Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS infrastructure programmatically. Covers CDK app structure, construct patterns, stack composition, and deployment workflows.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
This skill provides comprehensive guidance for developing AWS infrastructure using the Cloud Development Kit (CDK), with integrated MCP servers for accessing latest AWS knowledge and CDK utilities.
This skill includes two MCP servers automatically configured with the plugin:
When to use: Always verify AWS service information before implementation
Critical: Use this server whenever AWS service features, configurations, or availability need verification.
When to use: For CDK-specific guidance and utilities
Important: Leverage this server for CDK construct guidance and advanced CDK operations.
Use this skill when:
CRITICAL: Do NOT explicitly specify resource names when they are optional in CDK constructs.
Why: CDK-generated names enable:
Pattern: Let CDK generate unique names automatically using CloudFormation's naming mechanism.
// ❌ BAD - Explicit naming prevents reusability and parallel deployments
new lambda.Function(this, 'MyFunction', {
functionName: 'my-lambda', // Avoid this
// ...
});
// ✅ GOOD - Let CDK generate unique names
new lambda.Function(this, 'MyFunction', {
// No functionName specified - CDK generates: StackName-MyFunctionXXXXXX
// ...
});
Security Note: For different environments (dev, staging, prod), follow AWS Security Pillar best practices by using separate AWS accounts rather than relying on resource naming within a single account. Account-level isolation provides stronger security boundaries.
Use the appropriate Lambda construct based on runtime:
TypeScript/JavaScript: Use @aws-cdk/aws-lambda-nodejs
import { NodejsFunction } from 'aws-cdk-lib/aws-lambda-nodejs';
new NodejsFunction(this, 'MyFunction', {
entry: 'lambda/handler.ts',
handler: 'handler',
// Automatically handles bundling, dependencies, and transpilation
});
Python: Use @aws-cdk/aws-lambda-python
import { PythonFunction } from '@aws-cdk/aws-lambda-python-alpha';
new PythonFunction(this, 'MyFunction', {
entry: 'lambda',
index: 'handler.py',
handler: 'handler',
// Automatically handles dependencies and packaging
});
Benefits:
Use a multi-layer validation strategy for comprehensive CDK quality checks:
For TypeScript/JavaScript projects:
Install cdk-nag for synthesis-time validation:
npm install --save-dev cdk-nag
Add to your CDK app:
import { Aspects } from 'aws-cdk-lib';
import { AwsSolutionsChecks } from 'cdk-nag';
const app = new App();
Aspects.of(app).add(new AwsSolutionsChecks());
Optional - VS Code users: Install CDK NAG Validator extension for faster feedback on file save.
For Python/Java/C#/Go projects: cdk-nag is available in all CDK languages and provides the same synthesis-time validation.
Synthesis with cdk-nag: Validate stack with comprehensive rules
cdk synth # cdk-nag runs automatically via Aspects
Suppress legitimate exceptions with documented reasons:
import { NagSuppressions } from 'cdk-nag';
// Document WHY the exception is needed
NagSuppressions.addResourceSuppressions(resource, [
{
id: 'AwsSolutions-L1',
reason: 'Lambda@Edge requires specific runtime for CloudFront compatibility'
}
]);
Build: Ensure compilation succeeds
npm run build # or language-specific build command
Tests: Run unit and integration tests
npm test # or pytest, mvn test, etc.
Validation Script: Meta-level checks
./scripts/validate-stack.sh
The validation script now focuses on:
Always verify before implementing:
Example scenarios:
Leverage for CDK-specific guidance:
Example scenarios:
For detailed CDK patterns, anti-patterns, and architectural guidance, refer to the comprehensive reference:
File: references/cdk-patterns.md
This reference includes:
scripts/validate-stack.sh - Pre-deployment validationreferences/cdk-patterns.md - Detailed pattern libraryWhen GitHub Actions workflow files exist in the repository, ensure all checks defined in .github/workflows/ pass before committing. This prevents CI/CD failures and maintains code quality standards.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
日本語の概要は準備中です。原文の説明を表示しています。
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
日本語の概要は準備中です。原文の説明を表示しています。
Unified analysis pipeline for affinity-based proteomics platforms — Olink (PEA, NPX) and SomaLogic SomaScan (SOMAmer, RFU). Platform-aware QC, normalisation, differential abundance, volcano plots, heatmaps, and PCA.
日本語の概要は準備中です。原文の説明を表示しています。