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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Review, run, debug, and verify approved Python modeling code against its code plan, data contract, method decision, risk conditions, and experiment outputs, saving one compact JSON review.
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See how to installReview the instructions the agent will receive before you install.
code/Qx/qx_code_plan.md exist.syntax: imports, execution, exceptions, and obvious runtime faults.input_contract: paths, fields, units, shapes, missing-data handling, and raw-data protection.method_alignment: formulas, objectives, constraints, assumptions, main/baseline roles, and fallback scope match the approved plan.reproducibility: seed, deterministic setup, dependency/runtime record, and rerun consistency.output_contract: saved tables/metrics/figures, valid run summary, comparable main/baseline metrics, degeneracy evidence, and fallback-trigger state.code/Qx/reviews/qx_python_review.json.{
"schema_version": 1,
"question_id": "Q1",
"language": "python",
"reviewed_files": [],
"decision_id": "q1_method_choice",
"checks": {
"syntax": {"status": "PASS", "evidence": []},
"input_contract": {"status": "PASS", "evidence": []},
"method_alignment": {"status": "PASS", "evidence": []},
"reproducibility": {"status": "PASS", "evidence": []},
"output_contract": {"status": "PASS", "evidence": []}
},
"findings": [],
"verdict": "PASSED",
"reviewed_at": "ISO-8601"
}
Statuses are PASS, FAIL, or NOT_APPLICABLE with a reason. Any required FAIL blocks G3.
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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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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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Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
The English summary is being prepared. Showing the original description.