Audit economic research documents for mathematical, data, and code integrity.
npx skills add https://github.com/zbsaygin/econ-skills --skill audit-econ
Please help me install this Agent Skill. Skill: audit-econ Source: https://github.com/zbsaygin/econ-skills/tree/main/audit-econ Command: npx skills add https://github.com/zbsaygin/econ-skills --skill audit-econ
This Skill addresses the critical need for thorough verification of economic research, ensuring mathematical, data, and code integrity to meet top scholarly standards.
Provide the research document or code as input and request a full audit to identify inconsistencies and errors, then address the issues systematically.
High-intent search queries and answers about installing and using this skill.
To verify mathematical derivations in economic models, you can use a multi-agent audit that checks notation, assumptions, and equations step-by-step to ensure correctness and internal consistency.
You audit economic research data pipelines by tracing raw data sources, verifying variable construction, and reviewing regression code to confirm identification strategies and reproducibility.
The best way to check code reproducibility is through an exhaustive code audit that tests implementations, verifies data merging, and ensures results can be fully regenerated without errors.
Yes, after an initial audit identifies inconsistencies, you can fix issues systematically, propagate changes throughout the document, and run validation checks to verify consistency.
No specific statistical packages are required; the audit validates your existing econometric setups and package usage to confirm they align with your identification strategy and scholarly standards.
Research fails model validation when data pipelines contain filtering errors, regression code has implementation flaws, or mathematical derivations violate theoretical assumptions and scholarly standards.
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