Skill UI

This skill guides the systematic execution of complex development plans. It mandates a critical review of the plan for dependencies, ambiguous conditions, and environmental assumptions before execution. The process involves incremental task completion, rigorous testing, structured committing, and final reporting, ensuring adherence to established development best practices.

This guide outlines a systematic workflow for requesting code reviews, crucial for validating completed tasks, implementing major features, or preparing for merge. It emphasizes reviewing early and often, ensuring reviewers focus on the tangible work product rather than the development thought process. Learn how to use specific Git SHAs and structured templates to submit comprehensive feedback requests.

A powerful tool designed for systematic literature reviews and meta-analyses. It searches scientific papers and retrieves highly structured experimental data directly from the full text, going beyond mere abstracts. It extracts over 25 fields, including detailed methods, quantitative results, sample sizes, quality scores, and conclusions, enabling comprehensive evidence synthesis for academic research.

This skill provides a robust, systematic framework for evaluating the rigor of scientific claims and research studies. It guides users in assessing experimental design validity, detecting various biases (selection, confounding, publication), and evaluating statistical evidence. By applying established grading standards like GRADE and Cochrane Risk of Bias, it is essential for conducting systematic literature reviews, peer reviews, and deep scientific critical analysis.

This skill generates comprehensive and structured requirements documents for a new specification. It systematically aggregates context from project files, business rules, and technical constraints, ensuring all generated requirements focus on user-observable behavior (WHAT) rather than implementation details (HOW). It follows established standards like EARS format and includes a rigorous review gate to ensure scope clarity and completeness.

This skill provides an interactive, comprehensive quality review for technical design documents. It systematically validates a feature's readiness for implementation by assessing critical issues, identifying strengths, and providing a clear GO/NO-GO decision with actionable next steps. Ideal for ensuring architectural soundness and catching risks early in the development lifecycle.

A comprehensive mentor guiding users through the entire lifecycle of academic literature reviews. It teaches systematic search strategies (PICO/PCC), critical appraisal methods, and advanced synthesis techniques. The skill covers searching major English and Chinese databases (e.g., PubMed, Web of Science, CNKI), organizing findings, and effectively utilizing AI research tools like Elicit and Semantic Scholar to build robust, evidence-based research papers.

A multi-phase workflow designed to guide users through complex problem analysis and solution design. Instead of providing immediate answers, it systematically enforces deep thinking by reviewing the problem's validity, assessing information gaps, performing root cause analysis, and iterating through solution vetting (self-check and final review). Ideal for complex technical troubleshooting, decision-making, or theoretical discussions requiring rigorous methodology.

This tool systematically converts a project roadmap into actionable specifications. It first analyzes features by grouping them into dependency waves. It then automatically dispatches sub-agents to generate complete feature specs (requirements, design, tasks, and JSON) for each wave. The core value lies in its comprehensive cross-spec review, which verifies data model consistency, interface alignment, and architectural integrity across the entire project, ensuring a cohesive final product.

This skill guides the systematic process of conducting comprehensive literature reviews across multiple academic databases (e.g., PubMed, arXiv, Semantic Scholar). It supports meta-analyses, scoping reviews, and state-of-the-art investigations. The rigorous workflow includes structured searching, data extraction, citation verification, thematic synthesis, and generating publication-quality reports (PDF/Markdown) complete with mandatory scientific schematics (like PRISMA flow diagrams) to ensure academic rigor and reproducibility.

This skill automates the critical submittal workflow within the Procore construction management platform. Users can programmatically create new submittals, defining necessary specifications, assigning reviewers, and setting due dates. It further supports updating the submittal status (e.g., approving or rejecting) and listing filtered results, ensuring systematic tracking and smooth management of the entire project review cycle.

This skill performs rigorous mathematical proof verification using an adversarial, cross-model review process. It reads a LaTeX proof, systematically identifies logical gaps (e.g., unjustified assertions, quantifier errors) and theoretical flaws (e.g., illegal interchanges, non-uniform convergence). It then iteratively fixes these gaps with full derivations, re-reviews, and generates a comprehensive audit report, ensuring mathematical rigor and completeness. Use this when seeking deep, academic verification of a theory paper.