Skill UI

This skill provides comprehensive patterns for integrating external applications with the Lindy AI platform. It covers three primary channels: Webhook triggers for receiving inbound data, HTTP actions for making outbound API calls, and executing custom Python/JavaScript code (Run Code). Use this when building complex, multi-step automation workflows or connecting internal systems to Lindy agents.

A mandatory workflow for ensuring high code quality across multiple ecosystems, including Node.js/TypeScript and Python. This pipeline runs static analysis tools—such as linters, type checkers (MyPy, tsc), and security scanners (Bandit)—after every code change. It mandates that no task is considered 'done' or committed until all validation checks pass, resolving style, syntax, and type mismatches.

A comprehensive guide to integrating Lokalise into Continuous Integration/Continuous Deployment (CI/CD) workflows using GitHub Actions. This setup automates the entire translation lifecycle: uploading source strings on code pushes, downloading the latest translations during builds, and running PR checks to ensure translation coverage across all required locales. It eliminates manual file management, keeping code, translations, and CI pipelines perfectly synchronized.

This comprehensive workflow automates the core source-to-Lokalise process. Users can upload source translation files (JSON, XLIFF, PO, etc.), programmatically create new keys from code scans or CMS exports, update existing keys, and manage release tags. It is designed for CI/CD pipelines and bulk content synchronization.

Automates guidance for setting up and maintaining long polling handlers within API integrations, covering best practices, code samples, validation, and webhook flow optimization.

This comprehensive guide demonstrates the fundamental workflow for creating, retrieving, and managing work orders using the MaintainX REST API. It provides actionable code examples using both cURL for quick testing and TypeScript for robust client implementation. It is essential for developers integrating a CMMS system or testing basic API connectivity.

Automates the setup of memory profilers for performance testing workflows, guiding users through best practices, code/configuration generation, and validation against industry standards whenever memory profiler setup is mentioned.

This skill acts as a specialized expert for generating professional and complex diagrams using Mermaid syntax. It specializes in creating flowcharts, sequence diagrams, ERDs, state charts, Gantt charts, and various system architectures. It adheres to best practices, ensuring readability, consistent styling, and providing complete, ready-to-use code for multiple diagram types.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

This comprehensive workflow guides the process of building and deploying new features that must maintain consistent functionality and design across multiple operating systems (web, mobile, and desktop). It strictly follows an API-first architecture, starting with defining shared API contracts (OpenAPI/GraphQL) and a unified design system. The process then guides parallel implementation across specialized stacks like React/Next.js, SwiftUI, and Jetpack Compose, ensuring high code consistency and faster time-to-market.

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.

Guides writing Python code inside n8n Code nodes, covering recommended use cases, helper data access patterns, Beta vs Native modes, and webhook/data handling rules so you can safely script workflow transformations with Python.