software-architecture-design
システムレベルの設計決定に焦点を当て、単一のサービスやコンポーネントの実装詳細ではなく、ソフトウェアアーキテクチャ設計のガイダンスを提供します。
npx skills add vasilyu1983/ai-agents-public --skill software-architecture-designBefore / After 効果比較
1 组`software-architecture-design` スキルの指導がない場合、システム設計は長期的な計画を欠き、不適切なアーキテクチャ選択、モジュール間の高い結合度、拡張性や保守性の困難さにつながる可能性があります。例えば、大規模なモノリシックアプリケーションは、ユーザー数の増加後に水平スケーリングが困難になります。
`software-architecture-design` スキルを使用すると、システムレベルのアーキテクチャ設計決定を行うことができます。このスキルは、開発者が適切なアーキテクチャスタイル(マイクロサービス、イベント駆動型、サーバーレスなど)を選択し、ベストプラクティスを参照して、システムが高い可用性、スケーラビリティ、保守性、セキュリティを備えていることを保証するように導きます。これにより、システムはビジネスの変化や将来の発展によりよく適応できるようになります。
description SKILL.md
software-architecture-design
Software Architecture Design — Quick Reference
Use this skill for system-level design decisions rather than implementation details within a single service or component.
Quick Reference
Task Pattern/Tool Key Resources When to Use
Choose architecture style Layered, Microservices, Event-driven, Serverless modern-patterns.md Greenfield projects, major refactors
Design for scale Load balancing, Caching, Sharding, Read replicas scalability-reliability-guide.md High-traffic systems, performance goals
Ensure resilience Circuit breakers, Retries, Bulkheads, Graceful degradation scalability-reliability-guide.md Distributed systems, external dependencies
Document decisions Architecture Decision Record (ADR) adr-template.md Major technical decisions, tradeoff analysis
Define service boundaries Domain-Driven Design (DDD), Bounded contexts microservices-template.md Microservices decomposition
Model data consistency ACID vs BASE, Event sourcing, CQRS, Saga patterns data-architecture-patterns.md Multi-service transactions
Plan observability SLIs/SLOs/SLAs, Distributed tracing, Metrics, Logs architecture-blueprint.md Production readiness
Migrate from monolith Strangler fig, Database decomposition, Shadow traffic migration-modernization-guide.md Legacy modernization
Design inter-service comms API Gateway, Service mesh, BFF pattern api-gateway-service-mesh.md Microservices networking
When to Use This Skill
Invoke when working on:
-
System decomposition: Deciding between monolith, modular monolith, microservices
-
Architecture patterns: Event-driven, CQRS, layered, hexagonal, serverless
-
Data architecture: Consistency models, sharding, replication, CQRS patterns
-
Scalability design: Load balancing, caching strategies, database scaling
-
Resilience patterns: Circuit breakers, retries, bulkheads, graceful degradation
-
API contracts: Service boundaries, versioning, integration patterns
-
Architecture decisions: ADRs, tradeoff analysis, technology selection
-
Migration planning: Monolith decomposition, strangler fig, database separation
When NOT to Use This Skill
Use other skills instead for:
-
Single-service implementation (routes, controllers, business logic) → software-backend
-
API endpoint design (REST conventions, GraphQL schemas) → dev-api-design
-
Security implementation (auth, encryption, OWASP) → software-security-appsec
-
Frontend component architecture → software-frontend
-
Database query optimization → data-sql-optimization
Decision Tree: Choosing Architecture Pattern
Project needs: [New System or Major Refactor]
├─ Single team, evolving domain?
│ ├─ Start simple → Modular Monolith (clear module boundaries)
│ └─ Need rapid iteration → Layered Architecture
│
├─ Multiple teams, clear bounded contexts?
│ ├─ Independent deployment critical → Microservices
│ └─ Shared data model → Modular Monolith with service modules
│
├─ Event-driven workflows?
│ ├─ Asynchronous processing → Event-Driven Architecture (Kafka, queues)
│ └─ Complex state machines → Saga pattern + Event Sourcing
│
├─ Variable/unpredictable load?
│ ├─ Pay-per-use model → Serverless (AWS Lambda, Cloudflare Workers)
│ └─ Batch processing → Serverless + queues
│
└─ High consistency requirements?
├─ Strong ACID guarantees → Monolith or Modular Monolith
└─ Distributed data → CQRS + Event Sourcing
Decision Factors:
-
Team size threshold: <10 developers → modular monolith typically outperforms microservices (operational overhead)
-
Team structure (Conway's Law) — architecture mirrors org structure
-
Deployment independence needs
-
Consistency vs availability tradeoffs (CAP theorem)
-
Operational maturity (monitoring, orchestration)
See references/modern-patterns.md for detailed pattern descriptions.
Output Guidelines
The references in this skill are background knowledge for you — absorb the patterns and present them as your own expertise. Do not cite internal reference file names (e.g., "from data-architecture-patterns.md") in user-facing output. Users don't know these files exist.
