Total 50,476 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Retrieve historical financial ratings and key metric scores over time using Octagon MCP. Use when analyzing overall ratings, return on assets, return on equity, discounted cash flow scores, debt-to-equity scores, and letter grades (A+, A, B, etc.) for any public company.
Best practices for SciPy scientific computing, optimization, signal processing, and statistical analysis in Python
Use when analyzing FileMaker DDR to extract calculations, custom functions, and business logic for PostgreSQL import processes or maintenance scripts - focuses on understanding and adapting FileMaker logic rather than direct schema migration
Comprehensive guide for NumPy - the fundamental package for scientific computing in Python. Use for array operations, linear algebra, random number generation, Fourier transforms, mathematical functions, and high-performance numerical computing. Foundation for SciPy, pandas, scikit-learn, and all scientific Python.
An analytical in-process SQL database management system. Designed for fast analytical queries (OLAP). Highly interoperable with Python's data ecosystem (Pandas, NumPy, Arrow, Polars). Supports querying files (CSV, Parquet, JSON) directly without an ingestion step. Use for complex SQL queries on Pandas/Polars data, querying large Parquet/CSV files directly, joining data from different sources, analytical pipelines, local datasets too big for Excel, intermediate data storage and feature engineering for ML.
Merge multiple CSV/Excel files with intelligent column matching, data deduplication, and conflict resolution. Handles different schemas, formats, and combines data sources. Use when users need to merge spreadsheets, combine data exports, or consolidate multiple files into one.
Retrieve ratings snapshot with overall rating and key metric scores including DCF, ROE, ROA, Debt-to-Equity, P/E, and P/B for public companies. Use when screening stocks, comparing quality metrics, or quick fundamental assessment.
Compare key metrics and disclosures between annual 10-K filings using Octagon MCP. Use when analyzing year-over-year changes in financials, risk factors, business descriptions, and strategic priorities across fiscal years.
Numerical algorithms and computational techniques for statistics
A Python package useful for chemistry (mainly physical/analytical/inorganic chemistry). Features include balancing chemical reactions, chemical kinetics (ODE integration), chemical equilibria, ionic strength calculations, and unit handling. Use when working with chemical equations, reaction balancing, kinetic modeling, equilibrium calculations, speciation, pH calculations, ionic strength, activity coefficients, or chemical formula parsing.
Library for bioinformatics and community ecology statistics. Provides data structures and algorithms for sequences, alignments, phylogenetics, and diversity analysis. Essential for microbiome research and ecological data science. Use for alpha/beta diversity metrics, ordination (PCoA), phylogenetic trees, sequence manipulation (DNA/RNA/Protein), distance matrices, PERMANOVA, and community ecology analysis.
Build trading systems in the style of Renaissance Technologies, the most successful quantitative hedge fund in history. Emphasizes statistical arbitrage, signal processing, and rigorous scientific methodology. Use when developing alpha research, signal extraction, or systematic trading strategies.