by beita6969 · MCP Server · ★ 1.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/beita6969/ScienceClaw.gitTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 1,022 |
| Forks | 110 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65/100 |
| Open Issues | 10 |
| Last Updated | 2026-10-31 |
| Created | 2026-03-11 |
| Platforms | mcp, node |
| Est. Tokens | ~16k |
A self-evolving AI research colleague for scientists. Why ScienceClaw? General-purpose AI assistants are built for everyone. ScienceClaw is built for researchers. The core idea is simple: an AI that does real scientific work — searching literature, querying databases, running analyses — and gets better at it the more you use it.
Looking for a ScienceClaw alternative? If you're comparing ScienceClaw with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
ScienceClaw is a personal research assistant built with LangChain DeepAgents and AIO Sandbox infrastructure, a
BioMCP: Biomedical Model Context Protocol
Self-hosted OpenClaw gateway + agent runtime in .NET (NativeAOT-friendly)
🦞 ResearchClawBench: Evaluating AI Agents for Automated Research from Re-Discovery to New-Discovery
De-Anthropocentric Research Engine — AI-powered academic research automation with deep literature survey, gap
Let Skills Evolve Collectively with Agentic Evolver
Explore other popular mcp server tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: