Agent skill

Decision Prompt Builder

by zhnnky329 in zhnnky329/MathModeling-skills

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

MITAuto-check passed

Install Decision Prompt Builder

skills CLI
$ npx skills add zhnnky329/MathModeling-skills --skill decision-prompt-builder -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zhnnky329/MathModeling-skills decision-prompt-builder --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/decision-prompt-builder .claude/skills/decision-prompt-builder && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
decision-prompt-builder
GitHub stars
1.1k
Token cost
~837 tokens
SKILL.md length
395 words
Files
1
Skills in repo
29
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 6 steps: Identify one load-bearing judgment. → Create 2–3 mutually exclusive options.… → Add 都不合适 / 补充约束 when the listed options… → …
  • SKILL.md covers Before method screening, After a meaningful experiment and Before final freeze
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Decision Prompt Builder is an agent skill from zhnnky329/MathModeling-skills. 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.

Its SKILL.md is about 840 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: 面向数学建模竞赛的 Claude Code / Codex Skills ,支持分阶段建模流程与 Python、MATLAB/北太天元代码分支。 The licence is MIT.

Example prompts

  • “/decision-prompt-builder”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  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.

What it can do on your machine

Read from SKILL.md and the folder at commit 0b46e9c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Decision Prompt Builder loads about 837 tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 395 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~837

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from zhnnky329/MathModeling-skills at commit 0b46e9c, republished under its MIT licence (© zhnnky329). 395 words, ~837 tokens.

Download SKILL.mdSave it as .claude/skills/decision-prompt-builder/SKILL.md (or your agent's skills folder).
name
decision-prompt-builder
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.

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.

Show full SKILL.md (151 more words)Show less

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.

© zhnnky329, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .codex/skills/decision-prompt-builder of zhnnky329/MathModeling-skills.

Open the folder on GitHubat commit 0b46e9c

Compare with similar skills

Decision Prompt Builder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Decision Prompt Builder compared with similar skills
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Strategic Compactaffaan-m/ECC277k1 repos~2.1kAutomated safety check: PassMIT
Card Xiaohongshunexu-io/open-design100k—~402Automated safety check: PassApache-2.0
Token Cardaffaan-m/ECC276k—~1.3kAutomated safety check: PassMIT
Orbit Cardheygen-com/hyperframes60k1 repos~887Automated safety check: PassApache-2.0

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Questions about Decision Prompt Builder

What does Decision Prompt Builder do?

Build one compact choice card at a genuine mathematical-modeling judgment point. Decision Prompt Builder is an agent skill from zhnnky329/MathModeling-skills. Build one compact choice card at a genuine mathematical-modeling judgment point.

How do I install Decision Prompt Builder in Claude Code?

Run `npx skills add zhnnky329/MathModeling-skills --skill decision-prompt-builder -a claude-code`. Or copy the skill folder (.codex/skills/decision-prompt-builder in zhnnky329/MathModeling-skills) into .claude/skills/decision-prompt-builder in your project. Claude Code loads it when a task matches its description.

How do I install Decision Prompt Builder in Codex?

Run `npx skills add zhnnky329/MathModeling-skills --skill decision-prompt-builder -a codex`. Or copy the skill folder (.codex/skills/decision-prompt-builder in zhnnky329/MathModeling-skills) into .agents/skills/decision-prompt-builder in your project. Codex loads it when a task matches its description.

Can I use Decision Prompt Builder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zhnnky329/MathModeling-skills --skill decision-prompt-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/decision-prompt-builder, .gemini/skills/decision-prompt-builder, .github/skills/decision-prompt-builder and .opencode/skills/decision-prompt-builder in your project.

What does Decision Prompt Builder need to run?

SKILL.md names no scripts, command-line tools or credentials: Decision Prompt Builder is instructions for the agent only.

Does Decision Prompt Builder access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Decision Prompt Builder safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Decision Prompt Builder use?

Decision Prompt Builder is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Decision Prompt Builder use?

About 837 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Decision Prompt Builder?

Skills that share tags, products or a category with Decision Prompt Builder: Battle Card Builder (deanpeters/Product-Manager-Skills, 7.2k stars), Strategic Compact (affaan-m/ECC, 277k stars), Card Xiaohongshu (nexu-io/open-design, 100k stars) and Token Card (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Decision Prompt Builder?

zhnnky329 (a GitHub user) maintains it in zhnnky329/MathModeling-skills, which has 1,060 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on September 24, 2026.

Source: zhnnky329/MathModeling-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.