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Test-Driven Development (TDD) is a development methodology that emphasizes writing failing tests first, followed by writing minimal code to pass them (Red -> Green), and finally refactoring the code (Refactor). This approach ensures that code is always verified against its intended public behavior, making the system robust, maintainable, and resilient to internal changes. It teaches developers to focus on 'what' the system does, rather than 'how' it does it.

A comprehensive guide for automating workflows using the TechSmith Snagit COM API and Camtasia. This skill provides detailed instructions on cost tuning, handling various input/output types, and troubleshooting common errors for advanced screen capture and video editing automation. Primary scripting methods involve PowerShell and Python (pywin32).

This bundle provides comprehensive debugging guidance for automating TechSmith tools, specifically focusing on the Snagit COM API and Camtasia workflows. It details input/output constants, error handling procedures (e.g., COM registration, permissions), and scripting best practices using PowerShell or Python interop, enabling users to build advanced screen capture and video editing automation solutions.

This guide details the deploy integration process for automating screen capture and video editing using TechSmith's Snagit COM API and Camtasia Producer CLI. It is designed for developers needing to orchestrate complex content pipelines, such as programmatically capturing specific screen regions, handling various output formats (clipboard, file, printer), and automating batch rendering within a Windows environment using PowerShell or Python.

This guide establishes a comprehensive development loop for automating TechSmith's screen capture and video editing processes (Snagit/Camtasia). It outlines the required project structure, includes PowerShell scripting examples, and details how to use Pester testing framework for robust COM API connection testing, ensuring reliable automation workflows.

Provides detailed guidance on rate limits and implementation patterns for automating TechSmith products like Snagit and Camtasia. Covers using the Snagit COM API, batch rendering via Camtasia Producer CLI, and scripting best practices using PowerShell and Python interop. Essential for screen capture and video workflow automation.

This guide provides the reference architecture and instructions for automating TechSmith workflows. It details using the Snagit COM API and Camtasia Producer CLI for screen capture, video editing, and batch rendering processes. The architecture emphasizes using PowerShell and Python interop (pywin32) to manage complex automation tasks effectively.

This guide provides essential security and implementation guidance for automating TechSmith's screen capture (Snagit) and video editing (Camtasia) workflows. It covers using the Snagit COM API, best practices for batch rendering with Camtasia CLI, and technical details for scripting in PowerShell and Python using pywin32. It also addresses common error handling scenarios like permission issues and file locking, ensuring robust automation.

This comprehensive guide provides detailed instructions for migrating and automating workflows involving TechSmith's Snagit COM API and Camtasia. It serves as a technical resource for advanced users, covering key considerations such as Windows-only requirements, scripting implementation using PowerShell and Python (pywin32), detailed input/output constant mappings, and essential error handling mechanisms for achieving robust screen capture and video editing automation.

Automate the Continuous Integration and Continuous Deployment (CI/CD) workflow for applications leveraging Together AI's OpenAI-compatible API. This skill sets up comprehensive GitHub Actions workflows to run unit tests (mocking completions and embeddings) on every Pull Request, and full integration tests (live API calls) upon merging to the main branch. It ensures robust validation of prompt formatting, model selection, and API connectivity for various LLMs.

A comprehensive reference guide for common errors encountered when interacting with the Together AI API. This includes error codes and solutions for inference, fine-tuning, and model deployment, covering authentication, rate limits, validation failures, and billing issues. Essential for developers integrating OpenAI-compatible LLM services.

A comprehensive workflow guide for fine-tuning open-source Large Language Models (LLMs) using the Together AI API. This process covers preparing structured training data (JSONL format), uploading the file, initiating the fine-tuning job, monitoring the training status, and finally deploying the custom model for inference. Ideal for developers needing to customize models for specific enterprise tasks, such as customer support or specialized content generation.