Audit Stata, R, or Python econometrics code for design and inference issues.
npx skills add https://github.com/eabeam/econ-skills --skill econ-audit
Please help me install this Agent Skill. Skill: econ-audit Source: https://github.com/eabeam/econ-skills/tree/main/econ-audit Command: npx skills add https://github.com/eabeam/econ-skills --skill econ-audit
Adversarial econometrics review that identifies specification errors, clustering mistakes, bad controls, and silent analytical failures in Stata, R, or Python code, helping researchers ensure valid conclusions.
Run the econ-audit on your project's code directory to generate a full audit report.
High-intent search queries and answers about installing and using this skill.
An econometrics audit identifies specification errors, clustering mistakes, and bad controls in your Stata, R, or Python regression code. It flags design and inference issues to ensure your robust conclusions are valid for manuscript or repository submissions.
A hostile econometrics audit adversarially reviews your analysis code to detect silent analytical failures and specification errors. It checks robustness by evaluating clustering, sample selection, missing data, and valid controls against your project context.
Yes, you can audit a full project with multiple regressions. The review applies to single-file scripts or entire project directories, evaluating pre-analysis plan references and project context to guide comprehensive robustness checks.
Yes, the regression audit works with Stata, R, and Python code. It evaluates your scripts to verify proper clustering, appropriate fixed effects, valid controls, and clear documentation of design decisions across these environments.
You check your Stata or R script for bad controls and sample selection bias by running an adversarial review that flags inappropriate controls and missing data issues. The audit outputs structured findings and recommended fixes for your analysis.
You should run a robustness check on your econometrics analysis before manuscript or repository submission. Running an audit identifies silent analytical failures and ensures your design decisions and inference issues are properly documented and resolved.
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