Skill UI

This comprehensive guide provides patterns and best practices for implementing robust testing strategies in modern JavaScript and TypeScript applications. It covers setting up test infrastructure, writing unit tests for functions and classes, creating integration tests for APIs, and implementing end-to-end tests for user flows, supporting TDD and CI/CD integration.

Automates Jenkins Pipeline introductory tasks within the DevOps Basics domain, offering best-practice patterns, production-ready configs, and validation for foundational CI/CD, version control, and containerization workflows.

Automates json config manager workflows in DevOps basics by offering guidance, industry best practices, production-ready configs, and validation across version control, containerization, CI/CD, and infrastructure fundamentals.

This is a production-grade reference architecture for building scalable video generation platforms powered by Kling AI. It details the complete pipeline, from the API gateway and job queuing (Redis/SQS) to the worker pool, object storage, and CDN delivery. Use this guide when designing high-throughput, resilient, and scalable AI video systems.

This skill addresses the challenge of handling temporary CDN links for videos generated by Kling AI. It provides a comprehensive, multi-cloud solution to download the video asset and persistently upload it to major cloud storage platforms, including AWS S3, Google Cloud Storage (GCS), and Azure Blob Storage. It facilitates building robust, long-term media pipelines and ensures metadata preservation.

This specialized, end-to-end workflow guides the deployment of applications onto Kubernetes clusters. It systematically covers all critical production phases, including Docker container preparation, generating K8s manifests, structuring applications with Helm charts, configuring advanced service meshes (like Istio), enforcing robust security policies (RBAC, NetworkPolicy), and implementing full observability and GitOps pipelines for reliable, scalable deployments.

A comprehensive CI/CD pipeline designed for LangChain applications. It automates quality assurance through fast, mocked unit tests and slower, more accurate integration tests utilizing real LLMs (e.g., OpenAI). It specifically validates the entire RAG pipeline and integrates tracing with LangSmith, ensuring robust deployment quality.

This comprehensive guide details the best practices for configuring Langfuse across development, staging, and production environments. It covers essential steps including environment-specific API key management, secret integration (AWS/GCP), and implementing reliable CI/CD pipelines to ensure data isolation and prevent cross-environment leakage, maintaining observability throughout the software lifecycle.

Master the configuration of Langfuse webhooks to capture crucial prompt lifecycle events (Created, Updated, Deleted). This skill guides you through building a secure endpoint to intercept these events, enabling powerful, automated integrations. Use cases include triggering CI/CD pipelines upon production prompt changes, syncing data to external systems, or notifying teams via Slack.

This guide provides the framework for configuring Continuous Integration and Continuous Deployment (CI/CD) pipelines specifically for Lindy AI agent integrations. It covers setting up automated tests for webhook receivers, validating payload processing, and checking authentication signatures using GitHub Actions and Node.js, ensuring robust and reliable deployment of agent services.

This guide details the best practices for configuring and isolating AI agents across development, staging, and production environments. It covers the setup of separate workspaces, secure secret management using tools like Vault/AWS/GCP, and integrating environment-aware logic into CI/CD pipelines to ensure data integrity and smooth promotion from dev to prod.

A comprehensive solution to integrate Linear project management with GitHub Actions CI/CD pipelines. This skill enables automating development workflows by running integration tests, automatically linking Pull Requests (PRs) to issues, transitioning issue states based on PR events, and creating new Linear issues directly from build failures. Ideal for ensuring development quality and maintaining up-to-date project tracking.