Skill UI

A comprehensive studio workflow for generating professional, multilingual dubbing for local audio and video assets. This skill manages the entire process, including media submission, complex localization parameters (target locale, accent, language), real-time status polling, and final artifact download. It handles critical considerations like copyright checks, speaker consent, and preview generation to ensure high-quality, contextually appropriate dubbing across diverse languages.

A comprehensive tool for generating high-quality audio content, including instrumental background music (BGM), jingles, ambient beds, and full songs with sung vocals. It supports complex creative parameters such as mood, tempo, vocal style, and target language, ensuring original and copyright-safe output through specialized generation models.

A comprehensive studio for professional voice conversion. This tool allows users to transform local audio recordings into specific, authorized target voices. It manages complex workflows, requiring rigorous checks on source rights, target voice licensing, and detailed parameters like emotion, accent, pace, and language locale, ensuring compliance and high-quality output.

This professional tool helps developers and researchers identify the optimal AI model for a specific task, such as coding, reasoning, or image recognition. It queries official HuggingFace leaderboards, filters models based on hardware constraints (e.g., available VRAM or RAM), and provides a detailed comparison table including benchmark scores, parameter size, and licensing information.

This skill guides the architectural design of an Itô Data Atlas agent for advanced quantitative research. It facilitates market discovery, constructs candidate prediction-market baskets, drafts necessary parameter changes, and integrates human-in-the-loop review. It is strictly for workflow and architectural planning, and does not execute live trading orders.

A guide for researchers submitting to conferences like ACM Multimedia, detailing how to achieve robust result reproducibility. It covers the necessary steps—including logging code commits, data versions, media preprocessing parameters, and hardware details—to allow independent reviewers to successfully rebuild the reported findings and achieve official reproducibility badges.

A comprehensive guide detailing best practices for designing and auditing empirical evaluations of Large Language Models (LLMs). It addresses critical pitfalls such as data contamination, baseline unfairness, model version drift, and decoding parameter sensitivity, ensuring that published results are robust, reproducible, and scientifically sound for academic publication.

A comprehensive guide for hardening deep learning papers, particularly in Computer Vision (CVPR). It details best practices for achieving rigorous reproducibility by utilizing the Compute Reporting Form (CRF), maintaining a detailed recipe ledger, ensuring benchmark split hygiene, and meticulously documenting all training hyperparameters, seeds, and variance analyses for academic publication.

This comprehensive checklist is designed for auditing NLP research papers, particularly for major conferences like EACL. It serves as a claims audit, ensuring that every reported result, claim, or finding—including hyperparameters, compute costs, verbatim prompts, data sources, and significance testing—is fully documented and reproducible. It emphasizes academic rigor and transparency in AI research.

A comprehensive guide for authors submitting computer vision papers (e.g., to ECCV). It outlines best practices for ensuring the full reproducibility of AI research, covering everything from detailed training recipes and hyperparameter logging to pinning foundation model dependencies and reporting variance honesty. Essential for academic publication integrity.

This guide provides comprehensive standards for designing, auditing, and documenting AI experiments for major conferences like IJCAI. It covers establishing strong baselines, conducting ablation studies, and reporting meticulous details on hyperparameters, computational infrastructure, and ethical risks, ensuring maximum reproducibility and academic credibility for AI research.

This guide details advanced best practices for achieving scientific reproducibility in probabilistic modeling and deep learning research. It mandates rigorous disclosure of seeds, hyperparameters, diagnostic traces (e.g., R-hat, ELBO), and compute environments to ensure that all claims made in academic papers are verifiably backed by traceable evidence. It elevates reproducibility from a simple code run to a comprehensive audit.