Skill UI

A comprehensive system for implementing policy guardrails in data enrichment pipelines. It enforces critical controls including credit spending limits, blocks the enrichment of sensitive PII (e.g., SSN, bank details), and validates input data quality. Essential for maintaining compliance (GDPR/CCPA) and managing costs when processing large volumes of personal and business data.

Access over 800,000 economic time series from FRED's API, covering GDP, employment, inflation, interest rates, regional metrics, and more; ideal for macroeconomic analysis, financial research, policymaking, and forecasting workflows.

This skill provides structured procedures and Python examples for detecting internal data exfiltration. It analyzes endpoint, cloud, and email logs by establishing behavioral baselines and performing statistical anomaly detection. Use cases include investigating insider threats, monitoring DLP policy violations, and building robust User and Entity Behavior Analytics (UEBA) rules for SOC teams.

A powerful tool for deterministic and reproducible retrieval of evidence from specialized academic and policy corpora. It supports searching arXiv (preprints), PubMed/PMC (biomedical literature), and US policy documents. Users can check corpus freshness, retrieve stable metadata, and fetch full-document Markdown for high-reliability research tasks.

This skill provides a highly rigorous framework for analyzing complex public issues, platform governance, and institutional compliance. It is designed to structure evidence boundaries, assess procedural justice, and track public commitments by synthesizing source ledgers and multiple data points. It is intended as a specialized sub-routine triggered by the main CrossFrame suite, not for standalone use. Key focus areas include source verification, identifying weak signals, and mitigating AI compliance performance risks.

This guide outlines the mandatory steps and components required to assemble a robust, reproducible data and code replication package for submissions to the American Economic Journal: Applied Economics (AEJ: Applied). It ensures compliance with the stringent AEA Data and Code Availability Policy, covering data provenance, master scripts, and version control. This package must be built iteratively, not at the acceptance stage.

A comprehensive guide for assembling a fully reproducible research package for macroeconomics papers targeting the American Economic Journal (AEJ: Macro). It ensures compliance with the AEA Data and Code Availability Policy by documenting raw data, simulation/calibration code, estimation routines, and visualization steps, guaranteeing that results can be independently verified by reviewers.

This package assists researchers in assembling a compliant data and code deposit for AEJ: Economic Policy manuscripts. It ensures adherence to the AEA Data and Code Availability Policy by structuring the openICPSR deposit, generating the required README, and managing restricted data access paths. It is critical for passing the mandated pre-publication reproducibility check.

A comprehensive framework for translating reduced-form causal estimates (e.g., elasticity, treatment effects) into explicit policy statements. It guides researchers on building clear welfare, cost-benefit, or optimal-policy models, defining sufficient statistics, and articulating distributional effects, ensuring policy claims are rigorously grounded in empirical data.

This guide outlines the comprehensive package structure required for submitting manuscripts to American Economic Review: Insights (AER: Insights). It ensures authors meet the AEA Data and Code Availability Policy, providing all necessary code, raw data access, and documentation (including a master script and exhibit-to-code map) to allow the data editor to verify the reproducibility of every result before publication. It is crucial for passing the final publication gate.

This guide details the strict data and transparency policy required by the American Political Science Review (APSR). It outlines how to build a comprehensive reproducibility package—including raw data, master scripts, and detailed documentation—to allow the journal to verify that all reported tables and figures can be regenerated before publication. It covers quantitative methods, qualitative evidence sharing, and necessary exemption procedures.

This guide outlines rigorous standards for conducting heterogeneity analysis in economic research. It stresses that heterogeneity should not merely involve slicing data by region or size, but must be theoretically driven, linking multiple dimensions (e.g., ownership, financing constraints, industry type) to core economic mechanisms. The analysis must demonstrate significant group differences and clearly connect findings to differential policy implications, elevating the discussion from mere observation to actionable policy science.