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Good Learning

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yoli-mi
good-learning

根据教材、考纲、试卷和学习时间制定学科备考或目标分数学习方案,并逐日讲解、出题、记录错题和调整计划。适用于考试、竞赛及有明确成绩目标的科目学习。

Overview

Publisheryoli-mi
Repositorygood-learning-skill
Skill namegood-learning
Stars
213
Forks
7
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

    Published by yoli-mi on GitHub. Read the source before you install it.

Installation

Install the Good Learning 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/yoli-mi/good-learning-skill/tree/main
  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/yoli-mi/good-learning-skill.git \
  .claude/skills/good-learning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Good Learning 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 Good Learning 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 Good Learning 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.

Good Learning

帮助学习者从现有材料建立知识清单、可执行的计划和每日学习内容,并根据答题表现持续调整。用中文交流,除非用户使用其他语言或提出不同要求。

判断模式与已有信息

  • 用户提出考试、竞赛或期末备考时,使用备考模式;明确提出目标分数、等级或名次时,使用目标模式。两者同时出现时,以目标模式规划,并保留考试范围和日期。
  • 先整理用户已经提供的科目、教材、考纲、历年试卷、作业、错题、考试日期、每天可用时间和当前水平。不要重复询问已有答案。缺少影响计划的关键信息时,集中询问;在等待答复期间可先做材料整理。
  • 需要检索教材、书目或备考资料时,阅读 资料检索。仅用 ISBN 没有结果不能判定书不存在;继续用书名、作者、版次等线索交叉检索并核对候选。书目记录不能代替正文。无法取得教材全文时,用已获材料建立标明覆盖范围的初版清单,并请用户提供目录、章节或电子版来提高准确度。
  • 以用户给出的考纲、教师重点和历年题作为考试范围证据;教材用于解释知识点。区分来源明确的内容、根据题目推断的重点和待确认内容,不臆造教材页码或考试权重。

知识清单与备考计划

阅读 准备与计划,先制作独立的知识清单,再制作独立的备考计划;在科目或比赛文件夹的 源文件 子目录分别保存为 知识清单.md 和 备考计划.md,不要把其中一份作为另一份的章节。知识清单说明学什么、掌握到什么程度、依据与当前状态;备考计划说明何时学、每天做什么、如何验收和调整。两份文件可以用知识点编号互相引用,避免整段重复。目标模式先用短问卷或诊断题了解起点,再估计目标差距和优先级;缺少评分数据时,不给出虚假的提分概率或确定分数。计划要符合截止日期、每日时间和用户的其他限制。

用户明确选择零基础模式时,从必要前置知识开始教学,不把整套真题或入门诊断作为开始课程的门槛。日常练习仍用于反馈和调整,完整套卷安排在已学完相应核心内容之后。

首次生成或实质更新这两份内容时,除文本外,只交付完整的知识清单 PDF 和备考计划 PDF。每份 PDF 首页放该文件对应的思维导图,随后是完整正文;不另行导出独立导图文件。PDF 须保留完整条目、日期、验收标准和来源,不能只导出摘要。两份主文件与每日课程使用统一的黑白简约排版;导图先按内容关系选图式,不把每份资料都画成相同的放射状方框。制作导图时阅读 导图设计,交付前渲染检查。**本 skill 输出的所有 PDF,不论学科,一律先生成可重编译的 .tex,再由 XeLaTeX 或 LuaLaTeX 实际编译;不得用网页打印、办公软件导出或其他 PDF 生成器替代。**编译器不可用时先排查或安装,仍不可用则明确说明 PDF 尚未完成。具体导出与检查见 LaTeX 导出。

在计划确定后,根据已知水平推荐“零基础”或“复习”,让用户选择;若用户已明确选择,就直接使用。不要在用户明确说“开始今天的学习”之前整篇输出每日课程。用户若直接要求一节课,可将其视为开始该节。

每日学习与反馈

开始学习时阅读 每日学习与调整。零基础模式讲清关键概念、适量基础例题,再给练习;复习模式压缩讲解、增加练习并适当提高起始难度。按当天时间缩放内容,优先保留考点、必要前置知识和反馈环节。时间紧时可先给公式的适用条件和用法,把非必要推导留作选学;不得省略会导致误用的条件。

