Skill UI

Provides comprehensive guidelines for structuring and writing academic papers for Automated Software Engineering (ASE) conferences. It teaches authors how to present an automated task, emphasize the automation design over the deep learning model, provide evidence from real subjects, and adhere to strict formatting and argument presentation rules to maximize acceptance chances.

A comprehensive guide for hardening deep learning papers, particularly in Computer Vision (CVPR). It details best practices for achieving rigorous reproducibility by utilizing the Compute Reporting Form (CRF), maintaining a detailed recipe ledger, ensuring benchmark split hygiene, and meticulously documenting all training hyperparameters, seeds, and variance analyses for academic publication.

A comprehensive guide for academic researchers detailing how to achieve and disclose full reproducibility for Computer Vision and deep learning models. Covers essential practices such as protocol pinning for foundation models, detailed logging of datasets, hardware specifications, and variance honesty, ensuring results remain verifiable years after publication.

This guide helps researchers determine if their Machine Learning project is suitable for ICLR, or if a different top-tier conference (such as NeurIPS, ICML, or CVPR) is a better fit. It emphasizes the need for deep representation-learning insights, mechanistic understanding, and theoretical contributions over mere application performance.

A comprehensive guide for authors submitting AI/ML papers to the International Conference on Learning Representations (ICLR). It helps authors position the work, optimize framing, assess evidence strength, and ensure the manuscript adheres to the rigorous standards of deep learning and representation learning required for a top-tier, open-review conference.

This guide details advanced best practices for achieving scientific reproducibility in probabilistic modeling and deep learning research. It mandates rigorous disclosure of seeds, hyperparameters, diagnostic traces (e.g., R-hat, ELBO), and compute environments to ensure that all claims made in academic papers are verifiably backed by traceable evidence. It elevates reproducibility from a simple code run to a comprehensive audit.

This skill provides a structured way to understand complex codebases, functions, or architectural concepts. It allows the user to specify the depth of explanation (Simple, Standard, Deep), ensuring the output covers purpose, step-by-step flow, key decisions, and trade-offs for maximum learning efficiency.

This tool tests your deep understanding of code you have recently written or reviewed. It generates structured, multi-level questions (Recall, Understanding, Application, Analysis, Synthesis) based on the provided code. Users can focus the quiz on specific areas like error handling, performance, or security, making it an excellent mechanism for continuous learning and solidifying technical knowledge.

This tool provides a structured, multi-stage method for learning any technical concept, from basic definitions to advanced implementation details and common pitfalls. It progressively deepens the explanation, making complex topics easy to grasp.

Evidence-first deep reading skill. Extracts claims, evidence, assumptions, and counterarguments from books, articles, PDFs, and document sets; builds argument trees, evidence ledgers, and knowledge maps; supports Feynman-based learning and recall. Ideal for in-depth comprehension and durable learning.

A comprehensive companion tool for the foundational Deep Learning textbook. It indexes 20 chapters and provides a crucial delta layer, updating core concepts from 2016 to 2026 (e.g., Transformers, Diffusion Models). The tool features four deterministic utilities: a reading-path planner, a training-failure diagnostic, a capacity planner, and a parameter/FLOP calculator. Ideal for students and practitioners who need to study the theory, understand its modern relevance, and translate academic knowledge into actionable engineering decisions.

A comprehensive knowledge base covering the theoretical fundamentals and advanced algorithms of Deep Reinforcement Learning (Deep RL). It details core concepts like Bellman equations, policy gradients, and the trade-offs between policy optimization and Q-learning. The guide analyzes leading algorithms such as PPO, SAC, TRPO, and DDPG, and provides rigorous best practices for debugging and running scientifically sound RL experiments.