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Static Spa Conversion

Community
kevintsai1202
static-spa-conversion

Use this skill when you have structured course content (or any chapter-based dataset) in markdown form and need to turn it into a working interactive website — without picking a framework, without a build step. Triggers on phrases like "做成網頁", "轉成 SPA", "course-data.js", "render 函式", "把講義變網頁", "static site from markdown", "vanilla JS site", "no-framework site", "single-page app from markdown". The output is a vanilla HTML + JS single-page app that opens with `npx serve` and persists state in localStorage. Always invoke AFTER `course-content-authoring` (content stable), BEFORE `static-spa-interactions` (this skill produces the scaffold; interactions adorn it).

Overview

Publisherkevintsai1202
Repositoryteaching-site-skills
Skill namestatic-spa-conversion
Stars
52
Forks
10
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by kevintsai1202 on GitHub. Read the source before you install it.

Installation

Install the Static Spa Conversion 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/kevintsai1202/teaching-site-skills/tree/main/static-spa-conversion
  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/kevintsai1202/teaching-site-skills.git /tmp/teaching-site-skills
mkdir -p .claude/skills
cp -r /tmp/teaching-site-skills/static-spa-conversion .claude/skills/static-spa-conversion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Static Spa Conversion 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 Static Spa Conversion 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 Static Spa Conversion 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.

Static SPA Conversion

Schema authority: all window.COURSE field names come from _shared/domain-primitives.md. When the schema example below diverges from that file, that file wins.

Starter templates (copy these, don't re-derive):

Reference implementation: d:/GitHub/ai-workshop/index.html (4125 lines, fully populated). Use as visual + render-pattern reference; do not copy course-specific content from it.

Filename convention (English-first): course-package/, day{n}/outline.md, day{n}/content.md, materials/, overview.md. Legacy Chinese names (完整課程包/, 課程大綱.md, 教學素材/, etc.) are deprecated — see _shared/domain-primitives.md §0 for full mapping.

This skill turns chapter-based markdown content into a working single-page app. The architectural commitments are deliberate and worth understanding before you start.

The Three Architectural Commitments

  1. Vanilla HTML + JS, no framework, no bundler. No React, no Vite, no TypeScript build. Why: course websites have multi-year lifespans and are often handed to non-developer instructors. A .html you can open and edit beats a node_modules graveyard every time. Also: the content is the value, not the tech.

  2. Data and rendering live in different files. A course-data.js holds everything the course is as a single JS object (window.COURSE). An index.html holds how the course is shown. Instructors editing tomorrow's lesson never need to touch HTML.

  3. localStorage for persistence, no backend. Progress, theme, zoom — all client-side. Easier hosting, no privacy headaches. The cost: state is per-browser. Accept it.

Standard File Layout

project-root/
├── index.html              ← the single SPA (CSS + JS inline; ~3000 lines is normal)
├── course-data.js          ← window.COURSE = { ... }
├── instructor-data.js      ← optional: window.INSTRUCTOR (if scraped/external)
├── tools-data.js           ← optional: window.TOOLS (if showcasing tools)
├── package.json            ← just `{"scripts": {"serve": "npx serve ."}}` and scraping tooling
├── assets/                 ← images, illustrations
└── course-package/             ← markdown source (read-only from SPA's perspective)
    └── materials/           ← files linked from the SPA via getMaterialUrl()

index.html includes data files via plain <script src="course-data.js"></script> — no module imports, no fetch. The browser loads them in order, populating window.COURSE before init() runs.

The window.COURSE Schema

The single most important artifact. Design it carefully — every render function depends on it. Standard shape:

js
window.COURSE = {
  meta: {                              // top-level course metadata
    title: '...',
    audience: '...',
    hoursTotal: 24,
    days: [                            // index drives sidebar chapter list
      { id: 'day1', title: 'Day 1: ...', hours: 6, schedule: '...' },
      ...
    ],
    classroom: { name: '...', mapUrl: '...' }
  },
  sharedCase: { /* persistent fictional scenario */ },
  day1: {
    hero: { title, lead, illustration },
    units: [
      {
        id: 'u-1',                     // stable, never renamed
        title: '...',
        timeRange: '14:00–14:50',
        goals: [...],                  // learning outcomes
        tasks: [                       // ← these IDs are localStorage keys
          { id: 'd1-u1-t1', text: '...', done: false }
        ],
        prompts: [                     // optional: copyable templates
          { id: '...', title: '...', body: '...' }
        ],
        materials: [                   // optional: per-unit material refs
          { id: '...', name: '...', type: 'PDF 文件' }
        ],
        illustrations: [               // 1–3 entries, see Stage 5 Coverage Floor
          { name: 'day1-u1-hero.png',  kind: 'hero',    alt: '...', spec: '...' },
          { name: 'day1-u1-flow.svg',  kind: 'diagram', alt: '...', spec: '...' }
        ]
        // legacy single `illustration: 'day1-u1.png'` still accepted; treat as
        // illustrations: [{ name, kind: 'hero' }] and migrate when convenient
      },
      ...
    ]
  },
  day2: { /* same shape */ },
  ...
  materials: [                         // cross-day material index for the "all materials" tab
    { id: 'm-1', name: '...', type: '...', desc: '...' }
  ],
  quiz: [                              // optional: end-of-course assessment
    { id: 'q1', question: '...', options: [...], answer: 0, sourceUnit: 'day1.u-2' }
  ]
};

