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编程手

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XiaoMaColtAI
编程手

数学建模的 Python 或 MATLAB 实现、运行、表格输出、可视化和复现阶段。

Overview

PublisherXiaoMaColtAI
Repositorymath-modeling-skill
Skill name编程手
Stars
1.6K
Forks
84
Bundled files
11
Links
Skill sourceRepositoryHomepage
  • 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.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the 编程手 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.
  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.

Use it in TypingMind

Enable 编程手 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 编程手 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 编程手 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.

编程手

路径

  • ROLE_ROOT：本文件所在目录。
  • SKILL_ROOT：ROLE_ROOT/../../..，只读。
  • PROJECT_ROOT：用户项目目录，所有代码、结果和图只写这里。

输入

优先读取 PROJECT_ROOT/题目分析报告.md、PROJECT_ROOT/术语表格.md 和题目附件。若用户只执行本阶段，可从用户提供的模型说明开始；若说明不足以实现，先反馈缺项。

固定产物

  • Python .py、MATLAB .m，或用户要求的两套实现。
  • results/ 中的运行结果表格和必要文本结果。
  • figures/ 中的原始数据图、模型运行过程图、模型最终结果图，每类至少 3 张逻辑候选图、合计至少 9 张，并覆盖全部子问题：每个子问题在三类中各至少 1 张。文件名使用 raw_q1_*、process_q1_*、result_q1_* 等格式，不设上限；同一图的 SVG、PNG 和灰度预览只计 1 张。允许继续多生成，不机械删除有效备选视角。
  • 必须至少 1 幅总体建模流程图 flow_overall_model.*，覆盖输入、子问题依赖、模型、求解、验证与输出；子问题存在独立分支、循环或三步以上求解链时，补充 flow_qN_model.*。与三类候选图分开，不计入其配额，也不替代结果图。
  • results/复现清单.json。

执行顺序

  1. 按用户要求或现有项目语言选择 Python/MATLAB；没有偏好时按模型依赖和现有环境选择并说明。
  2. 按选中的模型功能动态检查依赖，禁止一次性要求全部包：
    • Python：python scripts/check_env.py --features data visualization optimization
    • MATLAB：check_matlab_env(["data","visualization","optimization"])
  3. 实现数据读取、预处理和核心求解链，用真实输入或结构等价小实例跑通从 PROJECT_ROOT 执行的最小命令；任何结论必须来自真实输出。
  4. 在全量计算、参数扫描和正式出图前，派发独立质检 Subagent 执行 P1 最小可运行结果门禁；实现问题由编程手修正，模型合同问题携证据返回建模手。未返回 PASS 不得继续扩展。
  5. 从题目分析报告提取全部子问题并规范为 q1…qN。绘图前加载 tools/figure/SKILL.md 并完成数据剖析与图表契约，按子问题核对行列、类型、缺失、分组样本量、分布、异常值和单位；先用一句话写出核心结论，再选择图型、证据面板、主次比例、统计口径、图例策略与最终尺寸。无法判断图型或用户指定图型存在误导风险时，读取 tools/figure/references/chart-types/chart_selection.md。
  6. 将 scripts/plot_style.py 或 MATLAB 的三个出版绘图工具复制到 PROJECT_ROOT/utils/ 后使用。按 tools/figure/SKILL.md 的 Nature/SCI 视觉论证流程生成三类候选图，每类至少 3 张、合计至少 9 张，且每个子问题在三类中各至少 1 张：不把不同重要性的面板机械等分，不用长标题、密集逐点标记、装饰性纹理或面板内重复图例堆成仪表盘；统计标注必须由代码计算，官方模板要求优先于内置基线。
  7. 所有正式图必须经 tools/figure/scripts/export_figure.py 或 export_publication_figure() 的布局与设计门禁，同时输出 SVG 与至少 300 DPI PNG；再运行 python "<SKILL_ROOT>/references/roles/编程手/scripts/figure_audit.py" "<PROJECT_ROOT>/figures" --questions q1 q2 ... qN --strict，实际打开彩色 PNG 和灰度预览，在论文预计尺寸下检查视觉层级、缺字、裁切、遮挡、颜色、尺度和面板一致性。有问题则改代码、重跑、重审，不能关闭门禁后继续，也不能直接修改位图。
  8. 生成建模流程图（必须）：至少 1 幅总体流程图，覆盖 输入 → 子问题依赖 → 模型 → 求解 → 验证 → 输出；仅当子问题存在独立分支、循环或三步以上求解链时，才补充该子问题的 flow_qN_model。节点与连线必须来自已确认的模型和真实代码，用已安装的 Matplotlib/MATLAB 原生绘图能力生成（不新增依赖），保留可运行生成代码；样式、命名、导出与自检见 tools/figure/references/chart-types/flowchart.md。
  9. 生成复现清单：python scripts/repro_manifest.py --project-root <PROJECT_ROOT> ...。
  10. 按 references/质检清单.md 完成作者自检，再派发独立质检 Subagent 执行 P2 编程终检；未返回 PASS 不得进入论文阶段或宣称编程交付完成。

阶段内独立门禁

  • P1：质检 Subagent 在隔离环境或只读副本中执行最小命令，核对退出码、输入到结果的追溯、单位、数值范围、关键约束和 M1 模型合同。它是纵向切片，不要求完整图表或最终性能。
  • P2：代码、结果、每类至少 3 张且覆盖全部子问题的三类图和复现清单冻结后，质检 Subagent 独立运行唯一复现命令并核对输入哈希、种子、关键数值、边界、量纲、各子问题图表语义及文件完整性。机械图审继续使用带全部 --questions 的 figure_audit.py --strict，Subagent 负责实际读图，检查单图核心结论、主次面板、图例、统计区间和最终尺寸可读性，而不是只确认文件能打开；并核对建模流程图与真实模型/代码一致、覆盖全部子问题且未替代结果图。

两次门禁均按 ../../../references/Subagent调度.md 返回证据；被审代码、数据或参数发生实质变化时重跑受影响门禁。

何时加载

情形读取
开始实现references/工作流程.md
使用 MATLABreferences/MATLAB规范.md
画图tools/figure/SKILL.md
不确定用什么图，或需审查指定图型tools/figure/references/chart-types/chart_selection.md
需要图表函数tools/figure/references/api-templates/plot_recipes.md
需要具体算法../../../references/算法索引.md，再读取匹配的 ../../../assets/*.md
处理 Excel../../../tools/xlsx/SKILL.md
交付前references/质检清单.md
阶段内独立验收../../../references/Subagent调度.md

若实际运行证明模型公式、约束或参数定义冲突，停止通过改算法规避问题，把证据反馈给建模手。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the 编程手 AI skill do?

数学建模的 Python 或 MATLAB 实现、运行、表格输出、可视化和复现阶段。

Why use 编程手 on TypingMind?

Because you install it once and use it with any model. 编程手 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 编程手 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/XiaoMaColtAI/math-modeling-skill/tree/main/dsh-plugin/math-modeling-agent/skills/math-modeling/references/roles/编程手. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use 编程手?

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 编程手?

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

Is the 编程手 AI skill free?

It is published on GitHub by XiaoMaColtAI. Check the repository for licensing terms. 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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