Skill UI

This skill teaches the methodology of forming concrete hypotheses about specific entities (such as film titles, director names, or historical events) when presented with constraints. Instead of performing broad, generic searches, users should leverage their world knowledge to narrow down possibilities, turning a wide query into a precise, verifiable search query. This is particularly effective for academic or historical research.

This skill guides users through structured, adaptive learning experiences for any topic. It diagnoses the user's current knowledge level and goal to create a personalized study path. It utilizes techniques like worked examples, retrieval questions, and spaced repetition to ensure deep understanding, making it ideal for self-study, skill acquisition, or subject review.

This skill transforms the current workspace into a comprehensive, stateful teaching environment. It guides the user through deep, multi-session learning by structuring content into core components: a defined mission, reference materials, curated resources, structured lessons, and continuous learning records. It employs educational best practices (spaced repetition, retrieval practice) to ensure long-term knowledge retention.

This skill helps you write growth log entries that extract reusable patterns from completed work, focusing on root causes and transferable rules rather than diary-style narration. It guides you to capture learnings after complex tasks, debugging, or failures using a specific template, ensuring knowledge compounds across sessions.

This comprehensive guide outlines the meticulous workflow for preparing a research paper for final publication (Camera-Ready) after acceptance at major NLP conferences like ACL. It covers critical steps including de-anonymization (restoring author details and acknowledgements), integrating mandates from meta-reviews, adding required disclosures (e.g., funding, AI assistance), and ensuring all technical metadata, font embedding, and licensing comply with the permanent open-access standards of the ACL Anthology.

This skill guides authors on how to position their manuscript within the complex, multidisciplinary literature of Agricultural Systems (AgSy). It ensures that the paper advances the conversation by engaging both the domain knowledge and the core modeling/systems methodologies. The process focuses on identifying unresolved systemic trade-offs, pinpointing the exact research gap, and clearly articulating the unique contribution among established alternatives.

This guide teaches researchers how to construct credible, high-impact synthesis exhibits for academic reviews (e.g., Academy of Management Annals). It focuses on creating framework figures, who-studied-what synthesis tables, and coverage exhibits that summarize existing literature, rather than presenting new primary data estimates. Essential for systematically structuring and communicating complex knowledge fields.

This comprehensive framework guides researchers on assessing the suitability of a candidate literature topic for a high-impact, integrative academic review (e.g., Annals). It requires the topic to pass five critical criteria—Maturity, Importance, Fragmentation, Integrability, and Tractable Scope—ensuring the resulting review addresses a profound 'problem with the field' and proposes a forward-looking agenda, rather than just summarizing existing knowledge.

A comprehensive guide for critically appraising primary academic studies (e.g., DID, IV, RDD) when writing literature reviews. It teaches how to assess the methodological credibility, robustness, and potential biases of existing evidence, ensuring the synthesis accurately reflects the field's best knowledge rather than merely counting conflicting results.

A guide for creating high-impact academic exhibits for literature reviews (e.g., Annual Reviews of Economics). It teaches how to synthesize findings into structured formats, including 'Who-found-what' summary tables, conceptual framework diagrams, and meta-evidence plots. The focus is on synthesizing existing knowledge across studies, not presenting original estimates. Essential for communicating complex, cross-field research findings to non-specialists.

This guide provides an in-depth analysis of the CIKM peer review process, which operates under double-blind rules and integrates perspectives from three distinct fields: Information Retrieval, Data Mining, and Knowledge Management. It details how authors can optimize their submissions to satisfy diverse expert standards, how to interpret complex, multi-faceted review reports, and the critical dynamics that determine a paper's acceptance status.

This comprehensive guide provides expert advice on structuring and polishing research manuscripts for multi-disciplinary academic venues like CIKM. It details how to satisfy reviewers from Information Retrieval, Data Mining, and Knowledge Management simultaneously, covering the optimal structure for the opening page, vocabulary discipline, budget compression, and effective title optimization.