Skill UI

Automates the Continuous Integration and Continuous Deployment (CI/CD) process for OpenEvidence clinical decision support integrations. This setup ensures that unit tests (using mocked evidence queries and citations) and live API connectivity tests are run automatically on every pull request and merge to main. It validates critical functions like query formatting, evidence parsing, citation extraction, and response quality scoring, maintaining high reliability for medical AI applications.

This skill finds and compares decentralized finance (DeFi) yield opportunities across various protocols. It aggregates crucial data, including APY/APR calculations, Total Value Locked (TVL), and comprehensive risk assessments. Use it when researching yield farming strategies, comparing cross-chain protocols, or optimizing crypto investment returns.

This tool analyzes and compares staking opportunities across various Proof-of-Stake (PoS) blockchains and liquid staking protocols. It calculates net Annual Percentage Yield (APY), assesses protocol risks, and provides data-driven recommendations for optimal asset allocations to maximize returns while managing risk. Use it when analyzing staking opportunities, comparing validators, or optimizing your DeFi portfolio.

This skill coordinates the complex parallel execution of end-to-end, integration, and unit tests across multiple frameworks (e.g., Jest, Playwright, pytest) and CI/CD environments. It manages test splitting, worker allocation, tiered execution strategies, and aggregates results into unified reports. Ideal for optimizing the testing lifecycle and ensuring robust quality assurance in large-scale, multi-component applications. Key functions include intelligent retry logic for flaky tests and building CI pipelines (e.g., GitHub Actions matrix).

This guide provides comprehensive principles for modern game development, covering engine selection (Unity, Unreal, Godot), platform integration (Steam features, console compliance), input abstraction, and performance optimization techniques. It helps developers make informed decisions when building cross-platform games for PC and console environments.

This guide details how to configure robust CI/CD pipelines for Perplexity Sonar API integrations using GitHub Actions. It emphasizes best practices for automated testing, including separating costly live integration tests from lightweight mocked unit tests, ensuring code quality while managing API costs and rate limits.

Set up comprehensive CI/CD pipelines for PostHog integrations using GitHub Actions. This guide covers mocked unit tests (no API key needed), robust integration tests against a development PostHog project, and automated deployment annotations to mark releases in your PostHog timeline. Ideal for developers ensuring data quality and traceability across all environments.

This guide provides a comprehensive workflow for setting up a robust local development environment for PostHog integrations. It covers enabling debug mode for real-time event inspection, implementing mocking strategies for stable unit testing, and configuring integration tests using a dedicated development PostHog project. Use this when building or testing analytics tracking features with posthog-js or posthog-node to ensure fast, isolated development cycles.

Provides automated guidance and production-ready code for property-based test helper tasks, covering unit, integration, and mocking scenarios within Test Automation workflows.

Guides developers through when and how to apply property-based testing across serializers, validators, normalizers, and smart contracts, highlighting properties, priorities, and workflow steps that complement existing unit testing efforts.

This skill auto-activates when someone mentions pytest test generator, offering guided test automation workflows, producing ready-to-use code/configuration, and validating outputs for unit, integration, and mocking scenarios.

An end-to-end workflow for building robust, production-ready Python backend APIs. This guide covers all stages, including project scaffolding, SQLAlchemy database integration, Pydantic validation, implementing JWT/OAuth2 authentication, comprehensive error handling, unit testing, and Docker deployment for modern microservices.