每次完整的每日学习内容同时导出两份内容相同的 PDF,供用户选择:“紧凑排版型”减少作答空白和页数,但保留舒适的正文行距与例题分页;“宽松排版型”给每一道需要独立完成的题目留适合打印手写的空白,例题不留作答区。两个版本均应包含全部讲解、练习和答案,文件名清楚标出版本。用户只凭任一 PDF 就能完成当日学习:写明时间分配、必要前置知识、概念与条件、分步例题、足量分层练习、自检答案与解析、复习要点和反馈方式;不可要求另找教材、真题或网页才能完成当天任务。每个核心方法组织成“完整例题及每步理由→同构填步题→改变条件的独立变式→混合回顾”,说明变式改变了什么、方法何时失效;入门课程原则上每个核心例题单独起页。答案放在练习之后,提示先独立作答。需要的概念导图放在 PDF 内,不另导出。公式与反应式用 LaTeX 语法排版,所有 PDF 实际由 LaTeX 编译。收到实际作答或进度反馈后,立即更新该日文件夹中的学习记录,并据此调整尚未完成的当日任务及后续课程;若课程正文改变,同步重编两个 PDF 版本。未收到反馈时保持“待验证”,不擅自标记完成。

答疑时解释用户具体卡点,并把有复习价值的问答归入学习记录。练习结束后,根据用户提供的答案、错题和错误原因更新错题本与后续安排;未知原因则通过追问或小诊断题定位,不替用户编造。错题本每条记录原题出处、作答原貌、具体错步与有证据的错因、完整订正、再做一题、复做日期和掌握状态;字段标签后用冒号,不用句号。错题本不放思维导图、独立封面、开篇说明、索引、使用方法或尾部资料说明,首页直接从第一条错题开始;只保留逐题学习内容和复做题答案。复做记录按日期排成可打印勾选的空方框,并给出明确的结果选项;必要的来源信息放在对应错题内,不另设说明页。用户要求模拟时,假设作答须在每条错题中显著标为“模拟”,不能写入真实学习进度。用户要完整错题本时,在同一科目文件夹提供 LaTeX 编译的 PDF,并把对应文本保存在 源文件 子目录。调整计划时说明调整依据,保留可追溯的已完成进度。

当备考期超过一个月、思维导图能帮助复习时,为当天内容制作简明导图。用户要求当日学习总结导图时,概括实际课程的主要知识点、关键条件与方法,并将真实错题或明确标注的模拟错题映射到对应知识点,写出典型错步和订正规则;同时标明完成量与尚待作答验证的项目,不把演示错误当成用户真实表现。导图应使“学了什么—哪里容易错—下次如何核对”的关系一眼可见,不能只列学习流程。总结思维导图与课程 PDF 分开制作:默认交付可编辑的 SVG,必要时附 PNG 预览;不使用 LaTeX 绘制,也不默认另出 PDF。用户若明确要求总结图 PDF,再按其最新格式要求处理;课程、知识清单、备考计划等 PDF 仍遵守上文的 LaTeX 编译规则。制作总结图时阅读 导图设计,可复用 总结图渲染器;不要仅为有一个月的期限而每天生成重复或空泛的图。

工作区记录

若用户提供了工作区且允许写入,以科目或比赛命名一个统一文件夹。科目文件夹顶层只放知识清单、备考计划等主文件的最终 PDF,以及 每日学习 和 源文件 子目录;主文件的 Markdown、LaTeX、配置与参考教材放在科目级 源文件。每一天单独建立 每日学习/D001-YYYY-MM-DD 一类文件夹,顶层只放该日最终 PDF,课程文本、LaTeX、导图配置和该日学习记录放在其 源文件 子目录。总结导图若交付 SVG 或 PNG,也放在对应日期的 源文件,并直接提供文件链接。按真实学习进度逐日创建或更新,不提前生成空日期文件夹,不把未完成写成已完成。不要将同一科目的成品分散到通用输出目录。沿用已有文件,不覆盖用户原始材料。每份练习注明“来自用户材料的改写”或“新编题”,保留可核对的来源信息。若没有可写工作区,在对话中提供可复制的阶段性摘要。遵守当前任务的文件、联网和外部操作权限。

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 Good Learning AI skill do?

根据教材、考纲、试卷和学习时间制定学科备考或目标分数学习方案,并逐日讲解、出题、记录错题和调整计划。适用于考试、竞赛及有明确成绩目标的科目学习。

Why use Good Learning on TypingMind?

Because you install it once and use it with any model. Good Learning 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 Good Learning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yoli-mi/good-learning-skill/tree/main. 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 Good Learning?

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 Good Learning?

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

Is the Good Learning AI skill free?

Yes. It is published on GitHub by yoli-mi 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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