defuddle_skill

Overview

This skill extracts clean, readable Markdown content from web pages using the Defuddle CLI, removing navigation, ads, and other clutter to save tokens. It is the preferred extractor for standard documentation, articles, and blog posts when a URL is provided. The skill outputs focused Markdown or JSON and can also capture specific metadata properties.

How this skill works

The skill invokes the Defuddle CLI to parse the provided URL and request Markdown output (--md) by default. It can write results to a file, return JSON that includes both HTML and Markdown, or fetch specific metadata properties (title, description, domain) with -p. It strips extraneous page chrome so downstream language models receive concise, high-value content.

When to use it

• You have a URL to a standard web page (docs, blog post, article) and need clean text for analysis or summarization.
• You want to reduce token usage by removing navigation, ads, and sidebars before feeding content to a model.
• You need Markdown output ready for notes, documentation, or publishing workflows.
• You want structured metadata (title, description, domain) alongside page content.
• You prefer a deterministic CLI-based extractor instead of a general web fetch when page is static or server-rendered.

Best practices

• Always request Markdown (--md) for compact, model-friendly output.
• Save content to a file when reusing it across tasks (defuddle parse <url> --md -o content.md).
• Install Defuddle globally if not present (npm install -g defuddle-cli) and ensure PATH access.
• If a page is heavily client-side (SPA), verify server-rendered HTML or use a renderer before parsing.
• Request specific metadata (-p title/description/domain) when you only need small context fields.

Example use cases

• Convert a technical blog post into clean Markdown before summarization or code extraction.
• Harvest documentation pages into a knowledge-base or note system with minimal manual cleanup.
• Preprocess multiple article URLs to reduce token cost before feeding them to an LLM pipeline.
• Extract page title and description to auto-populate metadata fields in publishing workflows.
• Pull readable content for offline review, diffing, or archiving in Markdown format.