Skill UI

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 framework serves as a rigorous thought partner, designed to challenge underlying assumptions, separate raw evidence from interpretation, and ensure that all conclusions are fully defensible. It guides users to define clear decision goals, test counterfactuals, and calibrate claims strictly to the available data quality. Ideal for high-stakes projects where the cost of error is significant.

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.

Guides scholars in the methodology of Critical Inquiry (CI), where evidence is not data but objects (texts, images, artworks, cases) read closely through theory. This skill teaches how to select objects that rigorously test an argument, perform deep close reading, incorporate counter-evidence, and prepare visual materials to professional journal standards.

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.

This skill provides a deep dive into the rigorous multi-stage review process for scientific manuscripts, particularly in field crops. It details editor scope screening, the single-anonymized expert review, and the specific criteria required (e.g., multi-environment data, statistical rigor, general relevance) to prevent desk rejection. Use this knowledge before submission to stress-test your manuscript, anticipate reviewer objections, and structure your findings for maximum scientific impact.

A comprehensive guide for managing the highly critical stage of journal revision. This skill helps researchers decode editor feedback, strategically decide on necessary data collection (e.g., new countries, invariance tests), and structure a professional, point-by-point response letter that addresses every reviewer comment effectively while maintaining scholarly rigor.

A comprehensive guide for structuring academic manuscripts to ensure the contribution is field-wide, not niche-specific. It teaches authors how to move beyond simply presenting data by engaging the core academic debate, naming precisely what is contested or untested, and positioning the work as a conceptual shift within the broader communication field.

This skill guides researchers in developing robust, theoretically innovative manuscripts, specifically for top-tier journals like JPSP. It moves beyond simply reporting data by teaching how to define a core claim, derive diagnostic, testable hypotheses (H1, H2, H3...), establish clear mechanisms, and map the cumulative argument across multiple studies. It focuses on the theoretical framework, not the data analysis itself.

This guide teaches how to elevate descriptive linguistic data (such as glossed patterns or corpus trends) into robust, theoretically grounded claims. It outlines the necessary structure for presenting an argument with explicit assumptions, testable predictions, and critical engagement with competing theoretical frameworks, moving beyond mere description to achieve genuine theoretical depth.

A comprehensive guide for researchers in finance and economics to rigorously assess their research proposals. It helps pressure-test an idea against the high standards of top academic journals, focusing on establishing genuine novelty (new questions, mechanisms, or data) and methodological rigor. Ideal for structuring the contribution claim and refining the research design.