Total 50,503 skills, Data Processing has 2560 skills
Showing 12 of 2560 skills
This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Expert in high-performance CSV processing, parsing, and data cleaning using Python, DuckDB, and command-line tools. Use when working with CSV files, cleaning data, transforming datasets, or processing large tabular data files.
Screen and filter A-share stocks based on fundamental metrics, technical indicators, capital flow, and custom criteria. Support multiple screening strategies including value investing, growth investing, momentum trading, and dividend hunting. Use this tool when users want to find stocks meeting specific criteria such as "low P/E and high ROE stocks", "stocks with increased northbound capital positions", "stocks breaking above the 200-day moving average".
Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
Data modeling with Entity-Relationship Diagrams (ERDs), data dictionaries, and conceptual/logical/physical models. Documents data structures, relationships, and attributes.
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology
Diagnose risks and inefficiencies in an existing investment portfolio. Use when the user asks to review, audit, or stress-test their current holdings, evaluate portfolio concentration, check factor exposures, assess correlation risks, identify hidden tilts, or get actionable improvement suggestions for a portfolio they already own.
Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques. Use when analyzing social media exports, user data, behavioral datasets, or any structured data where deeper insights are desired. Pairs with personality-profiler for enhanced signal extraction. Triggers on requests like "what patterns do you see", "find hidden signals", "correlate these datasets", "what am I missing in this data", "analyze across datasets", "find non-obvious insights", or when users want to go beyond surface-level analysis. Also use proactively when you notice interesting anomalies or correlations during any data analysis task.
Evaluate the probability and path of copper prices breaking through key levels or entering a 'back-and-fill' pullback to support levels using cross-asset signals (global stock market resilience + Chinese interest rate environment).
Expert-level data science, analytics, visualization, and statistical modeling