How the beautiful-article skill transforms URLs and documents into HTML articles

The beautiful-article skill does not exist in the ConardLi/garden-skills repository, so no implementation details are available; however, a standard implementation would fetch remote content, parse the document structure, sanitize the markup, and generate semantic HTML articles.

The ConardLi/garden-skills repository hosts automation scripts and AI-powered utilities, including the gpt-image-2 skill for image generation. Despite references to a beautiful-article capability that converts URLs and documents into clean HTML, the source code analysis confirms this skill is absent from the current codebase.

Repository Analysis: Absence of the beautiful-article Skill

During source code analysis, systematic searches for filenames and directories matching beautiful* and article patterns returned no results. The repository tree only contains website/ and skills/gpt-image-2/, with no skills/beautiful-article/ directory present. This confirms the skill is not implemented in the current version of ConardLi/garden-skills.

Expected Implementation Pattern for URL-to-HTML Conversion

Although the skill is missing, the typical architecture for such a feature follows a four-stage pipeline. Based on the patterns used in the existing skills/gpt-image-2/ directory and standard Node.js practices, the implementation would likely reside in skills/beautiful-article/index.js and execute the following workflow:

Fetching Target Content

The skill would initiate an HTTP request to retrieve raw content from a supplied URL. In a Node.js environment, this typically uses axios or the native fetch API to handle network operations:

const axios = require('axios');

async function fetchContent(url) {
  const response = await axios.get(url);
  return response.data;
}

Parsing and Extraction

After retrieval, the skill would parse the HTML to isolate the main article body, metadata, and images. Libraries such as jsdom or cheerio extract semantic elements while discarding navigation bars, advertisements, and footers:

const { JSDOM } = require('jsdom');

function extractArticle(html) {
  const dom = new JSDOM(html);
  const document = dom.window.document;
  
  const title = document.querySelector('h1')?.textContent || 'Untitled';
  const body = document.querySelector('article') || document.querySelector('main');
  
  return { title, html: body?.innerHTML || '' };
}

Sanitization and Styling

The extracted markup undergoes cleaning to remove inline scripts, normalize image URLs, and apply a consistent CSS theme. This step ensures the output is safe for embedding and visually uniform:

const DOMPurify = require('isomorphic-dompurify');

function cleanAndStyle(html) {
  const clean = DOMPurify.sanitize(html, { 
    ALLOWED_TAGS: ['p', 'h1', 'h2', 'h3', 'img', 'a', 'strong', 'em'] 
  });
  return `<div class="article-body">${clean}</div>`;
}

HTML Generation

Finally, the skill would inject the cleaned content into a semantic HTML template, returning a complete document string ready for rendering:

function generateHTML(title, bodyContent) {
  return `
<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <title>${title}</title>
  <link rel="stylesheet" href="/styles/article.css">
</head>
<body>
  <article>
    <h1>${title}</h1>
    ${bodyContent}
  </article>
</body>
</html>
  `;
}

Verifying Skill Existence in garden-skills

To confirm whether the beautiful-article skill is available in your local clone or deployment, inspect the skills/ directory:

ls -la skills/

# Expected output: gpt-image-2

# Missing: beautiful-article

If the directory is absent, the skill functionality is not implemented in that version of the repository.

Summary

• The beautiful-article skill is not implemented in the ConardLi/garden-skills repository.
• The codebase only contains skills/gpt-image-2/ and website/ directories at the top level.
• A complete implementation would require four stages: content fetching, document parsing, markup sanitization, and HTML templating.
• Developers looking to add this feature should create a new skills/beautiful-article/ directory with an entry point handling URL input and HTML output.

Frequently Asked Questions

Is the beautiful-article skill included in the garden-skills repository?

No. Source code analysis confirms the skill is not present. The repository only contains the gpt-image-2 skill under skills/gpt-image-2/ and a website/ directory for project documentation. No files match the "beautiful-article" naming pattern.

How would a beautiful-article skill theoretically transform a URL into HTML?

It would follow a pipeline: fetch the URL content using HTTP clients like axios, parse the HTML with tools like jsdom or cheerio to extract the article body, sanitize the markup to remove scripts and ads, and inject the cleaned content into a semantic HTML template with applied CSS classes.

Where should the beautiful-article skill be located if it were added to the repository?

Following the existing convention, it would reside in skills/beautiful-article/. The entry point would likely be skills/beautiful-article/index.js (or main.py for Python implementations), with supporting modules for parsing and templating stored in the same directory.

Can I contribute a beautiful-article skill to the garden-skills project?

Yes. You can implement the fetch-parse-template workflow in a new skills/beautiful-article/ directory. Ensure your implementation exports a function that accepts a URL string or document path and returns a sanitized HTML string, matching the async patterns used in skills/gpt-image-2/.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}