Skill UI

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.

Automates PyTorch training workflows by guiding data preparation, model configuration, hyperparameter tuning, and experiment tracking while following industry best practices for production readiness.

This skill automates the entire academic research lifecycle. It orchestrates stages from broad idea discovery and literature review to experimental implementation, rigorous cross-model review, and finally, generating a polished research paper PDF. Ideal for researchers needing an end-to-end, autonomous workflow for academic publication preparation.

Generates comprehensive, long-form cheat sheets for advanced Machine Learning and Large Language Model (LLM) topics. These tutorials are highly structured, including mathematical derivations, from-scratch PyTorch implementations, detailed comparison tables, and 25 categorized high-frequency interview questions (L1, L2, L3). Ideal for deep academic study and technical interview preparation.

Provides comprehensive guidelines for authors preparing supplementary materials for conference submissions (AAMAS). It covers structuring full proofs, extended game definitions, extra experiments, and anonymized code assets, ensuring the main paper remains comprehensible while maximizing the impact of supporting evidence.

This comprehensive guide assists authors in strategically positioning their computer vision manuscripts for the European Conference on Computer Vision (ECCV). It covers conference fit assessment, manuscript re-framing, required evidence (e.g., ablations, baselines, failure modes), and structural advice to ensure the work aligns with ECCV's scope and current review policies. Use this tool for venue selection and submission preparation.

This skill serves as a comprehensive router for managing the entire lifecycle of an ICML manuscript. It guides users through every phase, including topic selection, drafting, artifact preparation, OpenReview submission, double-blind review, author response, and final camera-ready publication. It helps track risks, evidence, and official conference policies.

Provides a comprehensive, detailed audit checklist for authors submitting papers intended for NAACL. This guide walks through the complex ACL Rolling Review (ARR) submission cycle, ensuring the manuscript passes all mandatory technical gates—such as anonymity checks, format compliance, structural requirements, and accurate declaration—before the final conference commitment. Essential for navigating the unique timeline and rigorous standards of top NLP conferences.