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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 production-ready patterns for automating TechSmith tools using PowerShell and COM API. It features a Snagit COM API factory pattern for consistent screen capture and a batch rendering function for Camtasia. Use these patterns to build robust, automated content pipelines for screen recording and professional video production.

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.

Provides comprehensive guidance for integrating TechSmith products (Snagit and Camtasia) into automated workflows. This skill focuses on utilizing TechSmith webhooks and the Snagit COM API, enabling batch rendering, screen capture integration, and advanced video editing automation within scripting environments like PowerShell and Python.

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 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.

This workflow guides users through the complete lifecycle of customizing large language models (LLMs). It covers uploading training data, initiating fine-tuning jobs, monitoring training progress, managing model versions, and finally deploying the optimized model to a dedicated, autoscaling inference endpoint using the Together AI API. Ideal for production-level customization.

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.

This utility handles asynchronous webhook events from Together AI, enabling developers to build robust, event-driven Machine Learning pipelines. Instead of constantly polling the API for status updates (e.g., fine-tuning completion, batch job status, model deprecation), you can subscribe to these webhooks. It includes logic for signature verification, event routing, and idempotency, making it ideal for mission-critical AI workflows.

This internal agent skill defines the structured procedure for displaying bug triage results in terminal markdown format. It generates comprehensive summaries of bug clusters, provides detailed views of evidence and reports, and handles the parsing and execution flow for user review commands (e.g., `details`, `file`, `dismiss`, `escalate`). It is a core component for automated bug triage and defect management workflows.