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 AgentTeam-TaichuAI · MCP Server · ★ 635
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ScienceClaw safe to install? View the security audit →
ScienceClaw English | 中文 ScienceClaw is a personal research assistant built with LangChain DeepAgents and AIO Sandbox infrastructure, adopting a completely new architecture beyond OpenClaw. It offers stronger security, better transparency, and a more user-friendly experience. 1,900+ built-in scientific tools · Multi-format content generation · Fully local & privacy-first Why ScienceClaw · Architecture · News · Quick Start · [Demo](#de
| Stars | 635 |
| Forks | 69 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 60.4257205616881/100 |
| Open Issues | 13 |
| Last Updated | 2026-05-09 |
| Created | 2026-03-13 |
| Platforms | docker, mcp, python |
| Est. Tokens | ~143k |
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ScienceClaw is ScienceClaw is a personal research assistant built with LangChain DeepAgents and AIO Sandbox infrastructure, adopting a completely new architecture beyond OpenClaw. It offers stronger security, better. It is categorized as a MCP Server with 635 GitHub stars.
ScienceClaw is primarily written in Python. It covers topics such as ai-agent, bioinformatics, data-analysis.
You can find installation instructions and usage details in the ScienceClaw GitHub repository at github.com/AgentTeam-TaichuAI/ScienceClaw. The project has 635 stars and 69 forks, indicating an active community.
The top alternatives to ScienceClaw on Agent Skills Hub include ScienceClaw, full-stack-ai-agent-template, 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: