Set up a Python project with AWS CDK, a toolkit for defining cloud infrastructure in code. Follow guidance on prerequisites, AWS credentials, virtual environments, dependencies, template generation, deployment preparation, and reviewing changes before deployment.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Setup and initialization guide for developing AWS CDK (Cloud Development Kit) applications in Python. This skill enables users to configure environment prerequisites, create new CDK projects, manage dependencies, and deploy to AWS.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
This skill provides setup guidance for working with AWS CDK (Cloud Development Kit) projects using Python.
Before starting, ensure the following tools are installed:
npm install -g aws-cdk
cdk --version
# Install AWS CLI (if not installed)
brew install awscli
# Configure credentials
aws configure
Enter your AWS Access Key, Secret Access Key, default region, and output format when prompted.
mkdir my-cdk-project
cd my-cdk-project
cdk init app --language python
Your project will include:
app.py — Main application entry pointmy_cdk_project/ — CDK stack definitionsrequirements.txt — Python dependenciescdk.json — Configuration file# macOS/Linux
source .venv/bin/activate
# Windows
.venv\Scripts\activate
pip install -r requirements.txt
Primary dependencies:
aws-cdk-lib — Core CDK constructsconstructs — Base construct librarycdk synth
Generates cdk.out/ containing CloudFormation templates.
cdk deploy
Reviews and confirms deployment to the configured AWS account.
cdk bootstrap
Prepares environment resources like S3 buckets for asset storage.
cdk diff before deployment to preview changes.requirements.txt pinned for consistent builds.If issues occur, check:
cdk doctor to diagnose environment issues.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.