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.
日本語の概要は準備中です。原文の説明を表示しています。
Faithfully append a human modeler's choice and rationale to one canonical per-subquestion JSONL decision ledger. Use after a choice card is answered or when migrating legacy decision artifacts; never originate or improve the decision.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Make human judgment traceable without multiplying decision files.
methods/Qx/qx_decisions.jsonl
One JSON object per line. Records are append-only.
Use planning/framing_decisions.jsonl for global/pre-Qx framing decisions.
{
"schema_version": 1,
"decision_id": "q1_method_choice",
"decision_type": "method_choice",
"status": "DECIDED",
"decided_by": "human",
"captured_in_mode": "learning",
"choice": "M2",
"rationale": "Human-authored reason tied to evidence.",
"evidence_refs": ["methods/Q1/probes/risk_probe_summary.json"],
"decided_at": "ISO-8601",
"supersedes": null
}
Optional structured fields may include confidence, rejected alternatives, round action, claim scope, assumption labels, or fallback activation.
supersedes; never overwrite history.qx_method_card.md only when the decision changes method state.Typical values:
framingmethod_choicefallback_activationresult_verdictstability_verdictassumption_necessityclaim_scopepackage_signoffsubmission_authorizationMark a decision stale only when its cited evidence materially changed:
decision_stale record naming the old decision and changed evidence;Read legacy:
methods/Qx/qx_decision_log.mdmethods/Qx/decisions/*_modeler_decision.mdMigrate only completed human decisions. Preserve original timestamps and source paths when available. Do not convert PENDING placeholders into decisions.
ai_suggestion into the human rationale.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
日本語の概要は準備中です。原文の説明を表示しています。