by kennethleungty · Agent Tool · ★ 146
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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.
| Stars | 146 |
| Forks | 26 |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 53/100 |
| Open Issues | 1 |
| Last Updated | 2026-11-03 |
| Created | 2023-09-25 |
| Est. Tokens | ~28k |
Compilation of Real-World LLM & AI Agent Use Cases in Financial Services Context Large language models (LLMs) and AI agents are transforming financial services: from streamlining regulatory compliance and accelerating deal analysis to personalizing customer experiences and automating complex workflows across banking, trading, and asset management.
These tools work well together with Finance-LLMs for enhanced workflows:
Looking for a Finance-LLMs alternative? If you're comparing Finance-LLMs with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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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: