Skill UI

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 serves as the central orchestrator dashboard for authors submitting manuscripts to PR&AI (Pattern Recognition and Artificial Intelligence). It meticulously outlines the entire publication lifecycle, from initial topic selection and structured writing to experimental design, rigorous self-review, navigating peer review stages (initial, expert, final), addressing revisions, and preparing the final camera-ready submission. It breaks down the academic process into actionable stages and critical milestones, ensuring no crucial step is overlooked during the 6-month review cycle.

A comprehensive style guide and checklist for writing and refining long-form academic articles for 'Pattern Recognition and Artificial Intelligence' (PR&AI). It covers the entire publishing workflow, from mastering the structured introduction (Problem-Gap-Contribution) and writing four-element abstracts, to adhering to rigorous standards for citations (GB/T 7714), figures, and mathematical notation in AI research.

A comprehensive guide for auditing and polishing original long-form manuscripts intended for submission to high-level Pattern Recognition and AI journals. It details mandatory components, formatting standards, anti-plagiarism checks, and ethical guidelines, ensuring the paper meets rigorous peer-review standards for publication.

An expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverages AI tools like GitHub Copilot, Qodo, GPT-5, and Claude with platforms like SonarQube, CodeQL, and Semgrep to identify bugs, vulnerabilities, and performance issues in code across 30+ languages.

This skill processes raw, unstructured materials (e.g., conversations, reports, controversies) into highly structured, reusable casebook entries. It focuses on identifying core facts, defining mechanism chains, and mapping responsibility links while ensuring rigorous data anonymization. It is designed for deep knowledge management and pattern recognition, helping users build a reliable, reusable knowledge base for future professional analysis, rather than generating immediate advice.

Expert AI-powered code review combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverages tools like GitHub Copilot, SonarQube, CodeQL, and Semgrep to identify bugs, vulnerabilities, and performance issues across 30+ languages, integrated with CI/CD pipelines for instant PR feedback.