ExaMeld’s Actions module consolidates script generation, management, and execution across multiple languages and platforms. AI-assisted code generation accelerates script creation from plain language descriptions, while multi-language execution engines ensure compatibility across heterogeneous infrastructure.
Script Library
A searchable grid interface displays available scripts with metadata including name, language, description, and recent modification date. Scripts support cloning to facilitate template-based creation, editing for refinement, and deletion for cleanup. Full-text fuzzy search enables rapid location of relevant scripts by name, description, or content, supporting libraries with hundreds of scripts.
AI-Assisted Generation
Describe what you need in plain language, and AI generates ready-to-use scripts in your chosen language (PowerShell, Python, C#, or Ansible) and target cloud platform (Azure, AWS, or GCP). The generated code is platform-aware, leveraging the appropriate APIs and SDKs for your environment. Output includes comments and structured error handling, reducing manual refinement requirements.
Execution Engines
Four distinct execution engines support different script types:
PowerShell runs scripts directly without requiring administrative changes to your system’s execution policy, enabling script execution even in restricted environments.
Python supports both portable (bundled) interpreters and custom interpreter paths, enabling use of project-specific environments and virtual installations. Real-time streaming output displays results as execution progresses, facilitating interactive debugging and long-running script monitoring.
C# compiles and runs code directly within ExaMeld, eliminating external tool dependencies and delivering rapid execution. Concise script-style code is supported without boilerplate class and method declarations, keeping scripts clean and readable.
Ansible manages playbook storage and execution within ExaMeld, integrating infrastructure automation directly into the actions workflow without context switching to external tools.
Job Management
Active job tracking displays all running jobs with real-time output streams. Individual jobs can be cancelled cleanly or terminated forcefully, with elapsed time displayed throughout execution. Output streams provide complete visibility into execution progress and results.
Configuration
Configurable timeout limits prevent runaway script execution, with sensible defaults and per-job overrides. Working directory selection determines execution context for file operations and relative path resolution. Python interpreter management enables selection between portable and custom interpreters. AI settings configure the model and endpoint used for code generation.