Skill UI

This tool guides the accurate invocation of the `ov skills` command group for managing the entire skill lifecycle on OpenViking. It supports adding skills from local directories, Git repositories, or GitHub URLs, and provides commands for listing, searching, updating, removing, and validating skill formats. Use it when an agent needs to automate skill deployment or discover available capabilities.

This skill provides comprehensive guidance for positioning computer graphics, visualization, and geometric computing manuscripts for the Pacific Graphics conference. It covers conference fit evaluation, necessary framing adjustments, evidence bar construction, and strategic advice for maximizing submission success in the Asia-Pacific visual computing domain. Ideal for authors needing to refine their paper's focus for a selective CS venue.

This skill provides comprehensive guidance for authors considering submitting a large-scale, monograph-length review to Physics Reports. It covers journal positioning, required scope across all physics subfields, the rigorous evidence bar, house style, and submission checklist. Use this tool to help structure and refine an article intended to become a definitive, self-contained academic reference.

This skill provides comprehensive guidance on adhering to PMLA (Publications of the Modern Language Association) style, utilizing the most recent MLA Handbook. It covers the mechanics of academic writing, including creating in-text parenthetical citations, building a Works Cited list from core elements, and correctly formatting quotations, translations, and discursive notes. Essential for final style checks before academic submission.

Provides expert guidance on structuring scholarly essays for major academic publications like PMLA. It helps writers organize complex arguments, ensuring a clear, compelling flow from problem statement to conclusion for a generalist academic readership. Essential for outlining or reorganizing sprawling drafts while adhering to strict word counts (6,000–9,000 words).

A comprehensive guide for structuring the analysis and results section of academic papers in Production and Operations Management (POM). It covers rigorous theoretical proofs, advanced empirical identification methods (e.g., DiD, IV), and data science validation techniques. The core focus is bridging statistical significance with actionable, operational managerial magnitude, ensuring research findings are decision-relevant.

This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.

This guide assists researchers in determining the topic suitability, appropriate format (research paper, review, special issue), and correct academic placement for manuscripts targeting 'Pattern Recognition and Artificial Intelligence' (PR&AI). It outlines the journal's focused scope across core AI domains like ML, CV, and NLP, helping authors avoid common submission pitfalls and ensuring their contribution has sufficient originality and academic depth.

This skill helps authors determine the suitability of their research paper (analytical, empirical, or behavioral) for prestigious Operations Management journals like Production and Operations Management (POM). It provides detailed guidance on required scope, methodological rigor (e.g., identification strategies, model assumptions), structural requirements, and common pitfalls to ensure maximum publication potential.

A comprehensive guide designed for authors targeting PRX Quantum. It helps determine the suitability of manuscripts in quantum information science, quantum computing, or quantum technologies. The skill provides detailed guidance on the journal's scientific scope, required significance bar, structure, and open-access submission standards.

Provides rigorous statistical guidance for conducting meta-analyses in academic settings. This skill covers the calculation of standardized effect sizes (e.g., Hedges' g, log odds ratio), selecting appropriate models (defaulting to random-effects or multilevel models), and accurately quantifying between-study heterogeneity (Q, I², τ², and prediction intervals). It ensures the analysis meets state-of-the-art standards for high-impact psychological journals.

This skill guides the structure and prose for drafting high-level integrative reviews, particularly for publications like the Psychological Bulletin. It ensures the manuscript moves beyond descriptive annotation to become a rigorous, evaluative synthesis. Users must comply with APA 7th edition style and specific reporting standards (MARS for meta-analyses, PRISMA for systematic reviews, and JARS for quantitative work). It provides detailed guidance on structuring the abstract (≤ 250 words), the method section, results, and discussion to meet expert reviewer expectations.