software-architecture-design

Software Architecture Design — Quick Reference

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "software-architecture-design" with this command: npx skills add vasilyu1983/ai-agents-public/vasilyu1983-ai-agents-public-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

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Automation

product-management

No summary provided by upstream source.

Repository SourceNeeds Review
Automation

marketing-visual-design

No summary provided by upstream source.

Repository SourceNeeds Review
Automation

startup-idea-validation

No summary provided by upstream source.

Repository SourceNeeds Review
Automation

marketing-leads-generation

No summary provided by upstream source.

Repository SourceNeeds Review