Skill UI

This advanced pipeline orchestrates complex feature development based on Request for Comments (RFCs). It breaks down monolithic changes into small, independently verifiable work units, managing dependencies through a Directed Acyclic Graph (DAG). It includes multiple quality gates, dedicated unit testing, and a safe merge queue to ensure robust integration and controlled deployment.

This system acts as an orchestrator for a three-tier model team (Advisor, Workers). It is designed for complex, large-scale tasks that exceed the capability of a single model pass, requiring decomposition, parallelization, and mandatory verification loops. Use cases include researching multiple competitors, executing multi-stage plans, or managing iterative development cycles involving different specialized AI agents.

This comprehensive guide details the rigorous standards for conducting and reporting advanced demographic analyses. It covers essential techniques, including constructing accurate rates, performing decomposition, generating life tables, and handling event history/survival data. The core focus is on methodological rigor, ensuring the reproducibility of results, and honestly reporting uncertainty (e.g., confidence and credible intervals) to meet the standards of expert peer review.

This guide assists users in structuring and rigorously defending the methodological design for demographic manuscripts. It provides detailed guidance on selecting and justifying advanced techniques—including life tables, decomposition analysis, survival models, Age-Period-Cohort (APC) modeling, and microsimulation. It emphasizes critical steps like establishing identification assumptions, addressing causal biases (selection and exposure), and formulating an adjudication test to rule out rival explanations, ensuring the research is methodologically robust.

This guide teaches how to restructure complex econometric tables and figures for manuscripts intended for a dual audience of professional economists and non-technical policymakers. The focus is on clarity, interpretability, and impact, ensuring that the main text presents policy-relevant findings (e.g., event studies, welfare decomposition) while relegating dense technical details (full regression dumps, diagnostics) to the appendix. It emphasizes translating standard coefficients into meaningful units like jobs or % of GDP.

A comprehensive guide for advanced econometric techniques in causal inference, covering modern approaches for quasi-experimental designs such as staggered Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Double Machine Learning (DML). It details best practices, critical diagnostic tests (e.g., weak instruments, parallel trend checks, bias decomposition), and robust reporting standards required for high-impact economic research.

This guide provides a comprehensive framework for designing rigorous, falsifiable system evaluations for academic research papers. It details best practices across four critical evidence layers: end-to-end performance, mechanism decomposition, operational cost, and boundary testing. Learn how to ensure workload realism, maintain baseline fairness, structure your experiment matrix, and avoid common pitfalls to meet top-tier systems research standards.

A comprehensive guide for conducting reproducible empirical analysis in labor economics, adhering to high academic standards (e.g., JOLE norms). It covers essential steps including sample construction from CPS/ACS/registers, advanced wage decompositions (Oaxaca/RIF), proper handling of standard errors, and robust sensitivity checks to ensure results are fully replicable.

This guide provides detailed instructions on writing the policy implications section for manuscripts submitted to quantitative journals (like JQTE). It emphasizes grounding all policy recommendations in the paper's specific quantitative findings (measurement, forecasting, or decomposition results), ensuring the advice is actionable, timely, and relevant to planning, industry, and technology decisions.

Provides comprehensive guidelines for building and validating complex structural economic models, including CGE, IO, and Structural Decomposition. It emphasizes mandatory elements like explicit data sourcing, parameter calibration, macroeconomic closure rules, and scenario reproducibility to ensure models are not 'black boxes' and are scientifically rigorous.

This guide provides essential knowledge for selecting and defending data sources for advanced research in international economics. It emphasizes the critical need for data to possess a 'global attribute,' covering trade flows, Global Value Chains (GVC), and cross-country comparisons. Key data types include micro-level customs data (firm-product-destination), cross-country panels (WDI, IMF), and global input-output tables (WIOD/ADB MRIO) for GVC decomposition.

A comprehensive guide for advanced empirical research in open economies. It outlines best practices for establishing causal inference by anchoring identification strategies in external or institutional shocks (e.g., tariffs, trade agreements, exchange rate reforms). The guide emphasizes modern critiques of standard methods like shift-share and gravity models, providing detailed protocols for DID, Bartik decomposition, and IV estimation to meet top-tier academic journal standards.