by xuzhougeng · MCP Server · ★ 103
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/xuzhougeng/ScientificFigureLibrary.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 | 103 |
| Forks | 8 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69/100 |
| Open Issues | 3 |
| Last Updated | 2026-10-03 |
| Created | 2026-07-29 |
| Platforms | claude-code, codex, mcp, node |
| Est. Tokens | ~17k |
Scientific Figure Library Website · 简体中文 · Quickstart · User Guide (online) · Protocol · Releases · Wisp Science Scientific Figure Library (SFL) is a local-first MCP server and MCP App for your scientific figures.
These tools work well together with ScientificFigureLibrary for enhanced workflows:
Looking for a ScientificFigureLibrary alternative? If you're comparing ScientificFigureLibrary 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.
Structural memory for AI coding agents. Bi-temporal graph, MCP-native, zero LLM calls. Cursor · Claude Code ·
Composable mathematics tools for agents
Rent the model. Own everything else. Free, open-source AI app for real work.
Supercharge AI Agents, Safely
Google Analytics 4 data to AI agents, agentic workflows, and MCP clients. Give agents analysis-ready access to
Persistent memory for AI coding agents
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: