defuddle

Extract clean markdown content from web pages using Defuddle CLI, removing clutter and navigation to save tokens. Use instead of WebFetch when the user provides a URL to read or analyze, for online documentation, articles, blog posts, or any standard web page.

31,392 stars
Complexity: medium

About this skill

The 'defuddle' skill leverages the Defuddle CLI to transform any standard web page (such as articles, blog posts, online documentation, or news pages) into clean, readable markdown content. It intelligently strips away common web page clutter, including navigation menus, advertisements, sidebars, footers, and other extraneous elements, to isolate the primary textual content. This process significantly reduces the amount of data an AI agent needs to process, leading to improved token efficiency, faster processing, and more focused analysis or summarization. Integrated within the 'antigravity-awesome-skills' collection, 'defuddle' is designed to be a superior alternative to generic web fetching methods when the goal is to analyze or extract concise information from human-readable web content without being overwhelmed by noise.

Best use case

Efficiently process, summarize, or analyze web page content by stripping away visual clutter and extraneous information, ensuring AI agents focus only on the core text. This is particularly useful for tasks requiring concise information extraction from online articles, documentation, or blog posts, where token usage and content purity are critical considerations.

Extract clean markdown content from web pages using Defuddle CLI, removing clutter and navigation to save tokens. Use instead of WebFetch when the user provides a URL to read or analyze, for online documentation, articles, blog posts, or any standard web page.

The agent will receive a clean, well-structured markdown representation of the primary content from the provided URL, free of typical web page clutter. This processed content will be significantly shorter than raw HTML, enabling more efficient analysis, summarization, or other text-based tasks by the AI agent, with lower token consumption and a clearer focus on relevant information.

Practical example

Example input

Please summarize the main points of this article: `https://www.theverge.com/23971408/ai-agents-google-openai-microsoft-anthropic-future`

Example output

The article discusses the emerging trend of 'AI agents' developed by tech giants like Google, OpenAI, Microsoft, and Anthropic. These agents are designed to perform complex, multi-step tasks autonomously by chaining together various tools and making decisions, rather than just responding to single prompts. Key points include their potential to revolutionize productivity, the challenges of ensuring reliability and safety, and the vision for a future where AI handles more intricate workflows, freeing up human time for higher-level tasks. The author also touches on the ethical implications and the race among companies to define this new frontier of AI.

When to use this skill

  • When an AI agent needs to read, summarize, or analyze the primary content of a standard web page provided by a user's URL.
  • To maximize token efficiency and reduce processing costs by eliminating irrelevant content like ads, navigation, and sidebars before feeding text to the agent.
  • For extracting clean, structured text from online articles, blog posts, documentation, news pages, or any content-heavy URL.
  • As a preferred alternative to generic 'WebFetch' or raw page-scraping methods when the goal is content understanding rather than raw HTML inspection.

When not to use this skill

  • When the task requires interacting with interactive web elements, forms, JavaScript-driven components, or dynamically loaded content.
  • If the AI agent needs to analyze the full, raw HTML structure, specific metadata, styling, or non-textual elements (images, videos) of a web page beyond their textual context.
  • For extracting data from highly dynamic web applications or Single Page Applications (SPAs) that heavily rely on client-side rendering beyond initial content load, which Defuddle might not fully process.
  • When the user's intent is to view the entire visual layout or specific non-content elements of a page, rather than just the core text.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/defuddle/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/plugins/antigravity-awesome-skills-claude/skills/defuddle/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/defuddle/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How defuddle Compares

Feature / AgentdefuddleStandard Approach
Platform SupportClaudeLimited / Varies
Context Awareness High Baseline
Installation ComplexitymediumN/A

Frequently Asked Questions

What does this skill do?

Extract clean markdown content from web pages using Defuddle CLI, removing clutter and navigation to save tokens. Use instead of WebFetch when the user provides a URL to read or analyze, for online documentation, articles, blog posts, or any standard web page.

Which AI agents support this skill?

This skill is designed for Claude.

How difficult is it to install?

The installation complexity is rated as medium. You can find the installation instructions above.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Defuddle

Use Defuddle CLI to extract clean readable content from web pages. Prefer over WebFetch for standard web pages — it removes navigation, ads, and clutter, reducing token usage.

## When to Use

- Use when the user provides a normal webpage URL to read, summarize, or analyze.
- Prefer it over noisy page-fetch approaches when token efficiency matters.
- Use for docs, articles, blog posts, and similar public web content.

If not installed: `npm install -g defuddle`

## Usage

Always use `--md` for markdown output:

```bash
defuddle parse <url> --md
```

Save to file:

```bash
defuddle parse <url> --md -o content.md
```

Extract specific metadata:

```bash
defuddle parse <url> -p title
defuddle parse <url> -p description
defuddle parse <url> -p domain
```

## Output formats

| Flag | Format |
|------|--------|
| `--md` | Markdown (default choice) |
| `--json` | JSON with both HTML and markdown |
| (none) | HTML |
| `-p <name>` | Specific metadata property |

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