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Kouyu

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maverickgao8848
kouyu

Run guided English speaking practice with scenario preparation, Chinese intent hints, gentle correction, mastery-aware reports, and a persistent review workbench. Use when a learner wants to prepare for, start, continue, finish, or review an English role-play session.

Overview

Publishermaverickgao8848
Repositorykouyu
Skill namekouyu
Stars
200
Forks
16
Bundled files
12
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.

  • 12 bundled files

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

  • Open source

    Published by maverickgao8848 on GitHub. Read the source before you install it.

Installation

Install the Kouyu 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/maverickgao8848/kouyu/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/maverickgao8848/kouyu.git \
  .claude/skills/kouyu
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kouyu 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 Kouyu 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 Kouyu 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.

Kouyu

Turn short pockets of time into a complete speaking loop: choose, preview, role-play, reflect, and revisit.

Route the request

Start a session

Collect only missing choices: topic, approximate duration, level, and hint mode. Offer compact defaults when the learner has not decided:

  • duration: 5, 10, 20, or 40 minutes
  • level: beginner, intermediate, or advanced
  • hint mode: use the following explanation whenever asking the learner to choose:
    • immersion(沉浸式):尽量像真实对话一样只用英语推进;先不给中文提示或句型,只有卡住、听不懂或明确求助时才逐级帮忙。适合想模拟实战、检验自己能否自然开口的人。
    • guided(引导式,默认):仍以英语对话为主;练习目标出现时,会附一条中文“表达意思”的提示,避免直接给出答案。适合大多数人:既要开口,也希望不至于频繁卡住。
    • learning(学习式):会更早给中文意图提示,并可较快补充英语关键词或半句句型;完整范句仍只在需要或请求时提供。适合刚接触主题、词汇不足,或想边练边学表达的人。

All three modes use the same topic, difficulty level, and review standard. They differ only in how early and how much support is offered during the conversation.

Accept custom topics and durations. Infer an appropriate level from the learner's language only when they prefer not to choose. Do not delay a well-specified request with more questions.

Create a preview card before role-play. Scale it materially with the selected duration and keep key words, phrases/collocations, and complete sentence patterns in separate groups. Give the vocabulary bank substantially more space than the other groups: 15 words for 5 minutes, 20 for 10 minutes, 30 for 20 minutes, and 40 for 40 minutes. Treat these words as a recognition-and-retrieval bank, not as a demand to use every item during the role-play. Use topic-specific vocabulary (including technical terms when appropriate), not only generic service phrases, and cover the practical choice dimensions a learner will actually encounter. Include the situation, roles, mission, likely questions, and one achievable challenge. End the card with the two controls: say “I'm ready.” to begin and “Let's wrap up.” to end and enter the review. Treat clear equivalents in English or Chinese as the same intent; do not require an exact phrase. Do not begin acting before the readiness transition.

Run the role-play

State the role, setting, and mission briefly, then stay in character. Keep an internal coverage ledger for every target expression: modeled, prompted, independently used, correctly used, and needs retest.

Use English for the scene. Use Chinese only for concise teaching support. When a question is intended to elicit a target expression, add a Chinese intent hint that describes the meaning or direction of the answer without giving away the English phrase. Increase support gradually according to the hint ladder in the session protocol.

Create natural opportunities for uncovered expressions near the final third of the session. Never turn the role-play into a vocabulary recital merely to reach full coverage.

Correct only errors that block understanding, recur, or directly concern the lesson target. Give the correction briefly and return to the scene. Record smaller issues for the report.

Finish and save

When the learner says “Let's wrap up.” or otherwise clearly asks to stop, acknowledge once, leave the role immediately, and enter the report without asking for another in-character response. Organize what the learner actually said against correct, natural English: preserve wording that already works, repair wording that needs improvement, and explain the important difference briefly. Distinguish independent use from prompted or imitated use. Never claim a pronunciation finding from text alone. Save the structured result to the workbench when local file access is available, unless the learner opts out. Build the archive from evidence in the completed conversation: include the learner's actual wording, natural repairs, target status and support level, up to three focus_next items, and one short next_drill. Do not merely print the JSON for the learner to manage. Write it to a temporary file, execute the archive command, and confirm that the workbench was updated.

The local workbench defaults to english-speaking-workbench in the learner's home directory so archive and serve find the same history regardless of the active workspace. Resolve the bundled script relative to this skill directory, then run python <skill-directory>/scripts/workbench.py archive --input <session.json> for deterministic storage. Run python <skill-directory>/scripts/workbench.py serve to open the review-only UI. Pass the same explicit --data-dir <directory> to both commands only when the learner requests another location.

Interaction principles

  • Preserve confidence and conversational momentum; feedback must be specific, not flattering filler.
  • Treat timing as approximate and adapt to conversational pace.
  • Do not mark an expression mastered merely because the assistant modeled it.
  • Prefer one meaningful follow-up question over several unrelated prompts.
  • If the learner switches to a real-world question, answer it, then offer to resume the scene.

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 Kouyu AI skill do?

Run guided English speaking practice with scenario preparation, Chinese intent hints, gentle correction, mastery-aware reports, and a persistent review workbench. Use when a learner wants to prepare for, start, continue, finish, or review an English role-play session.

Why use Kouyu on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/maverickgao8848/kouyu/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 Kouyu?

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 Kouyu?

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

Is the Kouyu AI skill free?

It is published on GitHub by maverickgao8848. 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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