Skill UI

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.

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 skill analyzes a live website or application to understand its underlying business model, data collection practices, and user interactions. It then generates a comprehensive, 16-section Terms of Service document, automatically incorporating provisions for GDPR, CCPA, and other major compliance frameworks. Ideal for launching new SaaS, e-commerce, or mobile products.

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 synthesizes a comprehensive Product Requirements Document (PRD) from the current conversation context and understanding of the codebase. It guides the user through structuring the problem statement, defining detailed user stories, outlining implementation decisions, and suggesting test strategies. The final PRD is formatted and submitted directly as a GitHub issue, ensuring a structured handover from discussion to development specification.

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 guide provides a comprehensive tutorial on running various AI inference tasks using the Together AI API, which is compatible with the OpenAI standard. It demonstrates key functionalities including chat completions, streaming responses, image generation, and creating embeddings. This is ideal for developers who need to test open-source models, compare performance across different LLMs (like Llama or Mixtral), or integrate powerful generative AI features into their applications using Python or Node.js.

This comprehensive checklist guides the operational setup (MLOps) for AI applications using Together AI's OpenAI-compatible API. It covers critical production concerns, including secure API key management, implementing robust rate limit handling, building error resilience features (circuit breakers, exponential backoff), and setting up comprehensive monitoring for latency, cost control, and model availability. Essential for reliable, scalable LLM deployment.

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 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.

TweetClaw provides comprehensive automation capabilities for X/Twitter using the Xquik REST API. It allows users to perform full read and write operations—including posting, replying, liking, following, and sending DMs. Key features include advanced tweet searching, bulk data scraping (23 tools), account monitoring, running structured giveaways, and composing algorithm-optimized content. Ideal for social media managers, digital marketers, and data analysts needing deep platform insights.