Skill UI

Moltbook AI Social Hub is a community-driven social network where registered agents post, comment, upvote, and join submolts, powered by the Moltbook API for secure authentication and heartbeat reminders.

This skill is designed for running comprehensive evaluations of Hugging Face Hub models on local hardware. It supports multiple evaluation frameworks, including `inspect-ai` and `lighteval`, and provides flexibility in backend selection (e.g., `vllm`, Transformers, or `accelerate`) to optimize for performance and compatibility. Use this for local GPU benchmarking, smoke testing, and backend selection, but note that it does not handle remote job orchestration or result publishing.

Discover, download, and manage reusable AI agent skills from the AionUI Skills registry, making it easy to augment autonomous assistants with community-built capabilities whenever a task benefits from specialized functionality.

This guide helps authors audit the novelty and eligibility of multiagent system research for submission to AAMAS. It provides comprehensive advice on positioning the work against literature spanning multiple top-tier venues (e.g., NeurIPS, ICML, AAAI, EC, JAAMAS), integrating concepts from multiagent RL, game theory, and social choice. It ensures submissions are positioned accurately compared to the broader academic community, maximizing impact and minimizing rejection risk.

A comprehensive strategic guide for authors targeting the Conference and Labs of the Evaluation Forum (CLEF). It helps frame IR/NLP research by defining the appropriate contribution type, evidence bar, and rigorous evaluation methods required for top-tier academic submission, ensuring the paper aligns with the venue's specific community expectations.

This comprehensive guide outlines best practices for packaging and releasing scientific artifacts—including code, training configurations, demonstration data, and policy checkpoints—for robotics research papers. It details how to prepare materials for anonymous reviewer assessment and for durable public release, ensuring the work is fully reproducible by the academic community.

This guide provides a comprehensive strategy for authors submitting NLP and Computational Linguistics papers to EACL. It covers conference positioning, manuscript re-framing from journal or arXiv styles, and establishes rigorous evidence standards. It helps authors ensure their work aligns with the European ACL community focus, optimizing the paper's narrative structure for high acceptance rates.

A comprehensive guide for academic authors detailing how to establish and maintain rigorous reproducibility in Machine Learning research papers (e.g., ICLR). It instructs on mapping every central claim to verifiable evidence, including recording seeds, variance, specific compute budgets (training vs. inference), data preprocessing scripts, and ethical considerations. The statement must function as a public contract for the scientific community.

A comprehensive guide for authors submitting papers on 3D reconstruction, geometry, depth, and neural rendering to top-tier conferences like 3DV. It helps authors strategically position their work, refine the narrative, structure evidence, and measure fit against specific academic community expectations.

This skill is a strategic guide for authors submitting to top-tier AI conferences, specifically the International Conference on Principles of Knowledge Representation and Reasoning (KR). It helps users assess manuscript fit, framing, and evidence requirements. It provides deep guidance on the required scope, such as formal semantics, nonmonotonic reasoning, and ontologies, ensuring the paper is positioned correctly within the specialized KR community. Ideal for pre-submission strategy and manuscript re-framing.

A comprehensive guide for NLP researchers to determine the ideal conference venue (NAACL, ACL, EMNLP, etc.) for their paper. It analyzes timing, target audience fit, and the unique regional focus of NAACL (Nations of the Americas) to ensure the work is submitted to the most appropriate and receptive community.

A skill that captures Guixingren's AI technology media creation mindset, featuring Silicon Valley first-line perspectives, China-US dual-line narratives, rapid news aggregation structure, and AI community productization operations. Suitable for AI industry news writing, Silicon Valley reporting, China-US AI comparison analysis, developer tool reviews, and AI community operations, including hackathons, podcasts, and offline events.