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github/aws-cdk-python-setup

AWS CDK(Cloud Development Kit) 파이썬 애플리케이션 개발을 위한 설정 및 초기화 가이드입니다. 이 스킬을 사용하면 환경 사전 요구 사항을 구성하고, 새로운 CDK 프로젝트를 생성하며, 종속성을 관리하고 AWS에 배포할 수 있습니다.

aws-cdk-python-setup란 무엇인가요?

이 스킬은 AWS CDK(Cloud Development Kit)를 사용하여 Python으로 인프라를 코드로 정의하고 배포하는 방법을 제공합니다. 개발자는 CDK CLI, Node.js, Python 및 AWS 자격 증명과 같은 환경 사전 요구 사항을 설정하고, 'cdk init' 명령어로 새 프로젝트를 구성할 수 있습니다. 또한 pip/Poetry를 통한 종속성 관리, AWS 계정 부트스트래핑, 'cdk deploy'를 통한 스택 배포 방법을 안내하며, Python용 타입 힌트와 코드 완성 기능을 활용한 모범 사례도 포함합니다. 이 스킬은 Claude Code, Cursor, Codex와 같은 AI 에이전트 플랫폼과 호환되며, AWS CDK Python 개발 워크플로우를 크게 간소화합니다.

지원 대상~Claude Code~Codex CLI~Cursor
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이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

aws-cdk-python-setup은(는) 무엇을 하나요?

AWS CDK(Cloud Development Kit) 파이썬 애플리케이션 개발을 위한 설정 및 초기화 가이드입니다. 이 스킬을 사용하면 환경 사전 요구 사항을 구성하고, 새로운 CDK 프로젝트를 생성하며, 종속성을 관리하고 AWS에 배포할 수 있습니다.

사전 요구 사항

AWS CDK를 Python과 함께 사용하려면 다음이 필요합니다:

  • Node.js 14.x 이상: CDK CLI는 Node.js에서 실행됩니다.
  • Python 3.6 이상: CDK 앱은 Python으로 작성됩니다.
  • AWS CLI: AWS 자격 증명 및 리전 구성용.
  • AWS 자격 증명: IAM 사용자 또는 역할. 자세한 내용은 AWS 문서를 참조하세요.

설치

CDK CLI 설치

npm install -g aws-cdk

설치를 확인합니다:

cdk --version

Python 의존성

프로젝트 디렉토리에서 가상 환경을 생성하고 활성화합니다:

python3 -m venv .venv
source .venv/bin/activate  # Linux/macOS
.venv\Scripts\activate  # Windows

CDK 라이브러리 설치:

pip install aws-cdk-lib constructs

새 CDK 프로젝트 초기화

mkdir my-cdk-app
cd my-cdk-app
cdk init app --language python

이 명령어는 다음 파일을 생성합니다:

  • app.py: 진입점.
  • stack: 디렉토리에 스택 정의 포함.
  • requirements.txt: Python 의존성.
  • cdk.json: CDK 앱 구성.

AWS 환경 부트스트랩

CDK를 처음 사용하는 경우 AWS 환경을 부트스트랩합니다:

cdk bootstrap aws://ACCOUNT-NUMBER/REGION

ACCOUNT-NUMBER와 REGION을 실제 값으로 바꾸세요.

스택 배포

cdk deploy

합성 템플릿을 보려면:

cdk synth

모범 사례

  • 가상 환경을 사용하여 종속성을 격리하세요.
  • 스택 구성을 위해 cdk.json의 컨텍스트를 활용하세요.
  • Python용 CDK 라이브러리에서 타입 힌트와 코드 완성 기능을 사용하세요.
  • 재사용 가능한 인프라 구성 요소를 위해 구성을 별도 파일로 분리하세요.

문제 해결

  • 권한 오류: IAM 역할에 필요한 권한이 있는지 확인하세요.
  • 버전 불일치: cdk --version, python --version, node --version을 확인하세요.
  • 가상 환경: 소스 .venv/bin/activate로 환경이 활성화되었는지 확인하세요.

추가 자료

Individual skills in this repo

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

github/acquire-codebase-knowledge

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.

github/add-educational-comments

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

github/agent-governance

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)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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