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.

This mode acts as a structured thinking partner, guiding users through ambiguous problems, complex decisions, and open-ended strategy questions. Instead of providing direct answers, it focuses on challenging assumptions, asking clarifying questions, surfacing multiple perspectives (e.g., stakeholder views), and helping the user structure their thought process using advanced techniques like reframing, pre-mortem analysis, and identifying critical constraints. Ideal for product managers and leaders who need to explore ideas without a clear path forward.

TileDB-VCF is a high-performance library built on TileDB's sparse array technology, designed for the efficient storage and retrieval of large-scale genomic variant-call data (VCF/BCF). It supports scalable, incremental ingestion of single-sample VCF/BCF files and enables highly efficient parallel querying across vast genomic regions and multiple samples. This tool is essential for building robust population genomics databases, conducting GWAS, and performing large-scale cohort studies.

This skill provides a comprehensive framework for security auditing LLM agents that possess external tools (file system access, command execution, network calls). It employs advanced dialogue probing techniques—such as command injection, path traversal, and SSRF attempts—to identify vulnerabilities where the agent might be induced to execute unexpected or malicious code, or access sensitive resources. Essential for red teaming and securing agent functionalities.

This technique guides users on how to use visual descriptions from music videos (MV), such as specific scenes, emotional themes (grief, loss), or real-world events, as powerful search anchors. Instead of only searching by title or artist, users can extract keywords from the visual narrative to refine search queries, significantly increasing the chances of identifying the correct song or tracing its origin. Ideal for music research and cultural analysis.

This technique is designed for the final verification stage when analyzing a candidate TV series. Instead of accepting initial matches, users must perform targeted searches combining the show name and specific character details (e.g., birth year, specific plot event, relationship). This ensures that the candidate show satisfies all complex constraints, eliminating false positives and confirming the accuracy of the plot details.

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 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.