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 xuzhougeng · MCP Server · ★ 1.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is wisp-science safe to install? View the security audit →
Wisp Science — Local-first AI research workbench English | 简体中文 Wisp Science is an open-source, local-first desktop AI research assistant and scientific computing workbench. It connects to OpenAI-compatible and Anthropic models, runs persistent Python and R environments on local, SSH, WSL, and GPU compute, loads reusable Agent Skills (), and reaches 80 bioinformatics and computational biology databases through bundled Model Context Protocol (MCP) servers. Built with Rust, Tauri v2, and Leptos, Wisp Science runs as a cross-platform desktop app or a headless CLI. Our manifesto: Wisp Science is open source and borderless. We are building a scientific workbench that
| Stars | 1,156 |
| Forks | 118 |
| Language | Rust |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 67.7512557688656/100 |
| Open Issues | 8 |
| Last Updated | 2026-09-20 |
| Created | 2026-07-01 |
| Platforms | mcp, rust |
| Est. Tokens | ~27k |
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wisp-science is Open-source, local-first desktop AI research workbench for scientific computing with Python/R, MCP bioinformatics tools, SSH/WSL/GPU runtimes, and OpenAI/Anthropic models.. It is categorized as a MCP Server with 1.2k GitHub stars.
wisp-science is primarily written in Rust. It covers topics such as agent-skills, ai-agent, ai-assistant.
You can find installation instructions and usage details in the wisp-science GitHub repository at github.com/xuzhougeng/wisp-science. The project has 1.2k stars and 118 forks, indicating an active community.
wisp-science is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to wisp-science on Agent Skills Hub include open-science, open-science, k-dense-byok. 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: