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 brycewang-stanford · MCP Server · ★ 323
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is StatsPAI safe to install? View the security audit →
English | 中文 StatsPAI: Agent-Native Causal Inference & Econometrics Toolkit for Python [](https://doi
| Stars | 323 |
| Forks | 67 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 63.1666562408099/100 |
| Last Updated | 2026-09-22 |
| Created | 2025-07-26 |
| Platforms | mcp, python |
| Est. Tokens | ~25k |
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StatsPAI is StatsPAI is the first Agent-native Python library for causal inference and applied econometrics — unified API, broad cross-method coverage, structured result objects, machine-readable schemas, Skills,. It is categorized as a MCP Server with 323 GitHub stars.
StatsPAI is primarily written in Python. It covers topics such as agent-native, ai-agents, causal-discovery.
You can find installation instructions and usage details in the StatsPAI GitHub repository at github.com/brycewang-stanford/StatsPAI. The project has 323 stars and 67 forks, indicating an active community.
StatsPAI is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to StatsPAI on Agent Skills Hub include finai-research, wisp-science, BambooAI. 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: