code-reviewer
FreeDetect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
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Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.
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See how to installReview the instructions the agent will receive before you install.
Ask the smallest useful question that only the human modeler can answer. Present mutually exclusive options with consequences; do not turn mechanical checks into user questions.
planning/session_config.json.methods/Qx/qx_decisions.jsonl.interaction_mode; accept legacy mode for compatibility.learning: show 2–3 short questions and withhold the AI suggestion until the user answers.speed: show one compressed question and optionally show the AI suggestion alongside.rigor_profile does not change who owns the judgment.都不合适 / 补充约束 when the listed options may not cover the user's intent.learning mode before the answer.modeler-decision-logger; do not create a per-skill pending decision file.Ask only the missing high-impact items:
Do not ask the user to choose an algorithm name before evidence exists.
Example:
请选择这轮方案的首要取向:
- A. 可解释性优先——方法更透明,但可能牺牲部分拟合效果。
- B. 平衡——接受中等复杂度,要求能解释且优于可信 baseline。
- C. 性能优先——允许更复杂的方法,但需要额外稳健性和解释工作。
- D. 都不合适 / 我补充约束。
Use computed evidence to ask:
Name the consequence and evidence for each option. Do not silently convert an AI metric preference into the human verdict.
Use only when claim scope or confidence is genuinely judgment-bearing:
Return one choice_card block containing:
decision_iddecision_typequestionDo not save the card unless another skill needs a durable prompt record.
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Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
The English summary is being prepared. Showing the original description.
Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.
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Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.
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Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.
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Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
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Build the authoritative final method explanation for a submission-ready subquestion from the method card, human decision ledger, code plan, final results, and robustness evidence.
The English summary is being prepared. Showing the original description.