Skill UI

A specialized toolkit designed for generating and validating research-only artifacts for Clinical Decision Support (CDS) systems. It supports evidence-profile generation, cohort analysis, survival modeling, biomarker evaluation, and governance documentation. IMPORTANT: This skill is strictly for aggregate research and documentation; it must never be used for patient care, diagnosis, or live clinical operations.

A highly structured framework designed for generating, organizing, and transparently prioritizing scientific research concepts. It enforces independent idea generation, explicit assumption labeling, structured discussion, and evidence-aware evaluation, ensuring the creation of robust, proposal-level research directions for early-stage exploration.

This skill is a comprehensive strategic guide for authors targeting the ACM Conference on Recommender Systems (RecSys). It helps assess the fit of a manuscript, guides the re-framing of work from journal or arXiv styles into a specialized conference narrative, and outlines specific requirements for evidence, evaluation (including offline metrics, user context, and leakage-aware methods), and contribution framing necessary for top-tier CS venues.

A comprehensive guide for authors submitting papers related to web search, ranking, and data mining to WSDM. It provides critical advice on conference fit assessment, proper manuscript re-framing, required evidence (retrieval/ranking baselines, platform-aware evaluation), and how to position the work against competing top venues like SIGIR and KDD.

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 designing rigorous and robust empirical programs in Information Retrieval, Data Mining, and Knowledge Management. It outlines how to meet the stringent evidence requirements for top-tier conferences, ensuring claims are substantiated across multiple evaluation dimensions (retrieval, mechanism, scalability) and incorporating best practices for reproducibility and handling modern challenges like LLM contamination.

A comprehensive guide for researchers on packaging and documenting NLP artifacts, such as datasets, prompts, and evaluation code, for academic publication. It details best practices for ensuring reproducibility, managing provenance, adhering to licensing standards, and structuring evidence for top-tier conferences like EMNLP.

A comprehensive guide for generating and packaging reproducible evidence for signal processing submissions to ICASSP. It details what reviewers prioritize (scoring scripts, dataset splits, logs) and outlines best practices to ensure that headline metrics are easily regenerated, thereby maximizing review credibility and post-publication impact.

A comprehensive guide detailing the ICASSP peer review pipeline, covering single-blind review mechanics, technical committee evaluation criteria, and the strategic importance of evidence, significance, and reproducibility in signal processing papers. Use this to craft submission strategies and decode decision letters.

This guide provides comprehensive strategic advice for authors targeting the IEEE Visualization Conference (IEEE VIS). It covers conference positioning, scope fitting, required evidence (visual encoding, evaluation), and helps authors re-frame their work from journal or arXiv formats into a selective, high-impact conference narrative. Use it when determining if a research manuscript is appropriate for IEEE VIS.

This guide models the rigorous, double-blind, and per-track evaluation process for submissions to the IPSN conference. It details how papers are judged based on the soundness of information-processing methods, the reality of collected evidence (e.g., measured energy, latency), and the novelty of sensing approaches. It also clarifies key differences from other academic venues like SenSys and OpenReview, guiding authors on what evidence is crucial for acceptance.

A comprehensive guide detailing how to structure, package, and validate research artifacts for major machine learning conferences like KDD. It covers best practices for creating anonymous, reproducible repositories, handling scale claims, and providing robust evidence for different research tracks (Research, ADS, Datasets).