Skill UI

Automated heap dump analysis guidance for performance testing, offering step-by-step instructions, best-practice patterns, production-ready code and configs, and validation against common standards.

This comprehensive guide details how to set up robust CI/CD pipelines for projects integrating with Ideogram. It covers automated unit testing, optional integration tests using GitHub Secrets, and crucial prompt validation logic directly within GitHub Actions workflows, ensuring high code quality and reliable API interaction.

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.

Guides frontend developers through keyboard navigation testing, offering best-practice advice, code snippets, and automated validation for accessibility, frameworks, and performance.

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 demonstrates the basic workflow for building an AI agent using a webhook trigger. The agent receives structured data (payload), utilizes an LLM to extract key information and generate a concise summary (summarization step), and finally executes an action by posting the summary to Slack or sending an email. Ideal for beginners learning about AI agent architecture, testing data flow, and implementing simple automated system notifications.

Master comprehensive strategies for evaluating Large Language Model (LLM) applications. This skill covers automated metrics (BLEU, ROUGE, BERTScore), structured human evaluation dimensions, and advanced LLM-as-Judge techniques. Use it to systematically measure model performance, compare prompts, detect regressions, and build confidence in production systems.

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.

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.

Handles disaster recovery, PITR, and automated failover for production databases via the database-recovery-manager plugin, streamlining backup validation and recovery testing whenever users ask for database recovery support.

Mutation Test Runner delivers automated help for configuring and executing mutation testing workflows, offering best practices, code samples, and validation steps whenever mutation test runner patterns or automation requests appear.

Automated skill that assists with percentile analyzer workflows in performance testing, guiding users through load, stress, benchmarking, and monitoring setups with best practices.