Skill UI

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 guide details how to effectively delegate complex, multi-step tasks to various specialized AI agents (Codex, Pi, Claude, Hermes). It covers best practices for maintaining conversation context, handling TUI prompts (e.g., avoiding newlines, using quotes), techniques for reliable polling, and choosing the optimal agent for coding, design, or general purpose work. Essential for advanced AI workflow automation.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

This tool executes a GEPA learning cycle, automating the evolution and optimization of policy prompts against a scored task corpus. Instead of relying on manual prompt iteration, it measures performance improvements, identifying the best working 'harness genome.' It is ideal for measuring sustainable improvements in task performance and supports a free, cost-estimating dry-run mode before committing to actual model calls.

This skill provides guidance on implementing common architectural patterns and best practices within the PyTorch framework. It is designed for developers building sophisticated deep learning models, helping to improve structure, efficiency, and code maintainability when working with neural networks.

This comprehensive guide provides authors with best practices for preparing supplementary artifacts for AAAI submissions. It outlines how to structure technical appendices, package reproducible code and data ZIPs, maintain strict anonymity (by removing author metadata and paths), and maximize the evidence for reproducibility. The goal is to ensure that external reviewers can easily verify the research claims without specialized or private tools.

This comprehensive guide helps researchers strengthen the reproducibility of AI academic papers for top-tier conferences like AAAI. It outlines best practices for creating a claim-to-evidence ledger, documenting seeds, hyperparameters, compute costs, and dataset licensing. Use this to ensure your work meets the highest standards of academic rigor and verifiability.

A comprehensive guide for authors preparing submissions for ACL conferences. It details how to effectively split content across the main body, required Limitations section, optional Ethics statement, and supplementary archives. Learn best practices for ensuring critical findings remain visible to reviewers, maximizing reproducibility, and structuring appendices for maximum impact.

A specialized guide for authors targeting the ACM Conference on Fairness, Accountability, and Transparency (FAccT). It helps authors strategically frame their responsible AI research, ensuring claims are rigorously grounded in both technical measurement and social context. Covers necessary evidence bars, scope calibration, and submission best practices for this niche venue.

A comprehensive guide for authors submitting papers to the ACL conference. It assists in positioning the manuscript, verifying topic fit (NLP, language models, semantics), and ensuring the evidence meets the high standards required for top-tier computational linguistics venues. Covers structure, best practices, and submission checklists.

This comprehensive guide outlines best practices for achieving rigorous reproducibility in complex AI and knowledge graph research. It details how to pin every component—from tokenizers and KGs to LLM prompts and environment settings—ensuring that results are fully documented and verifiable by peers, even when proprietary data is involved.

A comprehensive guide for authors on best practices for packaging, documenting, and ensuring the reproducibility of LLM artifacts (weights, data, code, prompts, etc.). It addresses requirements for anonymous academic review and public post-acceptance release, focusing on reproducibility, licensing, and transparency.