Loading...
Loading...
Found 203 Skills
Major index real-time quotes via Longbridge Securities — Shanghai Composite, CSI 300, ChiNext, Hang Seng, NASDAQ, S&P 500, Dow Jones and more; supports price, change, volume, historical trend. Triggers: "上证指数", "沪深300", "创业板指", "恒生指数", "纳斯达克", "标普500", "道琼斯", "指数行情", "指数点位", "上證指數", "滬深300", "創業板指", "恒生指數", "納斯達克", "標普500", "道瓊斯", "指數行情", "Shanghai Composite", "CSI 300", "Hang Seng Index", "NASDAQ", "S&P 500", "Dow Jones", "index quote", "market index".
Server-side quantitative indicator runner via Longbridge Securities — execute Pine Script v6 syntax subset against historical K-line data on Longbridge servers without a local Python environment. Supports built-in indicators (MACD, RSI, Bollinger Bands, EMA, SMA, etc.) and custom calculation logic; results returned as JSON. Triggers: "量化指标", "Pine Script", "指标计算", "MACD计算", "RSI计算", "服务端指标", "指标脚本", "量化脚本", "技术指标运行", "量化指標", "指標計算", "MACD計算", "RSI計算", "服務端指標", "指標腳本", "quant indicator", "Pine Script", "indicator calculation", "run indicator", "server-side quant", "MACD script", "RSI calculation", "technical indicator runner", "quant run".
Main business composition and operating data — revenue breakdown by segment, gross margin by business line, and operating metrics (ROE / ROA / ROIC / working capital turnover). Shareholder / customer / supplier data is not available via Longbridge; pair with longbridge-news to extract segment detail from filings. Triggers: "主营业务", "业务构成", "分部营收", "业务拆分", "经营数据", "业务占比", "收入结构", "主营收入", "主營業務", "業務構成", "分部營收", "業務拆分", "經營數據", "業務佔比", "business breakdown", "revenue breakdown", "segment revenue", "business composition", "operating data", "revenue structure", "main business", "segment breakdown", "gross margin by segment".
Design and operate data quality programs for financial data — golden source architecture, validation rules, data lineage, exception management, profiling, and governance. Use when building validation rules for pricing or client data pipelines, designing a data quality monitoring framework, establishing golden source designations across systems, implementing data lineage for BCBS 239 or MiFID II, investigating reconciliation breaks or billing errors traced to bad data, preparing for regulatory exams on data accuracy, building data quality scorecards, or defining data stewardship roles. Trigger on: data quality, golden source, data lineage, data validation, data profiling, exception management, data governance, BCBS 239, data completeness, data accuracy, validation rules, data anomaly, data stewardship, data quality scorecard.
API de datos financieros de EE.UU.: acciones, forex, crypto, indicadores técnicos, fundamental data.
Quantitatively verify the long-term transmission/linkage relationship between Platinum and the Brazilian stock market (EWZ) using public market data, and output dual-axis charts, lead-lag analysis, correlation strength scores, and monitoring signals.
Analyze business segment performance and reporting from SEC filings using Octagon MCP. Use when researching segment revenue, operating income, margins, geographic breakdown, and segment restructuring from 10-K and 10-Q filings.
Track finance investment signal evolution and update logic based on new finance market information. Use when monitoring finance signals and determining if they are strengthened, weakened, or falsified.
Best practices for Open Finance data retrieval and management. Use when working with accounts, transactions, investments, loans, or identity data.
This skill should be used when the user wants to integrate Plaid API for bank account connections and transaction syncing. Use when implementing financial data access, bank linking, or transaction imports in TypeScript/Bun applications.
Chart any technical indicator on a symbol using Plotly. Creates interactive dark-themed charts with candlestick, overlays, and subplots. Supports all 100+ openalgo.ta indicators.
This Skill is built based on Eastmoney's authoritative database and the latest underlying market data, supporting natural language queries for market data (real-time quotes, main capital flows, valuations, etc. of stocks, industries, sectors, indices, funds, bonds), financial data (basic information of listed companies, financial indicators, executive information, main business, etc.), and relationship and operation data (associated relationships, enterprise operation data). It prevents models from answering financial data questions based on outdated knowledge and provides authoritative and timely financial data.