Skills

Model Assumptions Builder

Extract and maintain global and method-specific mathematical-model assumptions from the...

Install

npx skills add zhnnky329/MathModeling-skills --skill model-assumptions-builder -g -a claude-code -y
Category
Engineering
Updated
1mo ago
License
MIT
Context cost
Lightabout 427 tokens
Runs scripts
No, instructions only

What it does

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.

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.

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