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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Draft submission-ready mathematical-modeling paper sections from the approved solution package, frozen numbers, human decision ledger, and verified figures without searching scattered exploratory outputs or inventing interpretation.
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See how to installReview the instructions the agent will receive before you install.
rigor_profile is submission.If any prerequisite is missing, return to its producer rather than drafting around the gap.
Use, in order:
qx_solution_package_for_writer.mdfrozen_numbers.jsonqx_decisions.jsonlDo not hunt through raw experiment folders to invent a narrative.
paper/sections/qx.tex or the requested Markdown section.The AI must not originate:
Transcribe these from the decision ledger with provenance. If absent, invoke a compact choice card and stop the final draft until answered; do not fill the paper with repeated sentinels.
frozen_numbers.json.No reviews yet. Tell others what you thought.
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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.