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 quantmind · MCP Server · ★ 52
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is quantflow safe to install? View the security audit →
Quantitative analysis and pricing tools. Installation Optional dependencies — data retrieval: — training the Deep Implied Volatility model: Features Stochastic Processes: a library of continuous-time models including Wiener processes, Poisson jumps, CIR mean-reverting dynamics, Heston stochastic volatility, jump-diffusion models, and the Barndorff-Nielsen & Shephard (BNS) model. Each process exposes its [characteristic function](https://quantflow.quantmind.com/theory/charac
| Stars | 52 |
| Forks | 12 |
| Language | Python |
| Category | MCP Server |
| License | BSD-3-Clause |
| Quality Score | 60.6948021931457/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-16 |
| Created | 2014-01-08 |
| Platforms | mcp, python |
| Est. Tokens | ~14k |
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quantflow is Quantitative finance and derivative pricing. It is categorized as a MCP Server with 52 GitHub stars.
quantflow is primarily written in Python. It covers topics such as ai-tools, cox-ingersoll-ross, finance.
You can find installation instructions and usage details in the quantflow GitHub repository at github.com/quantmind/quantflow. The project has 52 stars and 12 forks, indicating an active community.
quantflow is released under the BSD-3-Clause license, making it free to use and modify according to the license terms.
The top alternatives to quantflow on Agent Skills Hub include dartlab, MCP-Defender, langchainjs-mcp-adapters. 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: