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Senior English Vocabulary Daily

Community
hezkvectory
senior-english-vocabulary-daily

把高中英语每日打卡做成 10-15 分钟可坚持、可反馈、可复现的短训练。重点不是多刷题,而是围绕词汇复现、语境理解、拼写准确和句子输出形成当天闭环。 Workflow: senior_english_vocabulary_daily.run.

Overview

Publisherhezkvectory
Repositoryhermes-edu-skills
Skill namesenior-english-vocabulary-daily
Stars
127
Forks
58
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 hezkvectory on GitHub. Read the source before you install it.

Installation

Install the Senior English Vocabulary Daily 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/hezkvectory/hermes-edu-skills/tree/main/skills/daily-practice/senior-english-vocabulary-daily
  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/hezkvectory/hermes-edu-skills.git /tmp/hermes-edu-skills
mkdir -p .claude/skills
cp -r /tmp/hermes-edu-skills/skills/daily-practice/senior-english-vocabulary-daily .claude/skills/senior-english-vocabulary-daily
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Senior English Vocabulary Daily 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 Senior English Vocabulary Daily 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 Senior English Vocabulary Daily 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.

高中英语词汇每日练 Skill

把高中英语每日打卡做成 10-15 分钟可坚持、可反馈、可复现的短训练。重点不是多刷题,而是围绕词汇复现、语境理解、拼写准确和句子输出形成当天闭环。

这个 Skill 解决什么问题 / Problem

把高中英语每日打卡做成 10-15 分钟可坚持、可反馈、可复现的短训练。重点不是多刷题,而是围绕词汇复现、语境理解、拼写准确和句子输出形成当天闭环。

最适合 / Best For

  • 高中英语每日打卡
  • 英语同步巩固
  • 课后短练
  • 错题后的同类回练
  • 适合晨读、晚间默写和单元词汇复现

不适合 / Not For

  • 一次性刷大量题目
  • 只给答案不记录错因
  • 替代系统课程学习
  • 只背单词表,不在语境中使用,隔天就忘

使用前请准备 / Inputs

  • 年级/水平
  • 今天要练的单元或知识点
  • 可用时间
  • 最近错题或薄弱点
  • 使用场景:学生自练、家长陪练或老师布置

推荐工作流 / Recommended Workflow

  • 先确认今日目标、时间和难度,只练一个小目标。
  • 先确认今天要练的单词/句型/语篇,并让学生主动回忆含义或用法
  • 混合听写、选择、补全、造句和短阅读,不只背中英对照
  • 反馈要区分不会读、不会拼、不会用和语法搭配问题
  • 把易混词和错句放入下一次间隔复习并要求重新造句

输出格式 / Output Format

  • 今日目标
  • 热身回想
  • 短练题组
  • 答案与讲解
  • 错因标签
  • 下次复习

质量检查 / Quality Checks

  • 题量必须匹配 10-15 分钟
  • 先让学生尝试再讲解
  • 反馈必须包含错因
  • 必须留下下一次复习动作

没有平台工具时 / Standalone Fallback

  • 没有题库时,由 Agent 生成原创短练并附答案。
  • 没有学习记录时,用用户提供的错题或薄弱点开始。
  • 没有计时工具时,输出可手动执行的三轮练习表。

示例提示 / Example Prompts

  • 高中英语今天练词汇,只有 15 分钟,帮我安排。
  • 孩子英语最近错在词汇,先诊断再做 5 道短练。

适用场景 / When To Use

当学习者、家长、老师、学校或教育应用开发者需要处理以下场景时,可以使用这个 Skill。

最适合的场景:

  • 每日打卡
  • 高考复习

适用角色:

  • 学习者

调用信号 / Invocation Signals

意图:

  • senior_english_vocabulary_daily
  • daily_practice
  • 每日打卡
  • 高考复习

示例表达:

  • 开始高中英语词汇每日练 Skill
  • 帮我做每日打卡
  • 根据当前上下文执行高中英语词汇每日练 Skill

公开 Skill 契约 / Public Skill Contract

  • Workflow: senior_english_vocabulary_daily.run
  • Category: daily-practice
  • Stages: senior
  • Subjects: 英语
  • Abilities: 词汇
  • Quality Tier: curated
  • Standalone Support: needs_user_input
  • Public Release: recommended
  • Requires Tools: context.load, entitlement.check, workflow.create, plan.generate, memory.write
  • Requires Data: 年级/水平, 今日知识点或单元, 可用时间, 最近错题或薄弱点, 使用场景:学生自练/家长陪练/老师布置
  • Export Mode: installable
  • Release Channel: recommended

成熟度备注:

  • 已按精品 Skill 标准补充边界、输入、工作流、输出格式和示例。

参数化使用 / Parameters

这个 Skill 不再把年级、册别、单元、知识点和难度拆成大量独立 Skill。请在调用时通过参数或自然语言补充这些信息。

  • Grades: 高一, 高二, 高三
  • Semesters: 必修一, 必修二, 选择性必修
  • Scenarios: 每日打卡, 高考复习
  • Difficulties: 基础, 标准, 提高
  • Parameterized Dimensions: grade, semester, unit, lesson, knowledgePointCodes, scenario, difficulty

独立 Hermes 使用方式 / Standalone Hermes Usage

这个 Skill 可以通过 Hermes 的 skills.external_dirs 作为外部 Skill 加载。

如果你有自己的工具、记忆、课程数据或 workflow runner,可以把它们与本 Skill 组合使用。如果没有外部工具,也可以直接使用上面的说明来引导对话,生成有用的学习或教学反馈。

Frequently asked questions

What does the Senior English Vocabulary Daily AI skill do?

把高中英语每日打卡做成 10-15 分钟可坚持、可反馈、可复现的短训练。重点不是多刷题,而是围绕词汇复现、语境理解、拼写准确和句子输出形成当天闭环。 Workflow: senior_english_vocabulary_daily.run.

Why use Senior English Vocabulary Daily on TypingMind?

Because you install it once and use it with any model. Senior English Vocabulary Daily 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 Senior English Vocabulary Daily in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hezkvectory/hermes-edu-skills/tree/main/skills/daily-practice/senior-english-vocabulary-daily. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Senior English Vocabulary Daily?

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 Senior English Vocabulary Daily?

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

Is the Senior English Vocabulary Daily AI skill free?

Yes. It is published on GitHub by hezkvectory 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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