Skill UI

This comprehensive tool analyzes the entire customer conversion funnel to pinpoint drop-off points, identify bottlenecks, and determine the root causes of underperformance (e.g., messaging, UX, targeting). It benchmarks current rates against industry averages, models improvement scenarios with projected revenue impact, and provides statistically validated, prioritized action plans to maximize overall conversion rate.

This skill generates a comprehensive, live snapshot of your marketing performance by pulling data from all connected analytics platforms (e.g., Google Analytics, Meta, LinkedIn). It compares current KPIs against defined brand targets, historical benchmarks, and industry averages. Use this for daily health checks, trend monitoring, and identifying immediate actionable insights across all channels.

This skill transforms raw marketing data into comprehensive, narrative-driven performance reports. It goes beyond simple metrics tracking by performing trend analysis, detecting anomalies, benchmarking against industry standards, and generating actionable, prioritized recommendations. Ideal for stakeholders needing an executive summary or detailed channel commentary.

A comprehensive guide for authors submitting research on software testing, analysis, verification, and fuzzing to ISSTA. It provides strategic advice on framing the paper, establishing necessary evidence (e.g., fault models, benchmarks, real-world data), and ensuring the contribution aligns precisely with the requirements of top-tier CS conferences.

A comprehensive guide for revising technical papers submitted to ACM Multimedia (ACM MM). It teaches authors how to shift focus from single-modality benchmarks to cross-modal fusion mechanisms, ensuring all media elements (figures, audio, video) function as direct evidence. The guide emphasizes structuring the argument within a tight 6-8 page conference limit, making the multimedia contribution visible from page one.

A comprehensive pre-submission tool designed to guide authors targeting ACS Nano. It assesses whether a nanoscience or nanotechnology manuscript meets the journal's high standards across scope, method rigor, structural presentation, and conceptual framing. Use this skill to re-frame your work, understand the required evidence (e.g., single-molecule studies, quantitative benchmarks), and ensure optimal fit before official submission.

This tool helps researchers assess if their primary research on advanced energy materials (e.g., batteries, solar cells, fuel cells) is suitable for Advanced Energy Materials. It provides detailed guidance on scope, required evidence (benchmarking, mechanistic understanding), house style, and submission criteria necessary for high-impact energy science publications.

This comprehensive guide provides rigorous standards for advanced materials manuscripts. It mandates that structure–property claims must be validated through multiple, complementary characterization techniques (triangulation). It also details best practices for benchmarking against the state-of-the-art, ensuring reproducibility, and correctly partitioning core information between the main text and supplementary materials.

A comprehensive guide for authors submitting high-impact materials research. It details how to select expert referees (covering synthesis, device, and application domains) and provides actionable strategies to pre-empt common reviewer objections—such as claims of incremental advance, insufficient characterization, or weak benchmarking—thereby strengthening the manuscript's overall rigor and maximizing publication success.

A comprehensive guide for authors responding to reviewer reports and editor decisions (major/minor revision, rejection). It details how to structure a point-by-point rebuttal letter, what new data (e.g., stability, benchmarking) must be added, and how to ensure the revised manuscript maintains a clear scientific advance while adhering to publication guidelines.

A comprehensive guide detailing how to package all necessary supplementary evidence for academic submissions in AI, statistics, and machine learning domains. It covers best practices for ensuring reproducibility, including managing proofs, simulation scripts, benchmark data, random seeds, and minimizing metadata risks for reviewers.

A comprehensive guide for authors aiming to submit machine learning manuscripts to the Asian Conference on Machine Learning (ACML). This skill helps evaluate manuscript fit, re-frame research from journal or arXiv styles, understand required contribution types (e.g., algorithms, theorems, benchmarks), and align the paper's evidence bar with the expectations of a top-tier, regional AI/ML venue. Use it for venue selection and submission strategy development.