Skill UI

This comprehensive framework allows users to validate and test both crypto and traditional trading strategies against extensive historical market data. It calculates essential performance metrics (Sharpe Ratio, Sortino Ratio, Max Drawdown), simulates trade execution, generates equity curves, and includes advanced parameter grid search optimization tools for robust strategy validation.

A comprehensive guide to diagnosing and enhancing search relevance within Qdrant. This skill addresses reported issues such as low precision, low recall, or irrelevant matches. It guides users through determining if the problem lies in the embedding model, Qdrant configuration, or the query strategy. Techniques covered include optimizing chunk splitting, tuning HNSW parameters, implementing hybrid search, and utilizing reranking for superior result quality.

A comprehensive strategic guide for authors targeting the ACM International Conference on Multimedia Retrieval (ICMR). This skill helps authors assess the fit of their multimedia retrieval, cross-modal search, or representation learning work for the venue. It provides specific advice on framing the contribution, defining necessary evidence (e.g., ranking metrics, baselines), and structuring the manuscript to meet ICMR's high standards.

A comprehensive guide for authors submitting papers related to web search, ranking, and data mining to WSDM. It provides critical advice on conference fit assessment, proper manuscript re-framing, required evidence (retrieval/ranking baselines, platform-aware evaluation), and how to position the work against competing top venues like SIGIR and KDD.

A comprehensive guide for academic researchers responding to R&R or conditional acceptance decisions for the American Economic Journal: Applied Economics (AEJ: Applied). This skill structures the response-to-referees letter and revision plan, emphasizing the triage of reviewer comments, adherence to editor priorities (especially concerning identification and robustness), and maintaining a professional, evidence-based tone. It helps authors transform critical feedback into a concrete, actionable revision strategy.

This guide provides a comprehensive, advanced strategy for academic researchers writing microeconometric papers for top-tier journals (e.g., AEJ: Applied). It teaches authors how to anticipate sophisticated, technical objections from expert referees (such as issues with causal inference design, selection bias, or parallel trends). The focus is on pre-empting these objections by integrating solutions directly into the manuscript text, rather than waiting for a formal revision letter.

This guide provides comprehensive guidelines for selecting, validating, and defending rigorous research designs for Contemporary Accounting Research (CAR) manuscripts. It covers various approaches—including panel archival, controlled experiments, analytical models, field studies, and surveys—while stressing critical rigor checks like identification strategy, internal validity, and model discipline. It also mandates adherence to ethical review processes and data integrity policies.

A comprehensive guide to establishing credible causal identification strategies for economic empirical research. It covers advanced quasi-experimental methods such as Difference-in-Differences (DID), Instrumental Variables (IV), and Regression Discontinuity (RDD), alongside structural modeling. The core principle emphasizes the necessity of explicitly stating, empirically defending, and testing all identification assumptions to meet the rigorous standards of top-tier academic economics journals.

A comprehensive guide for strengthening causal identification strategies in empirical finance and economics. It covers advanced quasi-experimental designs like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), Propensity Score Matching (PSM), and Double Machine Learning (DML). Use this framework to stress-test your research design, address common econometric pitfalls, and ensure your paper meets the rigorous standards of top academic journals.

This guide helps researchers assess if their work correctly spans the tri-community area of Information Retrieval, Data Mining, and Knowledge Management. It provides a detailed framework for comparing CIKM against major academic venues like SIGIR, KDD, WSDM, and SIGMOD/VLDB, ensuring the paper's scope is optimally matched to the target conference.

A comprehensive guide for positioning Natural Language Processing (NLP) research papers for the Conference on Computational Natural Language Learning (CoNLL). It helps authors optimize their manuscripts by focusing on conference fit, structuring evidence, identifying necessary baselines, and adapting the narrative for a selective CS conference audience. Essential for submission strategy and manuscript re-framing.

A comprehensive guide helping authors position their research for the Conference on Learning Theory (COLT). It covers core topics like formal learning theory, statistical learning, optimization, and lower bounds. Use this tool to evaluate venue fit, structure the manuscript as 'theorem-first,' and adhere to specific academic conference submission standards.