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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.

This workflow automates complex video production tasks using the TechSmith Camtasia Producer COM API via PowerShell. It enables users to perform batch rendering, manage various output presets, apply custom watermarks/branding, and export projects in multiple formats (e.g., MP4, M4A) without manual intervention. It is ideal for content creators, educators, and businesses managing a large volume of video content efficiently.

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 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.

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.

This tool provides a local development loop for integrating with Together AI's OpenAI-compatible API. It mocks key functionalities like chat completions, embeddings, and model listing, allowing developers to build and test AI applications rapidly without consuming live API credits. Developers can toggle between mock mode for rapid iteration and live mode for final model evaluation, ensuring a seamless workflow.

A comprehensive guide and implementation demonstrating how to handle API rate limits when interacting with AI services like Together AI. It details the token bucket algorithm for client-side throttling and provides robust retry mechanisms (exponential backoff) to manage 429 (Rate Limit Exceeded) and 5xx server errors, ensuring stable, high-throughput batch processing.

This guide covers essential security best practices for utilizing Together AI's OpenAI-compatible API for inference, fine-tuning, and model deployment. It provides code examples and architectural advice on secure API key management, robust webhook signature verification, input validation using schemas (like Zod), and implementing data redaction pipelines to protect sensitive information in logs and datasets. Essential for building secure AI applications.

This skill guides a systematic process for debugging reported issues. It explores the codebase to identify the root cause, traces the code path, and then generates a comprehensive GitHub issue. The fix plan strictly follows the Test-Driven Development (TDD) Red-Green cycle, ensuring proposed solutions are robust and durable, surviving major refactors.

This guide outlines best practices and structured workflows for automating complex tasks within the Unity Editor using the Model Context Protocol (MCP). It provides programmatic methods for managing GameObjects, applying script edits, analyzing the editor state, executing batch commands, and capturing detailed, multi-view screenshots. Ideal for developers needing robust, repeatable, and automated testing or operational tasks within Unity projects.

This skill provides enterprise-grade observability patterns specifically for Veeva Vault deployments. It guides users through implementing advanced patterns, utilizing VQL for efficient data retrieval, and ensuring compliance in regulated life sciences environments. Use this when building robust, observable applications within the Veeva ecosystem.