ZA_literature

Literature reviews in empirical asset pricing and machine learning often mean shuttling between dozens of PDFs, a dozen working papers, and endless citation pages just to piece together a coherent narrative. ZA_literature folds retrieval, synthesis, and citation-chain tracing into a single DSH session: describe the sub-topic you want to cover, and the plugin automatically loads the right skill, organizing results along a domain context that spans factor models, cross-sectional return prediction, GMM/SDF, predictive regression, and ML/DL applied to finance.

Beyond the literature itself, the plugin covers the other high-frequency steps in the same research pipeline: evaluating datasets, running regressions and training ML models, drafting journal-style chapters, and producing Beamer or job-market-talk slides. Each step is triggered with plain language; the model decides which skill to invoke based on task semantics, so you never need to memorize a command syntax.

It is best suited for researchers working on empirical asset pricing, factor construction, cross-sectional prediction, or financial ML/DL who need to track the frontier, trace citation paths, and rapidly draft sections of a working paper.