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Decision Prompt Builder

CommunityPopular
zhnnky329
decision-prompt-builder

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.

Overview

Publisherzhnnky329
RepositoryMathModeling-skills
Skill namedecision-prompt-builder
Stars
1.1K
Forks
42
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by zhnnky329 on GitHub. Read the source before you install it.

Installation

Install the Decision Prompt Builder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/decision-prompt-builder
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git /tmp/MathModeling-skills
mkdir -p .claude/skills
cp -r /tmp/MathModeling-skills/.claude/skills/decision-prompt-builder .claude/skills/decision-prompt-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Decision Prompt Builder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Decision Prompt Builder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Decision Prompt Builder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Use only when claim scope or confidence is genuinely judgment-bearing:

  • keep the claim;
  • downgrade it;
  • drop it.

Output

Return one choice_card block containing:

  • decision_id
  • decision_type
  • question
  • 2–3 options plus optional constraint override
  • evidence paths
  • the consequence of each option

Do not save the card unless another skill needs a durable prompt record.

Rules

  • Ask about trade-offs, not mechanically determinable facts.
  • Prefer one card at a decision point; avoid repeated micro-confirmations.
  • Do not pre-fill the user's choice or rationale.
  • Do not mark a decision DECIDED.
  • Do not require a prose essay. One evidence-linked sentence is sufficient when it captures the user's real reason.
  • If there is no genuine human judgment, return control without asking a question.

Verification

  • Options are mutually exclusive and consequences are clear.
  • The card is grounded in the current problem or computed evidence.
  • No hidden recommendation appears in learning mode.
  • No per-skill decision artifact was created.

Frequently asked questions

What does the Decision Prompt Builder AI skill do?

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.

Why use Decision Prompt Builder on TypingMind?

Because you install it once and use it with any model. Decision Prompt Builder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Decision Prompt Builder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/decision-prompt-builder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Decision Prompt Builder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Decision Prompt Builder?

As many as you like. As long as a model supports skills, you can use Decision Prompt Builder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Decision Prompt Builder AI skill free?

Yes. It is published on GitHub by zhnnky329 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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