awesome-ai-for-economists: A curated list of AI tools, agent skills, libraries, and resources for economics research, teaching

by hanlulong · MCP Server · ★ 703

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

Security audit verdict: SAFE · quality 55/100

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. 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.

agent-skillsaiartificial-intelligenceawesomeawesome-listcausal-inferenceclaude-skillseconometricseconomicseconomists

Quick Facts

Stars703
Forks160
CategoryMCP Server
LicenseCC0-1.0
Quality Score55/100
Open Issues1
Last Updated2026-11-07
Created2026-02-21
Platformsclaude-code, mcp
Est. Tokens~28k

About awesome-ai-for-economists

Awesome AI for Economists A curated list of AI tools, agent skills, libraries, and resources for economics research, teaching, and policy analysis. Scope: AI tools and resources with specific value for economists, plus research on the economics of AI. General-purpose AI assistants and coding agents are in the Appendix. Highlights: Projects from the OpenEcon team, which maintains this list.

Compatible Skills

These tools work well together with awesome-ai-for-economists for enhanced workflows:

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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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