Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal…
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying…
Remove signs of AI-generated writing from academic medical papers. Use when editing or reviewing manuscripts to make them sound more natural and professionally written. Based on Wikipedia's "Signs of AI writing" guide, adapted for medical literature.
Entry point for the p-hacking skills suite. Routes a request to the right sub-skill for (a) mapping researcher degrees of freedom in an econometric design, (b) running an instrumented specification search, (c) detecting p-hacking in a body of results, (d)…
Name and classify p-hacking strategies, and quantify what each one does to the false-positive rate. Covers the twelve-strategy compendium of Stefan and Schoenbrodt (2023), thirteen econometrics-specific degrees of freedom (clustering doctrine, fixed-effect…
Map the garden of forking paths for a concrete econometric design and turn it into a machine-readable design card that the specification-search engine can walk. Use when asked how many defensible analyses a dataset or research design admits, to enumerate…
Run an instrumented walk of a specification universe and recover honest inference from it. Estimates every specification in a design card (or walks it with a realistic search procedure), logs a complete ledger, flags pathological specifications, calibrates…
Catalogue of prompt framings that determine whether an agent refuses or performs specification search, and the harness for probing them. Reproduces and extends the published finding that coding agents refuse an explicit request for significant results but…