#1
LLM驱动的 A/H/美股智能分析：多数据源行情 + 实时新闻 + LLM决策仪表盘 + 多渠道推送，零成本定时运行，纯白嫖. LLM-powered stock analysis system for A/H/US markets.
52K starsJun 29, 2026 pushfinancePythonFinance
$ npx skills add ZhuLinsen/daily_stock_analysisAlternatives
khquant alternatives for AI agents.
Compare similar skills by workflow fit, trust score, quality, GitHub adoption, maintenance, and install readiness.
Current skill
khquant
当用户提到"回测"、"看海量化"、"kh命令"、"kh web"、"网页回测"、"跑策略"、"下载股票数据"、"查看回测结果"、"双均线"、"MACD"、"RSI策略"、"KDJ"、"布林带"、"miniQMT"、"BaoStock"、"Tushare"、"tx数据源"、"同花顺"、"扶摇"、"沪深300"、"A股"、".kh配置文件"、"策略开发"、"K线"、"DuckDB"、"量化交易"、"khHandlebar"、"khGet"、"khPrice"、"khIndex"、"khHistory"、"khDuckDB"、"khMA"、"generate_signal"、"khAddExtraFields"、"MyTT"、"技术指标"、"交易信号"时使用。本 skill 是看海量化回测平台 CLI (kh) 的自然语言入口，支持首次配置、数据管理、策略开发、桌面与网页回测、结果分析和故障排查。
55
Quality
65
Trust
21
Stars
#2
Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
44K starsApr 22, 2026 pushfinancePythonFinance
$ npx skills add microsoft/qlib#3
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$ npx skills add ccxt/ccxt#4
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$ npx skills add juspay/hyperswitch#5
TradingAgents: Multi-Agents LLM Financial Trading Framework
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$ npx skills add actualbudget/actual#9
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$ npx skills add AI4Finance-Foundation/FinGPT#10
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$ npx skills add plotly/dash#11
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$ npx skills add hummingbot/hummingbot#12
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$ npx skills add firefly-iii/firefly-iii#14
Download market data from Yahoo! Finance's API
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$ npx skills add ranaroussi/yfinance#15
Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
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$ npx skills add QuantConnect/Lean#16
FinRL®: Financial Reinforcement Learning. 🔥
16K starsJul 13, 2026 pushfinanceJupyter NotebookTrading
$ npx skills add AI4Finance-Foundation/FinRLHow to choose
When should you switch?
Use an alternative when it has a clearer install path, higher trust score, fresher maintenance, or better platform fit for your current agent stack. Keep khquant if it already passes your workflow test and repository review.
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