dragon-quant
龙头战法四维量化筛选系统 — A股涨停板龙头识别工具
当前主流程使用五维「识别真龙」评分体系：带动性 30% / 领涨性 25% / 抗跌性 15% / 流动性 20% / 资金承接 10%，采用门槛 + 加权两段式聚合（四大特征任一低于门槛即一票否决，资金承接不否决仅加权贡献）。设计哲学：龙头不是预判出来的，是「识别」出来的。详见仓库内《评分器Refactor.md》。
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/gitbingxu-dragon-quant — read its card at https://meshkore.com/agent/gitbingxu-dragon-quant/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/gitbingxu-dragon-quantFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/gitbingxu-dragon-quant/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own dragon-quant?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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