Skill UI

This guide provides best practices for creating high-impact tables and figures (exhibits) for academic journals, especially in behavioral economics. It emphasizes visualizing the core behavioral comparison—such as treatment effects, dynamic changes over rounds, or underlying distributions—so the main finding is immediately visible before reading any text. Key practices include using confidence intervals over significance stars, ensuring grayscale readability, and providing self-contained captions.

This guide outlines a systematic methodology for gathering, reading, and synthesizing large bodies of academic research for comprehensive economic surveys (JEL style). It moves beyond simple summarization by enforcing structured data collection into a detailed evidence matrix. The process ensures maximum coverage through advanced search techniques (e.g., snowballing, JEL code sweeps) and focuses on synthesizing connections, identifying consensus, and articulating scholarly tensions across different studies.

This guide details best practices for generating self-contained, high-impact exhibits (tables and figures) for academic journals, particularly those with strict page limits like Management Science. It instructs users on structuring both theoretical proofs (analytical lane) and empirical results (e.g., regression coefficients, event-study plots) to ensure the findings are immediately readable and persuasive, even without reading the main text.

This skill automates the complex process of transforming raw research papers (PDF or URL) into structured, knowledge graph-ready resources for OpenViking. It executes a rigorous workflow: reading the entire paper, compiling an ARA-style artifact directory, validating the content against established schemas, and finally ingesting the structured data into the OpenViking knowledge base using the `ov add-resource` command. It ensures all evidence, claims, and logical relationships are captured systematically.

A detailed guide modeling the complex academic review machinery, using SIGIR as a primary example. It covers everything from per-track anonymity regimes and PC-member nomination duties to the critical scoring criteria: evaluating validity, fairness of comparison, and statistical rigor. Essential reading for authors preparing high-stakes research submissions.

A comprehensive guide for authors submitting visualization papers to major conferences like IEEE VIS. It outlines the strict rules for partitioning content, ensuring the core argument remains judgeable from the main body while leveraging supplemental materials (video, code, full data) to demonstrate interaction and reproducibility. Essential reading for academic researchers in the field.

This skill implements an advanced reference pattern for integrating with Monitoring Control Platform (MCP) servers. It ensures accurate data retrieval by first reading a domain-specific 'gotchas' reference file, mitigating query failures caused by complex server behaviors, pagination limits, or subtle API quirks. It analyzes issue data, groups errors by root cause, and generates a structured, actionable markdown report for improved system health visibility.