Skill UI

AI-powered search using Perplexity models via LiteLLM and a single OpenRouter API key for real-time, web-grounded answers with citations, ideal for up-to-date research, literature surveys, fact checking, and domain-specific queries beyond model cutoffs.

Literature Review Tool helps researchers gather and synthesize key insights from scholarly texts, highlighting patterns and critical arguments to accelerate literature surveying workflows even when structured content is initially unavailable.

Generates, validates, and ranks publishable research ideas from a broad direction. Surveys the landscape, brainstorms concepts, runs novelty checks, and pilots top candidates within GPU budget constraints.

This skill automates the discovery of robotics research ideas by orchestrating a literature survey, idea generation, novelty check, and critical review workflow. It focuses on embodied AI, sim2real, and benchmark-grounded concepts for manipulation, locomotion, and navigation. The pipeline ensures ideas are feasible, falsifiable, and aligned with robotics constraints like simulation-first approaches and hardware availability.

Conduct, analyze, and synthesize customer research using interviews, surveys, support tickets, reviews, and online communities to uncover pains, jobs to be done, personas, and voice-of-customer insights.

Provides guided statistical analysis for research data, covering test selection, assumption checking, effect sizes, and APA reporting. Supports frequentist and Bayesian methods including t-tests, ANOVA, regression, and correlation. Ideal for comparing groups, testing hypotheses, and analyzing experimental or survey data to produce robust, reviewer-ready results.

This skill performs a structured Systematic Literature Review (SLR) by analyzing multiple academic papers on a given topic. It searches academic databases (like arXiv), extracts structured metadata (research questions, methodologies, key findings, limitations) from the entire set of papers, and synthesizes overarching themes and research trends. It is ideal for creating comprehensive literature surveys, annotated bibliographies, and cross-paper comparisons, outputting reports in professional citation formats like APA, IEEE, or BibTeX.

This skill analyzes raw user research data—including transcripts, survey results, and feedback—and synthesizes it into a structured, actionable report. It identifies key themes, quantifies prevalence, pinpoints critical pain points, prioritizes feature requests, and provides strategic, evidence-backed recommendations for product improvement and development.

This skill generates comprehensive, academic-style survey papers as a single, self-contained HTML file. By reading an anchor resource and curating research data, it structures the content and utilizes advanced LLMs (like Kimi K2.6 via Fireworks) to generate all prose, figures, and bibliographies, suitable for rapid academic literature review.

This guide provides comprehensive methodological blueprints for designing scholarly manuscripts submitted to Accounting, Organizations and Society (AOS). It covers various research paradigms, including qualitative field studies, behavioral experiments, surveys, and historical/archival inquiries. Emphasis is placed on rigorous planning, ethical considerations (IRB approval, consent, anonymity), and maintaining theoretical alignment between the research design and the core argument, helping researchers avoid methodological pitfalls.

This guide provides comprehensive guidelines for selecting, validating, and defending rigorous research designs for Contemporary Accounting Research (CAR) manuscripts. It covers various approaches—including panel archival, controlled experiments, analytical models, field studies, and surveys—while stressing critical rigor checks like identification strategy, internal validity, and model discipline. It also mandates adherence to ethical review processes and data integrity policies.

A comprehensive guide for academic researchers on designing, auditing, and reporting rigorous studies (experiments, field deployments, surveys, etc.) for Human-Computer Interaction (HCI) papers. It details best practices for quantitative power analysis, qualitative thematic analysis, ethical reporting, and mixed-methods integration to ensure research claims are strongly supported by evidence.