Total 54,454 skills, Data Processing has 2788 skills
Showing 12 of 2788 skills
Expert-level algorithmic trading, market systems, quantitative analysis, and trading platforms
Entry signal patterns with historical success rates. Use when deciding whether to open a position.
Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
sqlite-vec extension for vector similarity search in SQLite. Use when storing embeddings, performing KNN queries, or building semantic search features. Triggers on sqlite-vec, vec0, MATCH, vec_distance, partition key, float[N], int8[N], bit[N], serialize_float32, serialize_int8, vec_f32, vec_int8, vec_bit, vec_normalize, vec_quantize_binary, distance_metric, metadata columns, auxiliary columns.
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.
Working effectively with JSON data structures.
Data validation patterns including schema validation, input sanitization, output encoding, and type coercion. Use when implementing validate, validation, schema, form validation, API validation, JSON Schema, Zod, Pydantic, Joi, Yup, sanitize, sanitization, XSS prevention, injection prevention, escape, encode, whitelist, constraint checking, invariant validation, data pipeline validation, ML feature validation, or custom validators.
Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
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.
Use when asked to calculate statistical power, determine sample size, or plan experiments for hypothesis testing.
Fetches web pages, parses HTML with CSS selectors, calls REST APIs, and scrapes dynamic content. Use when extracting data from websites, querying JSON APIs, or automating browser interactions.
Screen and identify undervalued stocks with strong fundamentals using professional equity research methodology. Use when the user asks to find undervalued stocks, screen for cheap or bargain stocks, identify value investing opportunities, perform fundamental stock analysis, find stocks trading below intrinsic value, or requests a stock screener based on financial metrics like P/E ratio, debt-to-equity, free cash flow, or ROIC.