Skill UI

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.

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 technique is used to locate a specific TV series by leveraging the known filmography of its creator (director, writer, or showrunner). When the question provides clues that the creator also worked on a distinct, identifiable project (e.g., a trilogy or an award-winning miniseries), use that secondary work as a powerful search anchor. Identify the person first using their other works, then trace back through their complete credits to pinpoint the target series.

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 a structured framework for capturing, organizing, and maintaining persistent facts about the user. By segmenting facts into topic-specific files (e.g., coding preferences, tech stack, goals), it creates a reliable, objective knowledge base. This structure is crucial for AI agents and applications to achieve highly personalized interactions and provide accurate context.

Comprehensive guidance for integrating with the Veeva Vault platform. This skill covers using the REST API, executing VQL queries, and utilizing the VAPIL Java SDK for managing documents and automating clinical operations. Ideal for developers working in the life sciences and pharmaceutical industry who need deep access to Veeva Vault data and workflows.

Provides enterprise-grade data handling patterns tailored for Veeva Vault deployments. This guide is essential for professionals managing data operations in regulated life sciences environments. It covers best practices for advanced data retrieval using VQL, API integration, and ensuring compliance when writing, reading, or editing critical enterprise data.