Skill UI

This tool serves as a persistent memory and state manager for complex coding projects within the Claude environment. It allows users to initialize new projects with structured metadata, track detailed session progress (including summaries, decisions, and next steps), and save the entire context reliably. By ensuring project continuity across disconnected sessions, it drastically improves the flow and consistency of the development workflow.

This module is designed to manage and resolve unexpected operational exceptions within the supply chain or logistics process. It helps maintain business continuity and ensures that deviations are handled efficiently, optimizing the overall flow from sourcing to delivery.

This toolset allows users to save and restore complete agent sessions, ensuring context continuity across multiple conversations. It captures the agent's entire state, including memory, configuration, task progress, and learning metrics. Use it when pausing work and needing to resume later with full context, or for checkpointing long-running tasks.

This guide provides a rigorous framework for defending causal identification claims in empirical microeconomics, tailored for top-tier journals like AEJ: Applied. It covers modern techniques and anti-patterns for RCTs, Difference-in-Differences (DiD), Regression Discontinuity (RDD), and Instrumental Variables (IV), ensuring the mapping from variation to the causal estimand is explicit, defensible, and falsifiable.

This guide provides stringent methodological standards for establishing credible causal claims in empirical policy manuscripts. It covers advanced econometric techniques—including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Randomized Controlled Trials (RCT)—required to move beyond mere correlation and prove that the observed effect is genuinely caused by the policy intervention.

This guide provides rigorous methodological guidelines for ensuring the identification strategy in high-impact empirical economics papers (AER: Insights). It covers key areas including Difference-in-Differences (DiD), Regression Discontinuity Design (RDD), Instrumental Variables (IV), and structural parameter identification. It emphasizes the need for extreme methodological clarity, transparency, and conciseness required for top-tier academic publication.

A comprehensive guide for strengthening the methodological rigor of academic manuscripts, particularly for high-stakes journals. It covers advanced techniques for causal identification (e.g., Difference-in-Differences, Instrumental Variables, Regression Discontinuity), pre-analysis planning, linking theory to empirical tests, and defending claims against skeptical reviewers. This skill focuses on methodological architecture, not code.

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 provides a comprehensive methodological framework and checklist for researchers conducting quasi-experimental and micro-panel studies in rural economics. It systematically addresses common econometric pitfalls, such as self-selection bias, reverse causality, and endogeneity, detailing best practices for advanced methods like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), and Propensity Score Matching (PSM).

This skill creates a standardized, persistent workspace (desk) for managing long-running projects or complex workstreams. It sets up a structured directory containing a detailed journal for historical context and a dedicated signals folder for structured data output, ensuring continuity across multiple sessions.

A comprehensive guide detailing advanced econometric methods for establishing causal relationships in policy evaluations. It covers techniques such as Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and structural modeling. The core focus is bridging the gap between econometric rigor and policy legibility, ensuring that the causal mechanism is explicit in the main text while maintaining formal defense in the appendix.