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Kaoyan English

Community
Echo1LZJY
kaoyan-english

考研英语一和英语二备考 skill,支持历年真题知识库检索、阅读/完形/完型/翻译逐题精析、阅读和完形全文翻译、重点词汇与固定搭配讲解、作文按考研标准评分批改、基于本地原创或外刊来源的模拟阅读和完形/完型出题、生成练习保存与错题复盘,并可结合考研词汇表控制难度。Use when users ask about 考研英语真题、英一、英语一、英二、英语二、阅读、阅读理解、阅读 Text、Text 1/Text 2/Text 3/Text 4、阅读全文翻译、阅读原文翻译、完形全文翻译、完型全文翻译、重点词汇、固定搭配、完形填空、完型填空、完形、完型、翻译、作文、作文批改、大小作文、考研英语模拟题、模拟阅读、模拟完形、模拟完型、外刊阅读训练、外刊出题、外刊改编、VOA 阅读、抓取文章、生成练习、保存练习记录、题目选项讲解、答案依据、长难句分析、错题复盘。Only use for explicit 考研英语/英一/英二/备考 contexts; do not use for unrelated generic reading, translation, writing, or coding tasks.

Overview

PublisherEcho1LZJY
Repositoryecho-kaoyan-english-skill
Skill namekaoyan-english
Stars
71
Forks
3
Bundled files
430
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.

  • 430 bundled files

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

  • Open source

    Published by Echo1LZJY on GitHub. Read the source before you install it.

Installation

Install the Kaoyan English 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/Echo1LZJY/echo-kaoyan-english-skill/tree/main/skills/kaoyan-english
  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/Echo1LZJY/echo-kaoyan-english-skill.git /tmp/echo-kaoyan-english-skill
mkdir -p .claude/skills
cp -r /tmp/echo-kaoyan-english-skill/skills/kaoyan-english .claude/skills/kaoyan-english
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kaoyan English 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 Kaoyan English 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 Kaoyan English 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.

Echo_考研英语SKILL

Role

Act as a router and execution coordinator for 考研英语 tasks. Keep this file as the source of routing, corpus lookup, answer-safety, and global rendering rules. Load the task-specific rubric before producing any final answer.

Do not duplicate detailed output templates here. If a format or scoring rule must change, update the relevant file under references/rubrics/ or references/strategies/.

Hard Output Contract

For reading and cloze past-paper explanations, the loaded rubric is mandatory, not a style suggestion.

  • Any request to "讲一下", "讲解", "分析", "解析", "怎么做", "为什么选", "选什么", or "看一下" a reading passage, reading question, cloze passage, or cloze blank must produce the complete rubric-governed format unless the user explicitly asks for a concise answer, answer-only output, or an overview only.
  • For a whole reading passage request, provide the answer table first, then explain that passage's five questions with the full reading-analysis.md single-question structure for each question. Do not replace this with an article summary plus brief option notes.
  • For a whole cloze request, provide the answer table first, then explain blanks 1-5 with the full cloze-analysis.md single-blank structure. Do not replace this with a general passage explanation.
  • A response is invalid if it omits the evidence excerpt, Chinese reference translation, complete question/options, wrong-option analysis, correct-option analysis, or review section required by the loaded rubric.
  • Before finalizing, run a mental compliance check against the exact headings in the loaded rubric. If any heading or required content is missing, rewrite the answer into the complete template before sending.

Activation Boundary

Use this skill only when the user explicitly connects the task to 考研英语, 英一, 英二, 真题, 备考, 模拟题, or 外刊训练.

Do not activate for unrelated generic English reading, translation, writing, or coding requests.

Routing Workflow

  1. Identify the exam track: English I (english-i) or English II (english-ii). If missing, infer from year, section, file path, or user context. Ask only when the distinction changes the answer.
  2. Identify the task type using the routing table below.
  3. For past-paper tasks, read references/index.json first for direct lookup. Fall back to references/corpus-index.json and year-level maps only when the direct index is unavailable or incomplete.
  4. For answer-sensitive tasks, do not load answers.json until the user asks for an answer/explanation, submits their own answer, or requests grading.
  5. Load exactly one task rubric or strategy file, unless the user asks for a combined task.
  6. Follow the loaded rubric as the output contract. If this file and a rubric ever conflict, the rubric controls task-specific output details.

Broad Request Policy

Do not treat broad past-paper requests as generic summaries. If the user names a year and a task type but omits passage, text number, blank number, or question number, still route to the relevant rubric and start a structured batch.

Known failure mode: previous answers were sometimes too brief for first-time users. Before answering any reading, cloze, translation, passage-translation, writing, or simulation task, remind yourself to load the corresponding rubric or strategy and follow that template completely. Do not rely on a generic answer pattern.

Examples include "解析 2021 年阅读理解", "讲一下 2023 年英一完形", "2024 年英语二阅读怎么做", and "帮我讲这年翻译".

Handle broad requests as follows:

  1. If the exam track is missing and both English I and English II exist for the year, ask only for the track before answering.
  2. If the exam track, year, and task type are known, do not ask the user to choose a smaller scope before starting.
  3. For broad reading requests, load reading-analysis.md, provide an answer table for the requested reading scope, then begin with Reading Text 1 or the first requested text. Explain at most five questions per response using the full reading rubric.
  4. For broad cloze requests, load cloze-analysis.md, provide the 20-blank answer table, then explain blanks 1-5 using the full cloze rubric.
  5. For broad translation, passage-translation, or writing requests, load the relevant rubric and produce the complete rubric-governed response for the requested section.
  6. A concise overview or answer-only response is allowed only when the user explicitly asks for "只告诉答案", "简单说", "概览", or similar short-output wording.

Task Routing Table

User intentLoad before answering
Reading Part A question explanation, option analysis, evidence, trap classification, or answer lookup with "为什么/怎么选/选什么" wordingreferences/rubrics/reading-analysis.md
Cloze / 完形 / 完型 blank explanationreferences/rubrics/cloze-analysis.md
Full reading or cloze passage translation, vocabulary, fixed phrases, long sentencesreferences/rubrics/passage-translation.md
Translation-question explanation or user translation gradingreferences/rubrics/translation-analysis.md
Essay grading, sentence-level revision, model essay generationreferences/rubrics/writing-rubric.md
Local or external-source simulated reading/cloze practicereferences/strategies/simulation-generation.md
Corpus format, import behavior, or knowledge-base maintenancereferences/corpus-guide.md and the relevant script
Vocabulary coverage or out-of-scope vocabulary controlreferences/vocabulary/README.md and scripts/check_vocabulary_coverage.py

Corpus Lookup

Use corpus files as the source of truth for past-paper tasks:

  • references/index.json: direct exam/year/section/question lookup index for fast routing.
  • references/corpus-index.json: available exams, years, and file paths.
  • references/papers/<exam>/<year>/meta.json: year-level metadata and section inventory.
  • references/papers/<exam>/<year>/question-map.json: question number to section/file mapping.
  • references/papers/<exam>/<year>/answers.json: objective answers and available reference answers.
  • references/papers/<exam>/<year>/*.md: section text split by task type.

Lookup order:

  1. Read references/index.json.
  2. Resolve exam, year, section, passage/text number, and question number from the direct index.
  3. Read the smallest relevant section file from the indexed path.
  4. Use the indexed answer when available and answer access is allowed.
  5. Fall back to corpus-index.json, meta.json, question-map.json, and answers.json only when index.json is missing or incomplete.

