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Juicebox CI/CD Data Analysis Testing

juicebox-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

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.

Automating Video Generation in CI/CD

klingai-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

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.

LangChain CI/CD Integration and Testing

langchain-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

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.

LangChain Local Dev Testing Setup

langchain-local-dev-loop

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to configuring a structured, local development environment for LangChain applications. It covers setting up project architecture, managing dev dependencies, and implementing a two-tiered testing strategy: isolated unit tests using mocked LLMs (for fast, API-free testing) and robust integration tests using real API providers. Ideal for establishing a reliable, rapid iteration workflow.

Langfuse CI/CD for LLM Quality Testing

langfuse-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

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.

Langfuse Tracing: Hello World Example

langfuse-hello-world

jeremylongshore/claude-code-plugins-plus-skills

A minimal example demonstrating how to implement basic tracing using the Langfuse SDK. It illustrates the hierarchy of traces, spans, and generations (LLM calls), providing code examples in TypeScript and Python for easy setup and integration testing.

Automating Lindy Webhook CI/CD Testing

lindy-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

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.

Local Webhook Testing for AI Agents

lindy-local-dev-loop

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide for setting up a local development loop to test AI agent integrations. This skill teaches developers how to build and debug webhook receivers, callback handlers, and associated application code that interacts with a cloud-hosted AI agent (like Lindy). It requires using tunneling services (e.g., ngrok) to expose local endpoints for receiving webhooks and simulating the entire agent-to-application data flow.

Setting Up Linear Local Development Workflow

linear-local-dev-loop

jeremylongshore/claude-code-plugins-plus-skills

This guide provides a comprehensive workflow for setting up a local development environment for Linear integrations. It covers project scaffolding, environment configuration, connection verification using the official SDK, robust integration testing with Vitest, and simulating webhooks using ngrok. Essential for developers building and testing SaaS connections with Linear.

MaintainX CI/CD Pipeline Integration Testing

maintainx-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

This comprehensive guide provides templates for integrating MaintainX API testing into automated CI/CD pipelines. It covers multiple stages, including unit tests (mocked), live integration tests against staging environments, and security scans (like gitleaks). Use this when setting up robust, automated quality gates for your MaintainX SaaS integrations.

MaintainX Local Dev Loop Setup

maintainx-local-dev-loop

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to setting up a local development loop for building and testing MaintainX integrations. This workflow utilizes TypeScript, hot reloading, mock API servers, and automated unit/integration testing. It provides a sandboxed environment for developers to efficiently develop and debug their services.

Mistral AI CI/CD Prompt Testing

mistral-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

Automate the validation and testing of prompts and AI responses within your CI/CD pipeline using Mistral AI. This integration provides robust prompt regression testing, model quality checks, automatic cost estimation for API usage, and deployment gates, ensuring the stability and reliability of your AI features upon every pull request.

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