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cumcm-independent-review

Independently review a CUMCM computation package before its claims enter validation or paper writing. Use only inside a generated independent-review package.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(2)

  • SKILL.md2.8 KB
  • REVIEW_REQUEST.md4.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

CUMCM Independent Review

Review this freshness-bound package without consulting the originating conversation. Treat conclusions as untrusted and reconstruct only what is needed from official inputs, contracts, selected source, official runs, and outputs.

Boundaries

  • Work read-only inside the package.
  • Do not search for missing official materials. Report them as missing.
  • Do not edit, rerun, or replace the preserved execution unless the user separately authorizes a reproduction run.
  • File existence and successful execution do not prove that the model answers the official question.
  • Verify the package/upstream bindings before substantive review; a stale package is inconclusive.
  • Give exact file, formula, code, or numerical locations for every P0/P1 finding.
  • Preserve negative and inconclusive findings verbatim.

Review order

  1. Read REVIEW_REQUEST.md and materials/problem/SOURCE_MANIFEST.json. In targeted mode, also read package-root TARGETED_FINDINGS.json; it is the self-contained prior-P0 brief, so do not request the complete prior review.
  2. Reconstruct each subproblem from the supplied official files and PROBLEM_FACTS.json.
  3. Compare the official request with the model objective, variables, constraints, assumptions, and cross-question dependencies.
  4. Inspect computation entry points, run manifests, executed outputs, and result locators.
  5. Challenge relevant failure classes:
    • task or target misunderstood;
    • upper/lower bound or optimization direction reversed;
    • a quantity counted twice;
    • unsupported extrapolation;
    • an observed variable omitted without justification;
    • cross-question contradiction;
    • code and mathematical formulation disagree;
    • numerical output violates units, bounds, conservation, or official constraints.
  6. For targeted mode, resolve every entry in TARGETED_FINDINGS.json first. Do not repeat a full review unless the target change has global impact or current evidence reveals a new, well-supported P0.
  7. Write the raw review and structured result using the supplied template.

Verdict

  • accepted: no open P0 and no material unresolved concern in scope.
  • accepted_with_concerns: no open P0; one or more P1 concerns remain.
  • revision_required: at least one open P0 requires returning to the earliest affected stage.
  • inconclusive: required material is missing or the available evidence cannot support a decision.

Classify findings as P0/P1/P2 and open/resolved/accepted_concern. State the reviewer, model if applicable, originating/reviewer task references, and independence grade. Never describe same-context review as independent; a same-model fresh task remains correlated.

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같은 저장소의 스킬

Build or resume mathematical-modeling competition work, including CUMCM, MCM/ICM, graduate and regional contests, data challenges, and open-topic statistical modeling. Guide problem framing, modeling, computation, validation, Chinese or English LaTeX writing, and reviewed PDF/source delivery using current official requirements. Not for ordinary paper polishing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Lucasuiii/modeling-workbench1752026년 9월 23일 업데이트

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