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Wjs Teaching English

Community
jianshuo
wjs-teaching-english

Use when the user wants to teach / learn an English word as a video — turn a single English word into a self-contained HLS "supercut" lesson built from the mira video base. Stitches every season2 clip where the word is spoken (via the search-app API) into one .m3u8, prepended with a Claude-written bilingual word-intro card (word + IPA + 中文 gloss + usage, Volcano TTS) and appended with a 关注王建硕 CTA card. No MP4 burn. Triggers — "teach <word>", "讲讲 <word>", "学英语 <word>", "把 <word> 做成视频", "/wjs-teaching-english <word>".

Overview

Publisherjianshuo
Repositoryclaude-skills
Skill namewjs-teaching-english
Stars
137
Forks
22
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 jianshuo on GitHub. Read the source before you install it.

Installation

Install the Wjs Teaching 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/jianshuo/claude-skills/tree/main/wjs-teaching-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/jianshuo/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/wjs-teaching-english .claude/skills/wjs-teaching-english
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wjs Teaching 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 Wjs Teaching 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 Wjs Teaching 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.

Teach an English word as a video supercut

Turn one English word into a self-contained HLS lesson:

intro.ts  (word /IPA/ · 中文 gloss · usage + Volcano TTS)
   ⋯ EXT-X-DISCONTINUITY
[supercut] every season2 clip where the word is spoken  (search-app /api/playlist, COS URLs)
   ⋯ EXT-X-DISCONTINUITY
cta.ts    (关注王建硕 + Volcano TTS)
= search-app/out/<word>.m3u8

No MP4 is burned — only the two cards are rendered as tiny .ts, re-encoded to match the supercut's codec so they play in any HLS player.

Prerequisites (check once)

  • ffmpeg / ffprobe on PATH (Homebrew).
  • Python volcengine SDK (declared in mira requirements.txt, used for TTS): python3 -c "import volcengine". If missing, ask the user to allow pip3 install volcengine==1.0.58 (it's a pinned repo dependency).
  • The deployed search-app at https://search-app-three-kappa.vercel.app (default). Override with SEARCH_APP_BASE=http://localhost:3000 if running locally (needs Node ≥ 23.6 + npm start).

Steps

  1. Get the word. A single English word (or short phrase). If the user gave a sentence, pick the target word.

  2. Write the mini-lesson JSON. YOU (Claude) author it — no dictionary API. Keep it accurate and concise. Save to a temp file, e.g. /tmp/lesson.json:

    json
    {
      "word": "love",
      "ipa": "/lʌv/",
      "pos": "v. / n.",
      "gloss": "爱,热爱",
      "usage": "下面是它在真实电影里的说法",
      "tts_text": "love. 爱。"
    }
    • tts_text is read aloud over the intro card — keep it to the word + a short 中文 gloss (Volcano reads mixed English/中文 fine).
    • usage is one short line shown on the card (≤ ~20 chars renders best at low resolutions).
  3. Build it:

    bash
    cd /Users/jianshuo/code/mira/search-app
    python3 scripts/build_lesson.py --word love --lesson /tmp/lesson.json

    Useful flags: --speaker zh_female_qingxin (default), --limit 300 (max clips), --no-tts (silent cards), --base <url>, --out <dir>.

  4. Report the printed output path (search-app/out/<word>.m3u8) and the clip count. The .m3u8 plus its sibling <word>.intro.ts / <word>.cta.ts are the deliverable; out/ is git-ignored.

Notes

  • If the word has no clips, the script exits with "no clips match" and writes nothing — tell the user and suggest a more common word.
  • Intro/CTA are encoded to the FIRST supercut segment's resolution/codec/fps so the first discontinuity needs no decoder re-init (avoids bufferAppendError).
  • To play locally: ffplay search-app/out/<word>.m3u8, or serve the out/ folder over HTTP and open in any HLS player (Safari plays .m3u8 natively).

Frequently asked questions

What does the Wjs Teaching English AI skill do?

Use when the user wants to teach / learn an English word as a video — turn a single English word into a self-contained HLS "supercut" lesson built from the mira video base. Stitches every season2 clip where the word is spoken (via the search-app API) into one .m3u8, prepended with a Claude-written bilingual word-intro card (word + IPA + 中文 gloss + usage, Volcano TTS) and appended with a 关注王建硕 CTA card. No MP4 burn. Triggers — "teach <word>", "讲讲 <word>", "学英语 <word>", "把 <word> 做成视频", "/wjs-teaching-english <word>".

Why use Wjs Teaching English on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jianshuo/claude-skills/tree/main/wjs-teaching-english. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wjs Teaching 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 Wjs Teaching English?

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

Is the Wjs Teaching English AI skill free?

Yes. It is published on GitHub by jianshuo 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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