StatsPAI: First Agent-native Python library for causal inference and applied econometrics

by brycewang-stanford · MCP Server · ★ 367

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 63/100

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.

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.

agent-nativeai-agentscausal-discoverycausal-inferencedata-sciencedifference-in-differencesdouble-machine-learningeconometricsinstrumental-variablesllm

Quick Facts

Stars367
Forks76
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score63/100
Last Updated2026-11-07
Created2025-07-26
Platformsmcp, python
Est. Tokens~25k

About StatsPAI

English | 中文 StatsPAI: Agent-Native Causal Inference & Econometrics Toolkit for Python [](https://doi

Compatible Skills

These tools work well together with StatsPAI for enhanced workflows:

  • DeepEye — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)
  • stata-skill — semantic(0.34)+complementary+same_lang+similar_pop+shared_platform (57%)
  • stata-mcp — semantic(0.35)+rare_topics+same_lang+similar_pop+shared_platform (56%)
  • statsclaw — semantic(0.30)+complementary+rare_topics+similar_pop (56%)

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How this security grade is produced

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:

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