Skill UI

This guide provides a comprehensive checklist for auditing experimental rigor in chemical manuscripts, particularly for high-impact journals like JACS. It covers necessary characterization data for new compounds, including NMR, HRMS, elemental analysis, and stereochemistry, and ensures mechanisms and catalytic claims are fully supported by appropriate controls and data.

A comprehensive guide for developing robust conceptual frameworks for top-tier marketing academic manuscripts. This tool helps researchers move beyond simple hypothesis application by specifying detailed nomological networks, identifying mediating mechanisms (processes), and defining boundary conditions (moderators). It ensures the theoretical logic is sound and directly links theory to managerial implications, meeting the rigorous standards of academic publishing.

This comprehensive guide assists authors in determining if their authoritative clinical-oncology review or perspective fits the scope of Nature Reviews Clinical Oncology. It details the required synthesis depth, scope, commissioned submission process, and warns against submitting primary research, ensuring authors adhere to the specialized guidelines for this prestigious journal.

A comprehensive guide helping authors determine if their drug discovery review or analysis meets the high bar of Nature Reviews Drug Discovery. It emphasizes that the content must be authoritative, commissioned synthesis, integrating mechanism, clinical evidence, and pipeline status, rather than original primary research. Essential for drafting pre-submission proposals.

This skill provides detailed guidance for authors proposing or writing a high-level, conceptual review for Nature Reviews Materials. It outlines the journal's unique standards, focusing on conceptual synthesis, establishing unifying design principles, and providing an expert map for cross-disciplinary audiences in materials science and engineering. It covers scope, evidence bar, house style, and the crucial pre-submission collaboration process.

This comprehensive guide provides rigorous methodological rules for developing academic hypotheses. It emphasizes that every hypothesis (main effect, mediation, or moderation) must be justified by an explicit, named theoretical mechanism, moving beyond mere correlation or intuition. Essential for strengthening the theoretical foundation of quantitative research models.

A comprehensive, multi-phase methodology for debugging complex software issues. This guide mandates systematic root cause investigation, starting from symptom collection, code path tracing, hypothesis formulation, and deterministic reproduction. It teaches developers to fix the underlying cause, not just the visible symptoms, ensuring highly robust and minimal changes.

This tool tests your deep understanding of code you have recently written or reviewed. It generates structured, multi-level questions (Recall, Understanding, Application, Analysis, Synthesis) based on the provided code. Users can focus the quiz on specific areas like error handling, performance, or security, making it an excellent mechanism for continuous learning and solidifying technical knowledge.

A rigorous methodology for debugging complex anomalies or incidents when multiple plausible causes exist. It guides the user to enumerate competing, falsifiable hypotheses and prioritizes running the cheapest, most discriminating observation first. By iteratively eliminating hypotheses and preferring the least-assumptive survivor, it systematically localizes the root cause efficiently.

This skill offers expert guidance for AI-assisted debugging workflows and observability analysis. It covers root cause analysis, hypothesis generation, and production-safe debugging strategies using tools like Sentry and DataDog. Use it for troubleshooting errors, analyzing logs, and implementing validated fixes in development or production environments.

This skill outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.

Expert AI-assisted debugging workflow for modern software systems. Covers triage, observability data collection, hypothesis generation, strategy selection, instrumentation, root cause analysis, fix implementation, validation, and prevention. Ideal for developers facing complex production issues.