Skill UI

Auto-activating ML training skill that kicks in whenever you mention optuna study creator, then guides you through data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and validations.

This technique guides users on how to construct highly specific search queries for academic papers. Instead of relying solely on broad topic keywords, users should prioritize combining concrete numerical identifiers—such as exact sample sizes, population percentages, concentration levels, or specific year ranges—with methodological terms and discipline keywords. Using these numerical anchors significantly increases the uniqueness and precision of the search results, helping to pinpoint the exact study required.

A comprehensive academic advisor covering the entire university journey. It guides students through major selection, GPA management, graduate school applications (Chinese and Western systems), study abroad planning, and career transition. Methodology includes Socratic questioning and structured decision-making to help users make informed, autonomous academic choices.

Activate this skill when students provide study materials and a syllabus, helping them prepare for exams. It extracts highly structured, exam-ready content, including key definitions, crucial points, keywords, diagrams, sample answers, and practice questions, ensuring all information is strictly derived from the provided notes to maximize study efficiency.

This skill guides users through structured, adaptive learning experiences for any topic. It diagnoses the user's current knowledge level and goal to create a personalized study path. It utilizes techniques like worked examples, retrieval questions, and spaced repetition to ensure deep understanding, making it ideal for self-study, skill acquisition, or subject review.

This skill generates compact, standalone, multi-lesson course artifacts suitable for study guides or mini-courses. It structures content with clear lesson plans, learning objectives, flashcards, quizzes, and source links, creating a self-contained, browser-ready learning experience optimized for knowledge retention.

This comprehensive skill guides the creation of high-quality technical content specifically tailored for developers. It covers the entire devrel content lifecycle: from researching topic validation (community signals, competitor gaps) to selecting the ideal content type (tutorial, guide, case study). The framework emphasizes deep technical accuracy, structured outlines (Hook, Meat, CTA), and writing runnable, copy-paste ready code examples to build trust and drive adoption.

This tool generates a complete, structured blueprint for developing powerful case studies and success stories. It guides users through defining the Challenge-Solution-Results (CSR) narrative, creating detailed client and internal interview questions, planning data visualizations, and mapping out multiple output formats (white paper, video, web page). This systematic approach ensures content is highly credible, optimized for sales enablement, and ready for multi-channel distribution.

This guide helps researchers strategically position their manuscripts for top-tier venues like ACM DIS. It provides deep insight into the required scope, contribution types (e.g., design artifacts, methods, theory), evidence standards (e.g., controlled study, field deployment), and necessary framing adjustments to ensure the work aligns perfectly with the conference's community expectations. Use this tool for venue selection, manuscript re-framing, and submission strategy.

This guide teaches researchers how to structure a high-impact literature review for top-tier academic journals. It moves beyond merely summarizing recent empirical studies, focusing instead on establishing a deep theoretical lineage (e.g., earnings quality, audit theory), identifying specific, non-obvious research gaps, and clearly positioning the current study's unique contribution within the established academic discourse.

A comprehensive guide for authors preparing empirical research manuscripts (especially in accounting). It details best practices for structuring main tables (keeping them lean), transforming statistical coefficients into interpretable economic magnitudes, and properly visualizing dynamic effects using event study graphs. It emphasizes moving beyond simple significance stars to provide deep academic context.

A comprehensive guide for formatting and presenting empirical results (tables and figures) for submission to the American Economic Journal: Applied Economics (AEJ: Applied). It ensures that main causal estimates are immediately legible to referees by adhering to rigorous academic standards, including presenting standard errors, confidence intervals, and specific exhibit structures like event-study plots and balance tables.