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 morluto · MCP Server · ★ 196
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is jacobian safe to install? View the security audit →
English · 简体中文 Jacobian An executable mathematical vocabulary for agents: discover one typed operation, run it, and compose its result. Jacobian is an MCP server that gives AI agents a searchable vocabulary of typed mathematical operations. matches a mathematical need or inspects one exact contract, and executes it and returns its typed result. The same mathematical library is also available through a CLI and native Python API. Each operation
| Stars | 196 |
| Forks | 14 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65.0855801961976/100 |
| Open Issues | 96 |
| Last Updated | 2026-10-04 |
| Created | 2026-07-23 |
| Platforms | mcp, python |
| Est. Tokens | ~15k |
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jacobian is Composable mathematics tools for agents. It is categorized as a MCP Server with 196 GitHub stars.
jacobian is primarily written in Python. It covers topics such as ai-agents, automated-theorem-proving, computer-algebra.
You can find installation instructions and usage details in the jacobian GitHub repository at github.com/morluto/jacobian. The project has 196 stars and 14 forks, indicating an active community.
jacobian is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to jacobian on Agent Skills Hub include agent-skills, maverick-mcp, open-ontologies. 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.
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