Skill UI

This skill provides comprehensive guidance and established patterns for conducting rigorous scientific research and complex data analysis. It helps users structure their thinking process, ensuring a systematic and methodical approach to scholarly inquiry. Ideal for academic projects, literature reviews, and complex data interpretation.

Structured manuscript and grant review guidance that walks reviewers through methodology, statistics, reporting compliance, and constructive feedback for rigorous scientific evaluation across disciplines.

Assess research claims and experimental rigor by evaluating methodology, statistical validity, evidence quality, and biases using GRADE and Cochrane ROB frameworks, plus supporting schematics for complex concepts.

A scientific, customer-centric framework for systematically improving conversion rates on websites and landing pages. This methodology guides users through funnel mapping, identifying core visitor objections, and conducting evidence-based A/B testing. It is essential for auditing underperforming funnels, optimizing checkout flows, and transforming general marketing hypotheses into measurable, statistically significant improvements.

This skill teaches users to apply the scientific method to software debugging. Instead of random changes, it guides the process through five steps: Observation, Hypothesizing, Prediction, Testing, and Analysis. By systematically forming and testing specific, measurable hypotheses, users can efficiently isolate failing components and confirm the true root cause of technical issues.

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.

A comprehensive framework for planning, configuring, executing, and analyzing complex Computational Fluid Dynamics (CFD) simulations using pseudospectral methods. It enforces a rigorous, multi-stage workflow, guiding users from defining state equations and boundary conditions to generating resource estimates, running dry-runs, and preparing site-specific MPI jobs. Designed for high-fidelity scientific computing and reproducibility.

A comprehensive skill for creating technical and scientific documentation using Markdown as the source format, enhanced by embedded Mermaid diagrams. This method establishes text-based diagrams as the canonical standard, ensuring that all visualizations are clean, editable, and highly readable in version control systems like Git. It is ideal for generating reports, architectural diagrams, workflows, and any content that requires cross-platform rendering and version history.

A comprehensive tool supporting rigorous, evidence-bounded, and structured peer-review of scientific manuscripts, research protocols, and academic proposals. It guides reviewers through critical assessments of methods, statistics, ethical compliance, reproducibility, and claim-evidence alignment, while strictly enforcing confidentiality and human accountability throughout the review process.

This skill generates detailed, structured research protocols for various academic, clinical, or market research studies. It ensures all critical components—such as objectives, study design, participant criteria, methodology, data management, statistical analysis plan, and ethical considerations—are comprehensively documented, providing a robust framework for any scientific investigation.

A comprehensive guide detailing the process of responding to major or minor revisions from academic journals (like AAAG). It teaches authors how to structure a point-by-point rebuttal letter, address editor and referee concerns methodically, strengthen the core academic argument, and incorporate required methodological and cartographic fixes, ensuring the response is both respectful and scientifically rigorous.

A comprehensive guide for structuring and writing high-quality AI research manuscripts intended for major academic conferences (e.g., AAAI). It teaches how to structure the first page for maximum impact, clearly state contributions (method, theory, system, etc.), ensure two-column readability, and address ethical limitations to appeal to a broad, non-specialist committee audience.