by lightpanda-io · Codex Skill · ★ 101
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Lightpanda Skill for LLM Agents A skill that provides integration with Lightpanda, a fast headless browser optimized for data extraction and web automation. What is this? This is a generalist skill for LLM agents (Claude Code, Openclaw, and others) that teaches agents how to use Lightpanda as a drop-in replacement for Chrome/Chromium when performing web scraping and automation tasks. Features Faster and lighter than Chrome for headless operations (9x faster, 16x less memory) Native MCP server with agent-optimized tools CLI fetch command with wait strategies and multiple output formats CDP (Chrome DevTools Protocol) compatible with custom LP domain extensions Works with Playwright and Puppeteer JavaScript execution support Semantic tree and markdown extraction Installation Copy to your agent's skills directory, then run the install script to download the Lightpanda binary: Quick Setup for Claude Code (MCP) Three Ways to Use Lightpanda Platform
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 →
| Stars | 101 |
| Forks | 10 |
| Language | Shell |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 64.79690693753/100 |
| Open Issues | 3 |
| Last Updated | 2026-10-06 |
| Created | 2026-02-05 |
| Platforms | browser, cli |
| Est. Tokens | ~7k |
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agent-skill is Lightpanda browser skill for openclaw. It is categorized as a Codex Skill with 101 GitHub stars.
agent-skill is primarily written in Shell.
You can find installation instructions and usage details in the agent-skill GitHub repository at github.com/lightpanda-io/agent-skill. The project has 101 stars and 10 forks, indicating an active community.
agent-skill is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to agent-skill on Agent Skills Hub include theorist. 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.
Sources & who's responsible: