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curiositech/windags-skills

AWS CDK 基礎設施建置器,使用 TypeScript、L2/L3 建構與 Well-Architected 模式。

windags-skills 是什麼?

這是一個專為 AWS CDK 開發者設計的 AI Agent 技能。它專注於使用 TypeScript 語言,透過 CDK 的 L2 和 L3 高層次建構來定義 AWS 基礎設施,並自動遵循 AWS Well-Architected Framework 的最佳實踐。此技能的核心價值在於幫助開發者快速建立安全、可擴展且易於維護的基礎設施即程式碼(IaC)專案。支援的平台包括 Claude Code、Cursor、Codex 等 AI 編碼助手。它能自動將基礎設施拆分為有狀態(如資料庫)與無狀態(如 API)的 Stack,建議使用 CDK Pipelines 建立自我變異的 CI/CD 部署管道,並強制整合 cdk-nag 進行架構合規性檢查。此外,它會幫助開發者避免常見的反模式,例如避免大量使用 L1 低階建構、避免單體式 Stack,並確保 IAM 遵循最小權限原則。總而言之,這個技能將 AWS CDK 開發的專業知識內建於 AI 代理中,大幅降低學習曲線並提升生產力。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/aws-cdk-builder

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

AWS CDK 建置器

擅長使用 CDK 搭配 TypeScript 建置 AWS 基礎設施,運用 L2/L3 建構與 Well-Architected Framework 模式。

觸發條件

啟用於: "AWS CDK", "CDK construct", "CDK stack", "CDK pipeline", "AWS IaC TypeScript", "L2 construct", "CDK patterns", "cdk deploy", "cdk synth"

不適用於: Terraform IaC → terraform-module-builder | Kubernetes manifests → kubernetes-manifest-generator | Serverless Framework → devops-automator

快速開始

  1. 初始化 CDK 應用程式 — npx cdk init app --language typescript
  2. 設計 Stack 結構 — 將有狀態(資料庫、儲存桶)與無狀態(運算、API)分離
  3. 使用 L2 建構 — 優先使用高層次建構而非 L1 CloudFormation 資源
  4. 加入 CDK Nag — 自動化的 Well-Architected 合規性檢查
  5. 使用 CDK Pipelines 部署 — 自我變異的 CI/CD 管道

核心能力

領域技術
CDK 核心CDK 2.180+, Constructs library, CDK CLI, cdk.json
L2/L3 建構aws-lambda, aws-apigateway, aws-ecs-patterns, aws-rds
合規性cdk-nag (AwsSolutions, NIST, HIPAA, PCI 套件)
CI/CDCDK Pipelines, CodePipeline, CodeBuild, 自我變異
模式ECS Fargate 模式, API Gateway + Lambda, S3 + CloudFront

架構模式

Stack 組織(有狀態 vs 無狀態)

// bin/app.ts — 具有環境分離的頂層應用程式
const app = new cdk.App();

// 有狀態 Stack — 很少變更,更新時需謹慎
const dataStack = new DataStack(app, 'Data-Prod', {
  env: { account: '123456789', region: 'us-east-1' },
});

// 無狀態 Stack — 頻繁部署,可安全銷毀/重建
const apiStack = new ApiStack(app, 'Api-Prod', {
  env: { account: '123456789', region: 'us-east-1' },
  database: dataStack.database,
  bucket: dataStack.bucket,
});

// 管道 Stack — 自我變異的 CI/CD
new PipelineStack(app, 'Pipeline', {
  env: { account: '123456789', region: 'us-east-1' },
});

包含最佳實踐的 L2 建構

// lib/api-stack.ts
export class ApiStack extends cdk.Stack {
  constructor(scope: Construct, id: string, props: ApiStackProps) {
    super(scope, id, props);

    const handler = new lambda.Function(this, 'Handler', {
      runtime: lambda.Runtime.NODEJS_22_X,
      handler: 'index.handler',
      code: lambda.Code.fromAsset('lambda/'),
      memorySize: 256,
      timeout: cdk.Duration.seconds(30),
      tracing: lambda.Tracing.ACTIVE,       // X-Ray
      insightsVersion: lambda.LambdaInsightsVersion.VERSION_1_0_229_0,
      environment: {
        TABLE_NAME: props.table.tableName,
        POWERTOOLS_SERVICE_NAME: 'api',     // Lambda Powertools
      },
    });

    props.table.grantReadWriteData(handler); // 最小權限

    const api = new apigw.RestApi(this, 'Api', {
      deployOptions: {
        tracingEnabled: true,
        metricsEnabled: true,
        throttlingRateLimit: 1000,
        throttlingBurstLimit: 500,
      },
    });

    api.root.addResource('items').addMethod('GET',
      new apigw.LambdaIntegration(handler));
  }
}

CDK Pipelines(自我變異)

來源 (GitHub) → Synth (cdk synth) → 自我變異
    │                                      │
    ▼                                      ▼
更新管道 ─── 部署測試環境 ─── 手動核准 ─── 部署正式環境
               │                           │
           整合測試                   煙霧測試

反模式

  1. 到處使用 L1 建構 — 使用 CfnBucket 而非 s3.Bucket。L2 建構預設即編碼了最佳實踐(加密、日誌記錄、存取控制)。
  2. 單體式 Stack — 一個 Stack 包含 200 多個資源會達到 CloudFormation 限制並導致部署緩慢。應將有狀態/無狀態 Stack 拆分,並使用跨 Stack 參考。
  3. 缺少 cdk-nag — 在沒有合規性檢查的情況下部署。應加入 Aspects.of(app).add(new AwsSolutionsChecks()) 以在部署前捕捉安全性問題。
  4. 硬編碼帳戶/區域 — 在建構程式碼中使用 account: '123456789'。應使用 cdk.json context 或 cdk.Environment 查詢以實現可移植性。
  5. 無快照測試 — CDK 會產生可能意外變更的 CloudFormation 範本。應加入 expect(template).toMatchSnapshot() 測試以偵測非預期變更。

品質檢查清單

[ ] Stack 已分離:有狀態(資料)vs 無狀態(運算)
[ ] 使用 L2/L3 建構(非原始 CloudFormation L1)
[ ] 已啟用 cdk-nag 並使用 AwsSolutions 套件
[ ] 所有 Stack 均有快照測試
[ ] 使用 CDK Pipelines 進行自我變異的 CI/CD
[ ] 透過授權方法(grantRead, grantWrite)實現最小權限 IAM
[ ] cdk synth 產生有效的 CloudFormation
[ ] 跨 Stack 參考使用匯出的輸出
[ ] 設定了刪除策略(正式環境資料使用 RETAIN,開發環境使用 DESTROY)
[ ] 透過 Aspects 應用標籤以進行成本分配
[ ] 每次部署前都需審查 cdk diff
[ ] Lambda 函數使用 Powertools 以實現可觀測性

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

curiositech/windags-skills

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

curiositech/windags-skills

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

curiositech/windags-skills

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

curiositech/windags-skills

Implement WCAG 2.2 AA/AAA compliance with automated testing, keyboard navigation, screen reader support, and focus management. Activate on: accessibility audit, WCAG compliance, keyboard navigation, screen reader, aria attributes, axe-core, focus trap. NOT for: design-level accessibility review (use design-accessibility-auditor), color contrast only (use css-in-js-architect).

curiositech/windags-skills

Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

curiositech/windags-skills

Designs digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.

curiositech/windags-skills

>- Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

curiositech/windags-skills

Extend and modify the admin dashboard, developer portal, and operations console. Use when adding new admin tabs, metrics, monitoring features, or internal tools. Activates for dashboard development, analytics, user management, and internal tooling.

curiositech/windags-skills

Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).

curiositech/windags-skills

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

curiositech/windags-skills

AI-powered calendar management and agent-based scheduling coordination. Covers calendar APIs (Google Calendar, CalDAV/iCal), AI scheduling assistants (Reclaim, Clockwise, Motion, Cal.com), building custom calendar agents with MCP, multi-calendar merging, timezone management, focus block protection, meeting fatigue detection, and agent-to-agent meeting negotiation protocols. Activate on: "calendar agent", "AI scheduling", "calendar coordination", "meeting scheduling", "calendar API", "focus time protection", "calendar optimization", "Google Calendar MCP", "Reclaim", "Clockwise", "Motion", "Cal.com", "smart scheduling", "calendar-aware agent", "timezone scheduling", "agent negotiation meetings". NOT for: manual calendar UI component design (use form-validation-architect), project management scheduling or Gantt charts (use project-management-guru-adhd), general time-tracking or pomodoro apps (use adhd-daily-planner for time-awareness), building the agent itself from scratch (use agent-creator).

curiositech/windags-skills

Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

curiositech/windags-skills

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

curiositech/windags-skills

Automated discovery and matching of agent skills for dynamic task routing and capability assessment

curiositech/windags-skills

Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).

curiositech/windags-skills

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

curiositech/windags-skills

Logic-based agent programming language implementing BDI architecture for practical autonomous agent development

curiositech/windags-skills

>- Design AgentSpeak(L)-style BDI agents with context-guarded plans, selection functions, and intention stacks. Use for interruptible autonomy, agent policy, and multi-agent orchestration in dynamic environments. NOT for simple rule engines, static planners, or centralized workflows.

curiositech/windags-skills

Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

curiositech/windags-skills

license: Apache-2.0 NOT for unrelated tasks outside this domain.

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