本文へ移動
cccskills
無料GitHub で公開

model-assumptions-builder

Extract and maintain global and method-specific mathematical-model assumptions from the problem frame, active method cards, data profile, and risk probes, while leaving necessity and impact judgments to the human modeler.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md1.9 KB

SKILL.md(原文)

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。

Inputs

  • problem parse;
  • active method cards;
  • data profile and risk-probe summaries;
  • question dependency map;
  • existing assumptions and human decisions.

Read legacy candidate pools only during migration.

Workflow

  1. Extract explicit problem assumptions and method-induced assumptions.
  2. Remove filler statements that do not affect model validity or interpretation.
  3. For each assumption record:
    • scope and source;
    • modeling need;
    • applicable method/Qx;
    • validation evidence;
    • mitigation or fallback link.
  4. Identify conflicts across Qx.
  5. Present unresolved necessity/impact trade-offs in one compact choice card where possible.
  6. Log human assumption_necessity decisions in qx_decisions.jsonl.
  7. Save planning/model_assumptions.md, transcribing settled human labels and impacts with decision IDs.

Assumption Fields

  • ID;
  • statement;
  • scope;
  • source and modeling need;
  • human-confirmed type: necessary or simplifying;
  • validation method/evidence;
  • impact if violated;
  • mitigation/fallback;
  • decision ID.

Rules

  • Do not invent generic assumptions such as “data are accurate” unless they affect a real dependency.
  • Do not finalize necessary/simplifying or impact judgments for the human.
  • Do not leave many repeated sentinels in the final file; collect missing judgments through a choice card and stop finalization until answered.
  • Revisit an assumption only when its method, evidence, or downstream use materially changes.

Verification

  • Every assumption has a modeling need and source.
  • Human-owned labels trace to decisions.
  • Probe/robustness evidence addresses load-bearing assumptions.
  • Cross-Qx conflicts are resolved or explicit.

レビュー

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。

同じリポジトリのスキル

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。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

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。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

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。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

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。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

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。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

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。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

zhnnky329 のスキルをすべて見る

このスキルの問題を報告する