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software-architecture-design软件架构设计

Agent Skill

用于辅助界面设计、视觉规范、排版、配色、布局和交互体验优化。它适合让 Agent 根据产品场景整理页面结构、生成 UI 方案、检查视觉一致性或改进组件层级。使用时需要结合现有品牌、设计系统和用户任务,不应只堆装饰元素;涉及真实页面改动时,应通过截图或浏览器预览检查文本溢出、对齐和响应式表现。

总安装

10,866

周安装

462

GitHub Stars

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下载量

3,807
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:software-architecture-design(软件架构设计)
来源仓库:https://github.com/vasilyu1983/ai-agents-public
仓库路径:skills/software-architecture-design
安装命令:
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill software-architecture-design
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill software-architecture-design

简介

用于辅助界面设计、视觉规范、排版、配色、布局和交互体验优化,适合整理页面结构或生成 UI 方案。

  • 它能检查视觉一致性或改进组件层级,使用时需结合现有品牌、设计系统和用户任务。
  • 不应只堆装饰元素,涉及真实页面改动时应通过截图或浏览器预览检查文本溢出和对齐。
  • 通过 npx skills add https://github.com/vasilyu1983/ai-agents-public --skill software-architecture-design 安装。
  • 适用于界面设计类任务,需确保与项目规范一致。

SKILL.md

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

TaskPattern/ToolKey ResourcesWhen to Use
Choose architecture styleLayered, Microservices, Event-driven, Serverlessmodern-patterns.mdGreenfield projects, major refactors
Design for scaleLoad balancing, Caching, Sharding, Read replicasscalability-reliability-guide.mdHigh-traffic systems, performance goals
Ensure resilienceCircuit breakers, Retries, Bulkheads, Graceful degradationscalability-reliability-guide.mdDistributed systems, external dependencies
Document decisionsArchitecture Decision Record (ADR)adr-template.mdMajor technical decisions, tradeoff analysis
Define service boundariesDomain-Driven Design (DDD), Bounded contextsmicroservices-template.mdMicroservices decomposition
Model data consistencyACID vs BASE, Event sourcing, CQRS, Saga patternsdata-architecture-patterns.mdMulti-service transactions
Plan observabilitySLIs/SLOs/SLAs, Distributed tracing, Metrics, Logsarchitecture-blueprint.mdProduction readiness
Migrate from monolithStrangler fig, Database decomposition, Shadow trafficmigration-modernization-guide.mdLegacy modernization
Design inter-service commsAPI Gateway, Service mesh, BFF patternapi-gateway-service-mesh.mdMicroservices 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:

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.

  1. Clarify: problem statement, non-goals, constraints, and success metrics
  2. Capture quality attributes: availability, latency, throughput, durability, consistency, security, compliance, cost
  3. Propose 2–3 candidate architectures and compare tradeoffs
  4. Define boundaries: bounded contexts, ownership, APIs/events, integration contracts
  5. Decide data strategy: storage, consistency model, schema evolution, migrations
  6. Design for operations: SLOs, failure modes, observability, deployment, DR, incident playbooks
  7. Call out scope limits: what NOT to build yet, what to defer, what to buy vs build
  8. 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?"

- 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.

ReferenceContentsWhen to Read
modern-patterns.md10 architecture patterns with decision treesChoosing or comparing patterns
scalability-reliability-guide.mdCAP theorem, DB scaling, caching, circuit breakers, SREScaling or reliability questions
data-architecture-patterns.mdCQRS variants, event sourcing, data mesh, sagas, consistencyData flow across services
migration-modernization-guide.mdStrangler fig, DB decomposition, feature flags, risk assessmentRefactoring a monolith
api-gateway-service-mesh.mdGateway patterns, service mesh, mTLS, observabilityInter-service communication
architecture-trends-2026.mdPlatform engineering, data mesh, AI-native systemsCurrent trends only
operational-playbook.mdArchitecture questions framework, decomposition heuristicsDesign discussion framing

Templates

Planning & Documentation (assets/planning/):

Architecture Patterns (assets/patterns/):

Operations (assets/operations/):

Related Skills

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

28.67%
按下载量换算1,091

Cursor

24.46%
按下载量换算931

Antigravity

17.32%
按下载量换算659

Gemini CLI

12.2%
按下载量换算464

OpenCode

8.46%
按下载量换算322

Codex

3.38%
按下载量换算129

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

来源信息

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