The Render Pipeline

index.html is one big <script> at the bottom. Its shape:

js
// 1. Utilities (top, ~50 lines)
const el = (tag, attrs, children) => { /* tiny DOM helper */ };
const store = {
  load() { /* read localStorage */ },
  save(state) { /* write localStorage */ },
  reset() { /* clear */ }
};

// 2. Render functions, each takes data → returns/appends DOM
function renderOverview(meta) { ... }
function renderSharedCase(case_) { ... }
function renderDay(day) { ... }
function renderUnit(unit, dayId) { ... }
function renderMaterials(materials) { ... }
function renderToolbox(tools) { ... }    // optional
function renderQuiz(quiz) { ... }        // optional

// 3. Sidebar + scrollspy + theme — see static-spa-interactions

// 4. Entry point
function init() {
  renderOverview(window.COURSE.meta);
  renderSharedCase(window.COURSE.sharedCase);
  for (const dayKey of ['day1', 'day2', 'day3', 'day4']) {
    renderDay(window.COURSE[dayKey]);
  }
  renderMaterials(window.COURSE.materials);
  renderQuiz(window.COURSE.quiz);
  // ... then interactivity wiring (see static-spa-interactions)
}
document.addEventListener('DOMContentLoaded', init);

Keep each render function pure: it receives data, it produces DOM. No reading from globals beyond its parameter. This makes adding a new section trivial.

The Material URL Router (getMaterialUrl)

The trickiest piece. The SPA needs to map a material's name and type to a real URL on disk. The router lives near the top of the script section:

js
function getMaterialUrl(name, type) {
  const base = 'course-package/materials';
  if (type === 'PDF 文件') {
    // PDFs trigger download instead of inline view
    if (name.includes('員工差勤')) return `${base}/pdf/員工差勤辦法.pdf`;
    if (name.includes('客訴SOP')) return `${base}/pdf/客訴處理SOP.pdf`;
    // ... one rule per PDF
    return null;  // unknown PDF → caller hides the link
  }
  // Other types open in new tab
  if (name.includes('FAQ')) return `${base}/FAQ官方版.md`;
  if (name.includes('差勤')) return `${base}/員工差勤辦法.md`;
  // ...
}

The three-place sync rule — when adding a material, update all of:

  1. Drop the file into course-package/materials/
  2. Add an entry to course-data.js:materials[] (and any unit's materials[])
  3. Add the name.includes(...) rule to getMaterialUrl()

Forgetting any one breaks the link. Consider adding a CI check that diffs filesystem vs. data vs. router.

The Task ID Lifecycle

js
// Save
function toggleTask(taskId) {
  const state = store.load();
  state.tasks[taskId] = !state.tasks[taskId];
  store.save(state);
  // re-render checkbox only, not whole page
}

// Load on render
function renderTask(task) {
  const state = store.load();
  const checked = !!state.tasks[task.id];
  return el('label', {}, [
    el('input', { type: 'checkbox', checked, onchange: () => toggleTask(task.id) }),
    task.text
  ]);
}

Never change task.id once published. The localStorage key tasks[task.id] is what survives across visits. Renaming wipes progress for every student.

Local Development

powershell
# Required: file:// blocks localStorage in Chrome — always serve via HTTP
npx serve .
# → http://localhost:3000

Add this to package.json:

json
{
  "scripts": {
    "serve": "npx serve ."
  }
}

Mention this prominently in any README. Double-clicking index.html produces a confusing "my progress doesn't save" bug that wastes hours.

Migration: Markdown → course-data.js

When porting an existing .md outline + content to course-data.js:

  1. Parse the outline first to get the day/unit skeleton.
  2. For each unit's content .md, extract tasks, prompts, materials references using grep-style passes (look for the marker patterns from course-content-authoring).
  3. Generate course-data.js as one big object literal. Don't try to make it pretty programmatically — let prettier/eslint format it, or just leave it as-is.
  4. Manually review: every unit ID in markdown should exist in course-data.js; the day count and hours should match meta.

When This Skill Hands Off

Tell the user:

  • "SPA scaffold is live. Run npm run serve and open http://localhost:3000."
  • "Next: invoke static-spa-interactions for progress persistence, sidebar, theme, RWD."
  • "Don't rename course-data.js paths or task IDs from this point — students' localStorage depends on them."

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 Static Spa Conversion AI skill do?

Use this skill when you have structured course content (or any chapter-based dataset) in markdown form and need to turn it into a working interactive website — without picking a framework, without a build step. Triggers on phrases like "做成網頁", "轉成 SPA", "course-data.js", "render 函式", "把講義變網頁", "static site from markdown", "vanilla JS site", "no-framework site", "single-page app from markdown". The output is a vanilla HTML + JS single-page app that opens with `npx serve` and persists state in localStorage. Always invoke AFTER `course-content-authoring` (content stable), BEFORE `static-spa-in...

Why use Static Spa Conversion on TypingMind?

Because you install it once and use it with any model. Static Spa Conversion 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 Static Spa Conversion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kevintsai1202/teaching-site-skills/tree/main/static-spa-conversion. 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 Static Spa Conversion?

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 Static Spa Conversion?

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

Is the Static Spa Conversion AI skill free?

Yes. It is published on GitHub by kevintsai1202 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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