Skill UI

A powerful cognitive tool that moves beyond surface-level brainstorming. It forces users to systematically deconstruct complex problems down to their 'irreducible truths.' The skill identifies 8-12 hidden assumptions (Surface, Middle, Deep), classifies them (Fact, Convention, Belief, Interest-Driven), and ranks them by Fragility × Impact. This process allows users to stress-test their premises and rebuild conclusions based only on verified realities, making it invaluable for major life decisions or business hypothesis testing.

This skill acts as an intensive sparring partner, rigorously questioning any provided plan, design, or concept. It guides the user through every decision branch, resolving dependencies one by one until a comprehensive and shared understanding is achieved. Ideal for stress-testing ideas, identifying blind spots, and ensuring the robustness of complex projects before implementation.

This skill serves as the central entry point for comprehensive API security testing. It guides testers to systematically categorize and prioritize potential vulnerabilities by focusing on core dimensions such as documentation review (Recon), object-level authorization (BOLA), token integrity (JWT), and complex data structures (GraphQL/Hidden Params). Use this as the initial step when assessing a new REST or GraphQL endpoint.

This skill acts as a workflow router, guiding researchers through the entire manuscript lifecycle for submissions to American Economic Journal: Microeconomics (AEJ: Micro). It does not write the paper, but diagnoses the current bottleneck—whether it's topic selection, model refinement, empirical testing, or rebuttal strategy—and specifies which specialized aejmic-* sub-skill to invoke next, ensuring the paper aligns with the specific requirements of AEJ: Micro. Triggered when users are unsure what step to take next, or when revising based on reviewer comments.

This skill guides the stress-testing of theoretical manuscripts by focusing purely on logical coherence. It helps authors develop robust arguments by verifying proposition chains, running counterfactual thought experiments, addressing rival theories, and identifying disconfirming cases. It ensures the internal consistency and rigor of a theory, independent of empirical data analysis.

This guide assists researchers in structuring and presenting academic evidence for top-tier journals like ASQ. It provides best practices for both qualitative (establishing transparent data-to-theory links, coding transparency) and quantitative (ensuring robust mechanism testing and alternative explanation checks) analyses. It ensures all theoretical claims are auditable and fully supported by the raw data.

A comprehensive guide to understanding the rigorous, model-driven review process at top cognitive psychology journals. Learn how editors and experts evaluate theoretical impact, methodological rigor, model identifiability, and data reproducibility. Use this guide to stress-test your manuscript before submission, preempt common rejection reasons, and shape your work to maximize its chances of acceptance in demanding academic venues.

A sophisticated guide for academic writers on structuring the literature review section of communication science manuscripts. It teaches authors how to move beyond simple summaries by actively positioning their hypotheses against existing empirical debates, identifying precise knowledge gaps, and adjudicating rival theoretical accounts. This skill ensures the work reads as a cumulative, theory-testing contribution suitable for top-tier academic journals.

A comprehensive guide to passing the rigorous, multi-stage peer-review process of top-tier academic journals. This skill helps authors understand the double-anonymized screening criteria, from initial editorial fit checks (quantitative/theory-testing) to satisfying multiple independent expert reviewers. Use this to stress-test your manuscript's methodology, measurement validity, and theoretical contribution before submission.

This skill guides the refinement of research topics to ensure they meet the rigorous standards of quantitative, theory-testing social science journals. It helps users structure vague ideas into testable communication-science mechanisms, focusing on the underlying 'why' and 'how' of processes, rather than merely documenting platform novelty or finding superficial correlations.

A comprehensive guide for structuring and presenting rigorous, high-impact experimental chapters suitable for top-tier computer science journals. Learn to design experiments driven by Research Questions (RQ), select fair baselines, conduct statistical significance testing, and implement ablation/sensitivity analyses. Ensures evidence is reproducible and perfectly aligns with abstract claims.

A comprehensive router designed to guide researchers through the entire lifecycle of writing a highly rigorous academic paper for top-tier econometrics journals, such as Econometrica. It outlines the necessary steps—from initial topic selection and theoretical positioning to detailed identification checks, robustness testing, and final submission package assembly—ensuring the manuscript meets the highest standards of mathematical rigor and scholarly depth. This guide does not replace specialized skills but dictates the optimal sequence of research stages.