# Decision Prompt Builder

Build one compact choice card at a genuine mathematical-modeling judgment point.

- Repository: https://github.com/zhnnky329/MathModeling-skills (1073 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-24
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add zhnnky329/MathModeling-skills --skill decision-prompt-builder -g -a claude-code -y`
Codex: `npx skills add zhnnky329/MathModeling-skills --skill decision-prompt-builder -g -a codex -y`
Cursor: `npx skills add zhnnky329/MathModeling-skills --skill decision-prompt-builder -g -a cursor -y`
Gemini CLI: `npx skills add zhnnky329/MathModeling-skills --skill decision-prompt-builder -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add zhnnky329/MathModeling-skills`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Purpose

Ask the smallest useful question that only the human modeler can answer. Present mutually exclusive options with consequences; do not turn mechanical checks into user questions.

# Inputs

- Current gate and the judgment it needs.
- Problem goal, required output, hard constraints, and available evidence.
- `planning/session_config.json`.
- Existing decisions in `methods/Qx/qx_decisions.jsonl`.

# Configuration

- Read `interaction_mode`; accept legacy `mode` for compatibility.
- `learning`: show 2, 3 short questions and withhold the AI suggestion until the user answers.
- `speed`: show one compressed question and optionally show the AI suggestion alongside.
- `rigor_profile` does not change who owns the judgment.

# Choice-Card Workflow

1. Identify one load-bearing judgment.
2. Create 2, 3 mutually exclusive options. Each option must state its practical consequence.
3. Add `都不合适 / 补充约束` when the listed options may not cover the user's intent.
4. Ask no more than three questions in one card.
5. Do not recommend an option in `learning` mode before the answer.
6. Pass the answer verbatim to `modeler-decision-logger`; do not create a per-skill pending decision file.

# Standard Cards

## Before method screening

Ask only the missing high-impact items:

- output form to defend;
- interpretability/performance priority;
- unacceptable failure;
- experiment budget.

Do not ask the user to choose an algorithm name before evidence exists.

Example:

```markdown
请选择这轮方案的首要取向：

- A. 可解释性优先, , 方法更透明，但可能牺牲部分拟合效果。
- B. 平衡, , 接受中等复杂度，要求能解释且优于可信 baseline。
- C. 性能优先, , 允许更复杂的方法，但需要额外稳健性和解释工作。
- D. 都不合适 / 我补充约束。
```

## After a meaningful experiment

Use computed evidence to ask:

- proceed with the current main method;
- adjust a stated assumption or parameter and rerun;
- activate the recorded fallback.

Name the consequence and evidence for each option. Do not silently convert an AI metric preference into the human verdict.

## Before final freeze

The rest: https://github.com/zhnnky329/MathModeling-skills/blob/HEAD/.claude/skills/decision-prompt-builder/SKILL.md