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 IMNMV · MCP Server · ★ 337
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ClaudeR safe to install? View the security audit →
ClaudeR - The Modern Researcher's Toolkit Connect RStudio to Claude Code, Codex, Qwen Code, Gemini CLI, Antigravity CLI, or any MCP-based LLM agent for interactive coding, multi-agent orchestration, automated manuscript auditing, and data annotation. ClaudeR is an R package that forges a direct link between RStudio and MCP configured LLM agents like Claude Code,
| Stars | 337 |
| Forks | 38 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 67.076097327079/100 |
| Open Issues | 17 |
| Last Updated | 2026-09-15 |
| Created | 2025-03-18 |
| Platforms | claude-code, cli, codex, gemini, mcp, python |
| Est. Tokens | ~24k |
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ClaudeR is Connect RStudio to Claude Code, Codex, Gemini, and other LLM agents via MCP. Multi-agent orchestration, automated manuscript auditing, and zero-config setup with uvx. It is categorized as a MCP Server with 337 GitHub stars.
ClaudeR is primarily written in Python. It covers topics such as agent, ai, claude.
You can find installation instructions and usage details in the ClaudeR GitHub repository at github.com/IMNMV/ClaudeR. The project has 337 stars and 38 forks, indicating an active community.
The top alternatives to ClaudeR on Agent Skills Hub include agnix, postman-mcp-server, tuui. 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: