Skill UI

Leverages the Exa API for semantic/embeddings search, discovering similar content, or filtering by categories like companies, people, and research papers, making research and knowledge retrieval more targeted.

Guides researchers through drafting publication-ready machine learning papers for NeurIPS, ICML, ICLR, ACL, AAAI, and COLM by combining expert writing philosophy with LaTeX templates, citation verification APIs, and collaborative workflows.

This skill provides comprehensive tools for AI researchers and engineers to publish, index, and manage academic papers directly on the Hugging Face Hub. It supports automatic arXiv indexing, linking papers to specific models and datasets, verifying authorship, and generating professional, markdown-based research articles. It streamlines the entire scientific publication workflow from creation to professional publication.

Python toolkit that queries the arXiv Atom API to search papers by keywords, authors, categories, or IDs, filter results, download PDFs, and emit structured JSON for AI/ML literature reviews and monitoring.

This skill orchestrates end-to-end autonomous AI research projects using a sophisticated two-loop architecture. It manages the entire research lifecycle, from initial literature review and hypothesis formation to running constrained experiments and synthesizing findings into publishable papers. It acts as a project manager, autonomously driving the process while structuring the workspace and presenting progress to human users.

This skill automates the iterative process of academic paper refinement, simulating the rigorous peer-review cycle of top-tier conferences (e.g., NeurIPS, ICML). It connects with an external LLM (MiniMax) to receive multi-round, critical feedback, tracks state persistence, and guides the user through necessary revisions until the research is deemed submission-ready. Ideal for maximizing the quality of technical writing.

This skill transforms a raw research proposal or method idea into a rigorous, paper-ready experimental roadmap. It guides the user through defining core claims, identifying necessary ablations (e.g., novelty, simplicity, frontier necessity), and structuring experiments into actionable, phased stages (Sanity, Baseline, Main, Decision). Ideal for advanced academic research in AI/LLM/VLM fields that requires strong validation and systematic reporting.

This autonomous skill orchestrates the entire scientific research lifecycle, guiding users from a vague research direction to a polished, review-ready document. The pipeline systematically executes Idea Discovery (Workflow 1), full-scale Implementation, Experiment Deployment (Workflow 2), automated Review Loops, and optional Paper Writing (Workflow 3). It handles everything from preliminary literature review and novelty checks to generating high-quality, reproducible code and final academic drafts.

This end-to-end workflow automates the transition from a vague research direction to a structured, actionable proposal. It first refines the core method thesis and dominant contribution (using research-refine), and then automatically generates a detailed, claim-driven experiment roadmap and comprehensive pipeline summary. Ideal for researchers needing to transform initial concepts into a complete, executable paper proposal and experimental plan.

A comprehensive tool and API wrapper for accessing, parsing, and analyzing research papers published on Hugging Face and arXiv. Users can input various URLs or IDs (e.g., arXiv ID) to retrieve structured metadata, full markdown content, and automatically link associated resources such as models, datasets, and application spaces. Ideal for researchers and developers needing structured access to cutting-edge AI knowledge.

This tool automates the generation of high-quality, publication-ready academic illustrations and diagrams. It employs a sophisticated, multi-stage, iterative workflow supervised by Claude, covering prompt planning, layout optimization, style verification (CVPR/NeurIPS standards), image rendering, and final strict visual review. Use it when generating complex figures for research papers.

This skill guides the entire process of writing a research paper rebuttal following submission to a major conference. It parses complex, multi-reviewer feedback, structures responses according to strict venue rules (e.g., text-only, per-reviewer threads), and ensures all claims are grounded in the original paper, preventing fabrication or overpromising. It manages follow-up rounds and maintains academic rigor.