Skill UI

A comprehensive suite for running advanced sensing applications using RuView hardware. It supports multi-modal data fusion from CSI, Wi-Fi, and mmWave sensors to perform critical tasks such as occupancy detection, vital sign monitoring (heart rate, breathing), pose estimation, environment mapping, and mass casualty assessment. Ideal for prototyping sophisticated IoT and smart building solutions.

This tool provides a holistic, portfolio-level quality assessment by evaluating multiple content pieces (assets, libraries, or deliverables) simultaneously. Instead of reviewing content one by one, it processes the entire set to generate comprehensive rankings, dimension breakdowns (e.g., brand voice, claim verification), systemic issue reports, and a prioritized revision list. Ideal for content audits, pre-launch checks, and large campaign reviews.

Generates a C-suite ready executive summary of marketing performance. It moves beyond operational data, synthesizing strategic wins, risks, and recommendations using core business metrics like ROI, CAC, and LTV. Ideal for board reports, portfolio ROI reviews, and strategic decision-making, providing a single source of truth for executive understanding.

This skill guides the creation of C-suite level marketing dashboards. It translates complex operational data (like ad spend, channel performance) into clear, actionable business outcomes (revenue influence, market share). Ideal for CMO reports, board presentations, and strategic decision-making, ensuring senior leaders can make fast, informed choices without being overwhelmed by detail.

A sophisticated tool designed to automatically generate professionally branded, structured marketing reports. It supports various report types, including executive summaries, campaign performance deep dives, channel analysis, and quarterly reviews. By pulling live data from multiple sources and tailoring the output to the intended audience (C-suite, team, or client), it eliminates manual report assembly, ensuring brand consistency and actionable insights.

This guide details how to construct a sophisticated and rigorous robustness suite for top-tier academic econometrics journals (e.g., AEJ: Applied). It moves beyond simple regression tables, providing targeted checks against common methodological threats such as omitted variables, specification search, functional form choices, and incorrect statistical inference. The goal is to systematically demonstrate that the main point estimate is stable, reliable, and not an artifact of the research design.

This guide helps researchers strategically assess whether their empirical microeconomics project is best suited for the American Economic Journal: Applied Economics (AEJ: Applied). It provides a structured framework to test the project's causal design, scope, and cross-field appeal, ensuring the question is 'AEJ: Applied-shaped' rather than being too general (AER) or too specialized (Field Journal).

This guide helps researchers assess the optimal venue for their work, determining if their project aligns best with AISTATS, or if it is better suited for major ML conferences (NeurIPS, ICML, ICLR), UAI, or specific statistics journals. It emphasizes the need for strong statistical foundations, inference guarantees, and the unique intersection of AI, ML, and statistics before starting the writing process.

This skill helps researchers determine if their scientific contribution—whether it is primary research, a major synthesis, a field-campaign overview, or a community essay—is best suited for the Bulletin of the American Meteorological Society (BAMS). It guides the author on the specific scope, required level of synthesis, and community significance expected by BAMS, helping to re-frame narrow technical results for a broad, interdisciplinary audience across atmospheric, oceanic, and hydrologic sciences.

This guide helps researchers determine if their embodied AI or robotics project is best suited for the Conference on Robot Learning (CoRL). It provides a rigorous two-axis test—assessing if learning is the core contribution and if embodiment is essential to the claim—to decide between submitting to CoRL, ICRA, IROS, NeurIPS, or other journals.

This guide provides a comprehensive checklist for evaluating Electronic Design Automation (EDA) research manuscripts for top-tier conferences like DAC. It emphasizes the necessity of using standard, recognized benchmark suites (e.g., ISPD, EPFL, TAU), comparing results against the true State-of-the-Art (SOTA), reporting full PPA metrics and runtime, and implementing ablation studies to ensure fairness, reproducibility, and generalizability, especially for ML-based methods.

A comprehensive guide for establishing research reproducibility in Electronic Design Automation (EDA). It details best practices for pinning benchmark suites, disclosing specific tool versions (open/commercial), documenting technology nodes (PDKs), and handling stochastic data flows (seeds, variance). Essential for ensuring that semiconductor design claims and ML-for-EDA results are credible and verifiable by the academic community.