Qwen Dianjin — Development skill for Claude Code
Qwen DianJin: LLMs for the Financial Industry by Alibaba Cloud(通义点金:阿里云金融大模型).
How to install Qwen Dianjin
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open aliyun/qwen-dianjin and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Qwen Dianjin does
Qwen DianJin: LLMs for the Financial Industry by Alibaba Cloud(通义点金:阿里云金融大模型).
Alternatives in Development
- Industry Funnel — 行业漏斗筛选:从全市场到 3 家的价值投资精选流程 15.9k ★
- Financial Services Plugins — Plugins that turn Claude into a specialist for financial services — investment banking, equity research, priva 7.7k ★
- Ralphy — My Ralph Wiggum setup, an autonomous bash script that runs Claude Code, Codex, OpenCode, Cursor agent, Qwen & 2.8k ★
README
🚀 News
- 2026.08.25 🎉 "FinGuard: Detecting Financial Regulatory Non-Compliance in LLM Interactions" has been accepted to Findings of EMNLP 2026!
- 2026.08.17 🎉 **DianJin-RED** is released — an action-grounded red-teaming benchmark for complete agent systems, with 1,661 executable cases, 15 intervention strategies (user input, agent-platform state, and external tools/data), and isolated service worlds.
- 2026.05.20 🎉 **DianJin-SKILLS (百技图)** is open-sourced — an AI Agent skill library for finance covering banking, insurance, and securities/asset management: 10 professional roles, 130+ standardized skills, ready to plug into Agent frameworks.
- 2026.05.10 🎉 "Fin-PRM: A Domain-Specialized Process Reward Model for Financial Reasoning in Large Language Models" has been accepted by IJCAI 2026!
- 2026.04.07 🎉 "Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset" has been accepted by ACL 2026 main conference!
- 2026.02.27 🎉 Our papers FinMCP-Bench and CARE have been accepted by ICASSP 2026!
- 2025.11.15 "Evaluating, Synthesizing, and Enhancing for Customer Support Conversation" has been officially accepted by AAAI 2026!
- 2025.10.11 "[FinMCP-Bench: Benchmarking LLM Agents for Real-World
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