adversarial-empirical-reviewlisted

# Adversarial Empirical Review Runs a regression-gated, N-round adversarial loop over a project's empirical output. The design is in `docs/2026-06-08-adversarial-empirical-review-design.md` (v2) and the module API in `docs/CONTRACTS.md`. Read the design before operating the skill. The costly error here is corrupting a result that was already correct, so the whole pipeline is incumbent-preserving: a hard failure-set regression gate, "no clear difference keeps the incumbent" for residuals, and minimization of LLM judgment in favour of mechanical checks. **The crux is whether the tables are correct.** Auditing an empirical manuscript means verifying that the numbers in its tables are what the analysis code actually computes — that each coefficient, standard error, p-value, N, and R² reproduces from the data and the code and is internally consistent. This is an empirical + code review: the battery, the reviewer, and the drafter are all scoped to **table-number correctness**, the manuscript is consulted only as the spine that maps paper Table numbers to fragment files, and prose is not examined (the prose-scanning catalog checks default to off; research-design and interpretation critique are out of scope, with the consequential-category machinery retained as a fail-safe). The mechanical anchor for this is `builtin.table_source_agreement` (every estout-fragment cell is audited against a `numbers.json` of computed values) together with the reproduction tier and the coef/SE/t, sta