Finance-LLMs
Reduces time-to-market for financial AI features by providing vetted, production-ready use case patterns and implementation guidance from industry practition…
Comprehensive Compilation of Real-World LLM & AI Agent Use Cases in Financial Services
- Ask Claude to analyze real-world financial service implementations using LLMs and AI agents
- Generate reference architectures for deploying language models in banking and trading applications
- Find best practices for building AI agents that handle financial data processing and analysis tasks
Reduces time-to-market for financial AI features by providing vetted, production-ready use case patterns and implementation guidance from industry practitioners.
Engineering teams building fintech platforms or trading systems needing validated LLM integration blueprints.
https://github.com/kennethleungty/Finance-LLMs
By kennethleungty
How to Get It
claude plugins install kennethleungty/Finance-LLMs
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Analyze real-world financial service implementations using LLMs and AI agents
Trust Signals Auto-scanned
Community Pulse New
- New finance LLM passed the CFA Level III exam — Hacker News · 14 pts
- Ask HN: Books for someone who is transitioning from FAANG to finance — Hacker News · 3 pts
- Show HN: Document OCR for Node.js with GPT Vision — Hacker News · 1 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 134 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 134 GitHub stars; contributors unknown; last commit 0d ago; license MIT.