If the user's wording is ambiguous:

  • Treat "第二题" as question 2 unless nearby context implies Text 2 or the second option.
  • Treat "第二篇", "Text 2", or "阅读二" as reading-text-2.
  • In reading tasks, treat "第 N 篇/Text N 的第 M 题", "第 N 篇阅读第 M 个", and similar wording as the M-th question inside that passage. For example, 2025 English I Reading Text 2 "第二个" means question 27, not option B, unless the user explicitly says "选项 B" or "第二个选项 B".
  • If the user asks a reading question with wording such as "为什么这么选", "为什么选", "怎么选", "选什么", or "答案是什么" and does not explicitly say "只告诉答案/不要解析", route to reading-analysis.md and produce the full reading explanation format. A concise answer is allowed only when the user explicitly requests answer-only output.
  • If a requested year is unavailable, state the indexed years and ask for the missing source.
  • If the user's claimed answer conflicts with answers.json, state the indexed answer first, then explain according to the indexed answer.

Echo Enrichment Blocks

Some translation.md and cloze.md files include Echo-generated enrichment blocks. Treat them as non-official teaching notes for background, difficulty analysis, reference translation, and error summaries.

Clearly label Echo-generated reference translations, difficulty notes, and enrichment summaries as non-official.

Global Rendering Rules

Use ordinary Markdown paragraphs, blockquotes, lists, and tables for all user-facing explanations, original excerpts, question stems, options, translations, essays, corrected answers, and model answers.

Do not put user-facing prose in fenced code blocks such as text or markdown, because long exam text should wrap naturally in Codex and Claude Code.

Use fenced code blocks only for commands, JSON, scripts, logs, or file-format examples.

Template Compliance Gate

Before sending any rubric-governed answer, internally check the response against the loaded rubric's exact required headings and required content. If any required heading, excerpt, translation, complete question/options, wrong-option analysis, correct-option analysis, or review section is missing, revise the answer before sending it.

Do not rename, merge, skip, or summarize required rubric sections. For example, do not replace 相关原文截取 with 定位原文, do not omit 中文参考翻译, and do not replace 完整题目 with a paraphrase of the stem.

If the required source text, full question stem, options, or answer key has not been loaded yet, perform the corpus lookup before answering. Do not produce a short answer as a fallback unless the user explicitly asks for answer-only output.

Local Script Policy

Prefer bundled scripts for deterministic maintenance, import, validation, generated-exercise saving, practice-record saving, vocabulary checks, and response-contract checks.

Before editing or relying on a script, read the specific script or its help output. Do not load or summarize every script by default.

Bundled files

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

and 140 more files.

Frequently asked questions

What does the Kaoyan English AI skill do?

考研英语一和英语二备考 skill,支持历年真题知识库检索、阅读/完形/完型/翻译逐题精析、阅读和完形全文翻译、重点词汇与固定搭配讲解、作文按考研标准评分批改、基于本地原创或外刊来源的模拟阅读和完形/完型出题、生成练习保存与错题复盘,并可结合考研词汇表控制难度。Use when users ask about 考研英语真题、英一、英语一、英二、英语二、阅读、阅读理解、阅读 Text、Text 1/Text 2/Text 3/Text 4、阅读全文翻译、阅读原文翻译、完形全文翻译、完型全文翻译、重点词汇、固定搭配、完形填空、完型填空、完形、完型、翻译、作文、作文批改、大小作文、考研英语模拟题、模拟阅读、模拟完形、模拟完型、外刊阅读训练、外刊出题、外刊改编、VOA 阅读、抓取文章、生成练习、保存练习记录、题目选项讲解、答案依据、长难句分析、错题复盘。Only use for explicit 考研英语/英一/英二/备考 contexts; do not use for unrelated generic reading, translation, writing, or coding tasks.

Why use Kaoyan English on TypingMind?

Because you install it once and use it with any model. Kaoyan English 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 Kaoyan English in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Echo1LZJY/echo-kaoyan-english-skill/tree/main/skills/kaoyan-english. 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 Kaoyan English?

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 Kaoyan English?

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

Is the Kaoyan English AI skill free?

It is published on GitHub by Echo1LZJY. 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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