Skill UI

A comprehensive tool for training and evaluating advanced neural network models (including LSTM, Transformer, and N-BEATS) on time-series market data. It facilitates the prediction of financial trends, generating results with confidence intervals, and comparing performance across multiple architectures. Ideal for quantitative trading research and financial forecasting.

This skill utilizes hyperbolic space embedding (Poincare ball model) to represent complex data structures with inherent hierarchy, such as dependency trees, ontologies, or module taxonomies. Unlike standard Euclidean embeddings, hyperbolic space preserves hierarchical distances efficiently, allowing for accurate distance calculations (geodesic distance) that grow logarithmically with tree depth. It is ideal for advanced knowledge graph analysis and code architecture mapping.

This skill provides a rigorous framework for constructing high-quality microeconomic theory papers, adhering to the standards of top academic journals like AEJ: Micro. It guides users through defining minimal, motivated model primitives (players, actions, payoffs), selecting and justifying the correct equilibrium concept (e.g., Nash, SPBE), and choosing a robust proof architecture (e.g., duality, fixed-point). It ensures the resulting theorems are general, illuminating, and deeply motivated by economic principles.

This guide provides expert principles for designing and auditing system-level experiments in computer architecture (e.g., for ASPLOS-level conferences). It details the proper usage of various instruments—from real silicon and FPGAs to cycle-level simulators and analytical models—ensuring that every claim is matched to a valid, stated error model. It emphasizes workload selection, establishing strong baselines, performing rigorous ablation studies, and honestly reporting measurement noise and limitations to maintain scientific credibility.

A detailed model of the ACM/IEEE Design Automation Conference (DAC) research manuscript review process. This guide explains the double-blind, TPC-driven, and single-shot nature of the evaluation, emphasizing the critical balance between genuine novelty and measured Quality-of-Results (QoR). It outlines the high standards for evidence fairness, benchmark credibility, and ablation studies required for acceptance, differentiating DAC's approach from architecture and software venues.

This guide provides rigorous guidelines for structuring and validating experimental results in computer architecture papers. It covers essential aspects like declaring simulation methodology contracts, ensuring result reproducibility through manifest files, selecting representative workloads, and performing detailed attribution and sensitivity analyses to distinguish true architectural gains from modeling artifacts.

This guide provides a rigorous framework for evaluating computer architecture designs. It covers critical steps including selecting the appropriate simulation instrument (from analytical models to silicon measurements), constructing robust baselines, performing ablation studies, and reporting comprehensive metrics like geomeans and full overhead accounting to ensure scientific validity.

A comprehensive guide for microarchitecture researchers on achieving rigorous scientific reproducibility. It mandates tracking every critical artifact—including simulator commits, workload traces, power/area models, and RTL toolchains—and compiling them into a single run manifest, ensuring any reported result can be verified by a third party.

A comprehensive model detailing the entire academic review pipeline for microarchitecture papers, anchored to major conferences like MICRO. It outlines the timeline, from initial submission and double-blind review through the rebuttal/revision window, to final notification. The guide details what expert reviewers focus on—including mechanism novelty, methodology credibility, and cost honesty—providing authors with actionable strategies for strengthening their work through the academic publication lifecycle.

This skill is designed for building complex, audit-ready financial models in Excel. Use it for 3-statement projections, DCF valuation, LBO analysis, unit economics, or advanced scenario planning. It enforces professional standards like the three-zone architecture (Inputs, Calculation, Outputs), strict formula discipline, and robust auditing gates, ensuring the output is suitable for high-stakes decision-making.

A comprehensive guide for designing conceptual figures for highly theoretical manuscripts (e.g., Psychological Review). This skill teaches how to create two types of non-experimental exhibits: structured model/architecture diagrams and simulation-of-model-behavior plots. It emphasizes rigorous academic standards, including defining constructs vs. processes, distinguishing predictions from fits, and ensuring figures are self-contained and diagnostic.

This skill generates fully valid Microsoft Threat Modeling Tool (.tm7) files. It translates complex system architectures, including components, data stores, data flows, and external actors, into the required WCF DataContractSerializer XML format. Use it for structured STRIDE threat modeling outputs compatible with Microsoft's official tool.