Skill UI

This skill performs a structured Systematic Literature Review (SLR) by analyzing multiple academic papers on a given topic. It searches academic databases (like arXiv), extracts structured metadata (research questions, methodologies, key findings, limitations) from the entire set of papers, and synthesizes overarching themes and research trends. It is ideal for creating comprehensive literature surveys, annotated bibliographies, and cross-paper comparisons, outputting reports in professional citation formats like APA, IEEE, or BibTeX.

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 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.

This skill demonstrates how to automate core content creation operations using industry-standard tools. It utilizes the Snagit COM API to perform batch screenshot and video captures, and leverages command-line execution (CamtasiaProducer) to batch-render and export video projects (e.g., .tscproj to MP4). Ideal for building robust documentation pipelines, automating content workflows, or integrating TechSmith tools into larger scripting systems.

This guide provides comprehensive technical guidance for performance tuning and automating workflows using TechSmith Snagit COM API and Camtasia. It details key considerations for robust scripting, including using PowerShell and Python interop, defining various input/output types (like desktop, window, clipboard, or file), and solving common errors (such as COM registration failures or permission issues). Use this skill when building complex, automated pipelines for screen capture and video editing.

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.

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 skill provides a production-grade, containerized integration service for connecting to the Together AI platform. It facilitates high-performance inference, fine-tuning, and model deployment across over 100 open-source models using Together's OpenAI-compatible API. Includes comprehensive Docker setup, environment variable management, health checks, and zero-downtime rolling update strategies for robust, real-time LLM serving.

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.

Provides comprehensive guidance for performance tuning, inference, and fine-tuning using the Together AI platform. This skill supports working with Together AI's OpenAI-compatible API, covering model deployment, utilizing various open-source models (like Llama, Mixtral), and implementing best practices for efficient and cost-effective batch inference.