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 engineers and researchers to publish, manage, and link research papers directly on the Hugging Face Hub. It supports creating dedicated paper pages, linking papers to corresponding models and datasets, claiming authorship, and generating professional, markdown-based research articles with LaTeX support. Use it to streamline the entire academic publishing workflow from initial draft to final 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.

Improve a compiled academic paper through iterative LLM review, fix implementation, and recompilation across two rounds, using GPT-6-Astra via Codex with a fresh reviewer each round to avoid score inflation. Polishes writing quality, fixes theoretical inconsistencies, softens overclaims, and improves presentation after initial paper generation.

Autonomous full research pipeline that takes a general research direction through idea discovery, experiments, review, and writing to a polished submission-ready paper PDF. Use for complete AI/ML research lifecycle workflows from ideation to submission, including resume support and cross-model quality review.

Get a multi-round adversarial critical review of your research ideas, papers, or experimental results from an external LLM reviewer backend (Codex or manual). Compiles a briefing, runs ultra-reasoning review rounds, and returns a verdict with logical gaps, missing experiments, and narrative weaknesses.

Designs systematic ablation studies for ML paper submissions. Helps isolate component contributions, test hyperparameters, and answer reviewer questions. Codex plans experiments while CC implements them, ensuring rigorous validation before submission.

This skill provides comprehensive analysis and structured data extraction for academic research papers hosted on Hugging Face and arXiv. It can read paper content in markdown format, retrieve key metadata (authors, abstracts, GitHub repos), and identify linked assets such as associated models, datasets, and Spaces. Use this when the user provides a Hugging Face or arXiv URL, or explicitly requests a summary or explanation of an AI research topic.

This sophisticated tool generates publication-quality AI illustrations and diagrams for academic papers. It implements a multi-stage, iterative workflow, using advanced LLMs (like Claude and Gemini) to parse user requests, optimize complex layouts, verify academic styling (e.g., CVPR/NeurIPS standards), and render high-fidelity figures. Ideal for researchers needing professional, complex visuals for their manuscripts.

Workflow to parse external paper reviews, atomize reviewer concerns, and draft a grounded, venue-compliant text rebuttal under strict limits. Supports multiple reviewers, follow-up rounds, and safety checks to avoid fabrication and overpromising. Use when the user needs to reply to reviewers or run an ICML/OpenReview rebuttal.