by csmar432 · MCP Server · ★ 100
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Evidence-first AI workflow for economic and financial research: literature → identification → data → econometrics → verifiable LaTeX. 43 data sources, 58 method modules, 18 AI skills, 30 journal templates.
| Stars | 100 |
| Forks | 2 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 56.7413440058258/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-16 |
| Created | 2026-06-15 |
| Platforms | mcp, python |
| Est. Tokens | ~13k |
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finai-research is Evidence-first AI workflow for economic and financial research: literature → identification → data → econometrics → verifiable LaTeX. 43 data sources, 58 method modules, 18 AI skills, 30 journal templ. It is categorized as a MCP Server with 100 GitHub stars.
finai-research is primarily written in Python. It covers topics such as academic-research, ai-agents, causal-inference.
You can find installation instructions and usage details in the finai-research GitHub repository at github.com/csmar432/finai-research. The project has 100 stars and 2 forks, indicating an active community.
finai-research is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to finai-research on Agent Skills Hub include StatsPAI, Auto-Research-Skills, paperreading. 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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