Skill UI

This utility is designed to facilitate the migration process between various versions of the WebContainer API and updates within the StackBlitz SDK. Use this skill specifically when working on projects that rely on WebContainers or the StackBlitz development environment, ensuring smooth, compatible upgrades to the latest framework versions.

This workflow automates the creation of step-by-step documentation guides using the TechSmith Snagit COM API and PowerShell. It first captures a series of sequential screenshots for a guide, and then uses a batch process to open and annotate these images within the Snagit Editor, ensuring consistency and efficiency in creating professional tutorials.

This guide details the deploy integration process for automating screen capture and video editing using TechSmith's Snagit COM API and Camtasia Producer CLI. It is designed for developers needing to orchestrate complex content pipelines, such as programmatically capturing specific screen regions, handling various output formats (clipboard, file, printer), and automating batch rendering within a Windows environment using PowerShell or Python.

Provides comprehensive instructions for installing, registering, and interacting with the TechSmith Snagit and Camtasia COM Server API. This guide covers setup for COM interop using PowerShell, Python, and C#, allowing developers to automate screen capture and video processing workflows without traditional API keys.

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.

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 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.

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.

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.

A comprehensive reference architecture for building scalable, cost-effective, and robust AI services. This system manages the full AI lifecycle, including intelligent model routing (for quality/cost trade-offs), response caching (using Redis), asynchronous batch processing, and fine-tuning pipeline management via Together AI's OpenAI-compatible API. Ideal for enterprise applications requiring high availability across multiple open-source models.

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.

unslop is a powerful command-line interface (CLI) tool designed for post-processing text. It programmatically strips out common and identifiable patterns associated with AI-generated writing. Use it as a final, deterministic pass before publishing documentation, blog posts, or any content derived from large language models (LLMs), ensuring a more natural and human-like tone.