Every architecture recommendation should include:
-
Concrete technology picks: Name specific technologies (e.g., "Temporal.io for workflow orchestration", "Socket.io with Redis adapter") rather than staying abstract. The user needs to make build decisions, not just understand patterns.
-
What NOT to build: Explicitly call out what to defer or avoid. Premature scope is the #1 architecture mistake — help the user avoid it.
-
Team and process alignment: How does this architecture map to team structure? What ownership model does it imply? Include CODEOWNERS, deployment ownership, and on-call boundaries where relevant.
-
Success metrics: How will the team know the architecture is working? Include measurable indicators (deploy frequency, lead time, error rates, MTTR).
-
Focused length: Aim for depth on the 3–5 decisions that matter most rather than exhaustive coverage of every concern. A recommendation that's too long to read is a recommendation that won't be followed.
Workflow (System-Level)
Use this workflow when a user asks for architecture recommendations, decomposition, or major platform decisions.
-
Clarify: problem statement, non-goals, constraints, and success metrics
-
Capture quality attributes: availability, latency, throughput, durability, consistency, security, compliance, cost
-
Propose 2–3 candidate architectures and compare tradeoffs
-
Define boundaries: bounded contexts, ownership, APIs/events, integration contracts
-
Decide data strategy: storage, consistency model, schema evolution, migrations
-
Design for operations: SLOs, failure modes, observability, deployment, DR, incident playbooks
-
Call out scope limits: what NOT to build yet, what to defer, what to buy vs build
-
Document decisions: write ADRs for key tradeoffs and irreversible choices
Preferred deliverables (pick what fits the request):
-
Architecture blueprint:
assets/planning/architecture-blueprint.md -
Decision record:
assets/planning/adr-template.md -
Pattern deep dives:
references/modern-patterns.md,references/scalability-reliability-guide.md
2026 Considerations
Load only when the question explicitly involves current trends, vendor-specific constraints, or "what's the latest thinking on X?"
-
references/architecture-trends-2026.md — Platform engineering, data mesh, composable architecture, AI-native systems
-
data/sources.json — 60 curated resources organized by category:
platform_engineering_2026 — IDP trends, AI-platform convergence, Backstage
-
optional_ai_architecture— RAG patterns, multi-agent design, MCP/A2A protocols -
modern_architecture_2025— Data mesh, composable architecture, continuous architecture
If live web access is available, consult 2–3 authoritative sources from data/sources.json and fold findings into the recommendation. If not, answer with durable patterns and explicitly state assumptions that could change (vendor limits, pricing, managed-service capabilities).
Navigation
Core References
Read at most 2–3 references per question — pick the ones most relevant to the specific ask. Do not read all of them.
Reference Contents When to Read
modern-patterns.md 10 architecture patterns with decision trees Choosing or comparing patterns
scalability-reliability-guide.md CAP theorem, DB scaling, caching, circuit breakers, SRE Scaling or reliability questions
data-architecture-patterns.md CQRS variants, event sourcing, data mesh, sagas, consistency Data flow across services
migration-modernization-guide.md Strangler fig, DB decomposition, feature flags, risk assessment Refactoring a monolith
api-gateway-service-mesh.md Gateway patterns, service mesh, mTLS, observability Inter-service communication
architecture-trends-2026.md Platform engineering, data mesh, AI-native systems Current trends only
operational-playbook.md Architecture questions framework, decomposition heuristics Design discussion framing
Templates
Planning & Documentation (assets/planning/):
-
architecture-blueprint.md — Service blueprint (dependencies, SLAs, data flows, resilience, security, observability)
-
adr-template.md — Architecture Decision Record for tradeoff analysis
Architecture Patterns (assets/patterns/):
-
microservices-template.md — Microservices design (API contracts, resilience, deployment, testing)
-
event-driven-template.md — Event-driven architecture (event schemas, saga patterns, event sourcing)
Operations (assets/operations/):
- scalability-checklist.md — Scalability checklist (DB scaling, caching, load testing, auto-scaling, DR)
Related Skills
-
software-backend — Backend engineering, API implementation, data layer
-
software-frontend — Frontend architecture, micro-frontends, state management
-
dev-api-design — REST, GraphQL, gRPC design patterns
-
ops-devops-platform — CI/CD, deployment strategies, IaC
-
qa-observability — Monitoring, tracing, alerting, SLOs
-
software-security-appsec — Threat modeling, auth, secure design
-
data-sql-optimization — Database design, optimization, indexing
-
docs-codebase — Architecture documentation, C4 diagrams, ADRs
Weekly Installs204Repositoryvasilyu1983/ai-…s-publicGitHub Stars42First SeenJan 23, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled ongemini-cli173opencode173codex167cursor163github-copilot157claude-code138
forumユーザーレビュー (0)
レビューを書く
レビューなし
統計データ
ユーザー評価
この Skill を評価