proofcheck

# ProofCheck — Mathematical Proof Verification for Statistics/ML Theory Papers > 🔬 **Model Recommendation**: Run this skill on **Claude Opus** for best results. > Mathematical proof verification requires deep reasoning. If your session is not on > Opus, run `/model opus` before invoking. The skill will also delegate heavy > reasoning to Opus sub-agents internally when the Agent tool is used. Systematically check proofs in long technical appendices using a structured, evidence-based methodology with multi-pass verification. Based on: https://github.com/maweiruc/proofcheck-stat-paper ## Context: $ARGUMENTS ## Core Objective > Given the paper's stated assumptions, definitions, and cited results, does each claimed theorem follow with the stated constants, rates, quantifiers, probability levels, domains, and edge cases? **Goal**: Find correctness issues — NOT summarize the proof. Never silently repair proofs. --- ## Operating Principles 1. **Evidence First**: Every conclusion cites exact page/section/equation/line numbers. No vague references. 2. **Small Proof Units**: One definition, one lemma, one proof at a time. Never verify 20+ pages at once. 3. **Separate Facts/Inferences/Suspicions**: Verified (checked + referenced), Inferred (likely but unchecked), Suspect (possible gap). 4. **No Silent Repairs**: If proof proves B but claims A, record the mismatch explicitly. 5. **Human Owns Final Judgment**: Agent indexes, cross-checks, and reconstructs. Human reviews all S0/S...