Turn an incoming set of findings — from an AI reviewer, a referee report, a code review, a linter, or a second model — into verified fixes, without letting a confident misread damage correct work. Every finding is a CANDIDATE until checked against the actual source. Use whenever you receive review comments, audit findings, or a critique you did not write yourself, especially when the reviewer is a model or when the volume is too large to check by feel.
A fluent, specific, line-numbered finding is not a verdict. It is a hypothesis about your work. Modern reviewers — especially models — produce objections that are confidently wrong at a meaningful rate, and some proposed fixes will introduce defects if applied. Your job is to convert findings into evidence-backed decisions.
Rule: never change correct work to satisfy a reviewer you have not checked.
Before adjudicating anything, confirm the reviewer saw what you meant to send (see verify-artifact). Findings about missing references, truncated sections, or numbering that does not match your copy are usually artifacts of a bad upload/excerpt, not defects. Adjudicating those as real is how correct material gets broken.
Classify each finding:
If a finding is computable, compute it: run the identity on a small adversarial case, grep for the symbol, resolve the cross-reference, execute the consuming code, count the occurrences. A two-minute check outranks any amount of reviewer confidence — in either direction. Several findings that look like taste turn out to be real, and several that look devastating evaporate.
Open the cited location. Ask:
Return one of: CONFIRMED / REFUTED / PARTIAL, each with line-level evidence. A refutation must cite the text that refutes it, not your recollection.
Apply all confirmed fixes together, rebuild, and re-run the mechanical checks. Do not drip one fix per round. Keep edits surgical — a qualifier, a scope word, a corrected formula — unless the defect genuinely requires structural work.
If a careful reviewer stumbled, a careful human may stumble the same way. For each refutation, ask: can I make the correct mechanism unmissable at the point where they stumbled? Add a short signpost — prose only, no change to claims.
The dominant cause of confident-but-wrong findings is remoteness: the claim is correct, but what licenses it sits elsewhere (a standing hypothesis a few sentences up, a factor established two paragraphs above, a premise imported by reference, a delimitation in a distant note). Where that is the cause, bring the qualifier local — a short parenthetical or an inline naming of the governing regime. This is also the single best defense against AI-assisted review generally.
Symbols carrying two meanings (centered/uncentered, raw/normalized, restricted/unrestricted) are the highest-risk case: disambiguate at the use site, not only at the definition.
Return: what was fixed (location + evidence), what was refuted and why (with the refuting text), what remains unresolved, and which decisions belong to the owner (estimand changes, scope concessions, reporting language, positioning). Escalate those rather than deciding them.
Stop when a confirmation pass returns no new confirmed defect — only held items and taste. Track the yield: when a round produces mostly refutations, artifacts, and exposition, further rounds cost more to adjudicate than they return. The number of findings is not a measure of rigor.
external-oracle-process.md §5 — adjudicate, never ingestorchestration-schemas.md §7 — the validated FINDING contract9d371f0
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.