by using a combination of these patterns. These patterns are crucial for building reliable, highly secure, cost-optimized, operationally efficient, and high-performing applications in the cloud.

The following cloud design patterns are technology-agnostic, which makes them suitable for any distributed system. You can apply these patterns across Azure, other cloud platforms, on-premises setups, and hybrid environments.

How Cloud Design Patterns Enhance the Design Process

Cloud workloads are vulnerable to the fallacies of distributed computing, which are common but incorrect assumptions about how distributed systems operate. Examples of these fallacies include:

  • The network is reliable.
  • Latency is zero.
  • Bandwidth is infinite.
  • The network is secure.
  • Topology doesn't change.
  • There's one administrator.
  • Component versioning is simple.
  • Observability implementation can be delayed.

These misconceptions can result in flawed workload designs. Design patterns don't eliminate these misconceptions but help raise awareness, provide compensation strategies, and provide mitigations. Each cloud design pattern has trade-offs. Focus on why you should choose a specific pattern instead of how to implement it.


References

ReferenceWhen to load
Reliability & Resilience PatternsAmbassador, Bulkhead, Circuit Breaker, Compensating Transaction, Retry, Health Endpoint Monitoring, Leader Election, Saga, Sequential Convoy
Performance PatternsAsync Request-Reply, Cache-Aside, CQRS, Index Table, Materialized View, Priority Queue, Queue-Based Load Leveling, Rate Limiting, Sharding, Throttling
Messaging & Integration PatternsChoreography, Claim Check, Competing Consumers, Messaging Bridge, Pipes and Filters, Publisher-Subscriber, Scheduler Agent Supervisor
Architecture & Design PatternsAnti-Corruption Layer, Backends for Frontends, Gateway Aggregation/Offloading/Routing, Sidecar, Strangler Fig
Deployment & Operational PatternsCompute Resource Consolidation, Deployment Stamps, External Configuration Store, Geode, Static Content Hosting
Security PatternsFederated Identity, Quarantine, Valet Key
Event-Driven Architecture PatternsEvent Sourcing
Best Practices & Pattern SelectionSelecting appropriate patterns, Well-Architected Framework alignment, documentation, monitoring
Azure Service MappingsCommon Azure services for each pattern category

Pattern Categories at a Glance

CategoryPatternsFocus
Reliability & Resilience9 patternsFault tolerance, self-healing, graceful degradation
Performance10 patternsCaching, scaling, load management, data optimization
Messaging & Integration7 patternsDecoupling, event-driven communication, workflow coordination
Architecture & Design7 patternsSystem boundaries, API gateways, migration strategies
Deployment & Operational5 patternsInfrastructure management, geo-distribution, configuration
Security3 patternsIdentity, access control, content validation
Event-Driven Architecture1 patternEvent sourcing and audit trails

External Links

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Primary accessibility skill for VS Code. REQUIRED for new feature and contribution work, and also applies to updates of existing UI. Covers accessibility help dialogs, accessible views, verbosity settings, signals, ARIA announcements, keyboard navigation, and ARIA labels/roles.

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。

jcasnellie69/homelab-config22026年10月12日 更新

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level 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。

jcasnellie69/homelab-config22026年10月12日 更新

Act on user feedback attached to the current session. Use when the user submits feedback on the session's changes via the Submit Feedback button.

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。

jcasnellie69/homelab-config22026年10月12日 更新

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

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。

jcasnellie69/homelab-config22026年10月12日 更新

Use when adding, modifying, or reviewing VS Code configuration policies. Covers the full policy lifecycle from registration to export to platform-specific artifacts. Run on ANY change that adds a `policy:` field to a configuration property.

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。

jcasnellie69/homelab-config22026年10月12日 更新

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

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。

jcasnellie69/homelab-config22026年10月12日 更新

jcasnellie69 のスキルをすべて見る

このスキルの問題を報告する