No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by joeseesun · Claude Skill · ★ 112
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is defuddle-skill safe to install? View the security audit →
defuddle-skill A Claude Code skill that wraps defuddle — extract clean article content from web pages, removing ads, sidebars, and clutter. Install Option 1: npx (recommended) This will: Install CLI globally (if not already installed) Copy the skill to Option 2: Vercel Skills CLI Option 3: Manual What it does Once installed, Claude Code can automatically extract clean content from any URL: Triggers: "defuddle", "extract article", "clean this page", "get content from URL" Output: Clean Markdown or JSON with metadata (title, author, date, word count) Usage examples Ask Claude Code: CLI Reference The skill uses CLI under the hood: bash defuddle parse [options] Options: -m, --markdown Convert to Markdown -j, --json Output as JSON with metadata -o, --output Save to file -p, --property Extract single field (title, author, published, etc.) --debug
| Stars | 112 |
| Forks | 10 |
| Language | JavaScript |
| Category | Claude Skill |
| Quality Score | 65.8400913089415/100 |
| Last Updated | 2026-03-03 |
| Created | 2026-03-03 |
| Platforms | browser, claude-code, node |
| Est. Tokens | ~1k |
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defuddle-skill is Claude Code skill for defuddle - extract clean article content from web pages. It is categorized as a Claude Skill with 112 GitHub stars.
defuddle-skill is primarily written in JavaScript.
You can find installation instructions and usage details in the defuddle-skill GitHub repository at github.com/joeseesun/defuddle-skill. The project has 112 stars and 10 forks, indicating an active community.
The top alternatives to defuddle-skill on Agent Skills Hub include claude-ai-mcp, skyll. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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