Total 30,538 skills, Data Processing has 1462 skills
Showing 12 of 1462 skills
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
Retrieve Chinese financial market data (A-shares, Hong Kong stocks, US stocks, funds, futures, bonds). Supports over 220 Tushare Pro interfaces: stock quotes, financial statements, macroeconomic indicators. Use this when users request stock price data, financial analysis, index quotes, macro data such as GDP/CPI, etc.
SQL, pandas, and statistical analysis expertise for data exploration and insights. Use when: analyzing data, writing SQL queries, using pandas, performing statistical analysis, or when user mentions data analysis, SQL, pandas, statistics, or needs help exploring datasets.
Web scraping with anti-bot bypass, content extraction, undocumented APIs and poison pill detection. Use when extracting content from websites, handling paywalls, implementing scraping cascades or processing social media. Covers requests, trafilatura, Playwright with stealth mode, yt-dlp and instaloader patterns.
Fetch hot finance news, unified trends, and prediction financial market data. Use when the user needs real-time financial news, trend reports from multiple finance sources (Weibo, Zhihu, WallstreetCN, etc.), or Polymarket finance market prediction data.
A-share financial data toolkit. Provides scripts to obtain real-time A-share market quotes, financial indicators, share increases/reductions by directors, supervisors and senior executives, northbound capital flows, and macroeconomic data (LPR, CPI/PPI, PMI, social financing, M2). Used when real-time A-share market data is required to support investment analysis. All data sources are free and no API key is required.
Use akshare to obtain real-time and historical data of China's financial market. Use this skill when you need to query real-time quotes, historical data, and financial statements of financial products such as A-shares, Hong Kong stocks, US stocks, indices, funds, futures, etc.
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
This skill should be used when the user asks to "create financial projections", "build a financial model", "forecast revenue", "calculate burn rate", "estimate runway", "model cash flow", or requests 3-5 year financial planning for a startup.
Pragmatic qualitative analysis for interview data in sociology research. Guides you through systematic coding, interpretation, and synthesis with quality checkpoints. Supports theory-informed (Track A) or data-first (Track B) approaches.
R statistical analysis for publication-ready sociology research. Guides you through phased workflows for DiD, IV, matching, panel methods, and more. Use when doing quantitative analysis in R for academic papers.
Abductive analysis for qualitative interview data following Timmermans & Tavory. Guides you through theory-first analysis that recognizes anomalies and generates novel theoretical insights through systematic puzzle exploration.