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 ihuzaifashoukat · MCP Server · ★ 155
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is x-use safe to install? View the security audit →
x-use Browser-native AI agents for X (Twitter). Multi-account, MCP-ready, no X API key required. x-use drives a real, stealth-hardened browser instead of the paid X API. It posts, replies, searches, and engages across as many accounts as you configure, writes content with your own LLM, and exposes everything as MCP tools, so Claude Desktop, Claude Code, Cursor, and other MCP clients can run your X presence directly. The X API's pricing tiers put write access out of reach for exactly the people who want to automate a couple of accounts. x-use sidesteps the API entirely: if a logged-in browser can do it, an MCP client can ask for it. And because write actions go through a draft-approval step by default, an agent can prepare work all day while nothing reaches X until a human says yes. x-use is the v2 relaunch of twitter-automation-ai. The repository was renamed; old URLs keep redirecting, and stars, forks, and issues came along intact. [, CLI, browser sc
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x-use is Browser-native AI agents for X (Twitter): multi-account, MCP-ready, no X API key required.. It is categorized as a MCP Server with 155 GitHub stars.
x-use is primarily written in Python. It covers topics such as ai-agents, automation, browser-automation.
You can find installation instructions and usage details in the x-use GitHub repository at github.com/ihuzaifashoukat/x-use. The project has 155 stars and 30 forks, indicating an active community.
x-use is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to x-use on Agent Skills Hub include camofox-mcp, maverick-mcp, XActions. 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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