Skill UI

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.

Configure automated Continuous Integration and Continuous Deployment (CI/CD) pipelines for Juicebox AI data analysis integrations. This setup utilizes GitHub Actions to run comprehensive unit tests (with mocked datasets) and integration tests (connecting to live APIs) on every PR and merge to main. It ensures that data upload logic, analysis execution, and result parsing workflows are robust and reliable before deployment.

This tool allows developers to integrate advanced AI video generation (using Kling AI) directly into continuous integration and continuous deployment pipelines (e.g., GitHub Actions, GitLab CI). Automate the creation of marketing videos, product demos, or regression video testing whenever code changes or a release occurs, ensuring consistent and timely video asset updates.

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.

Integrate Langfuse into CI/CD pipelines (e.g., GitHub Actions) to automate the testing and validation of Large Language Models (LLMs). This guide covers prompt regression testing, tracing validation, and setting up experiment-driven quality gates, ensuring reliable and high-performing AI applications throughout the development lifecycle.

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.