Data

This skill provides a structured five-phase workflow for financial statement analysis, enforcing data verification and audit documentation. It dispatches tasks to a specialized financial-analyst agent, ensuring calculations are traceable and conclusions are supported by evidence.

This skill wraps the songsee CLI tool to generate spectrograms and multi-panel audio visualizations from audio files. It supports various visualization types, time slicing, and output formats. The tool handles WAV/MP3 natively and uses ffmpeg for other formats when available.

Provides concrete patterns for extracting data from GA4 and Google Search Console APIs, including parallel execution, caching, rate limiting, and error handling. Focuses on practical implementation details rather than abstract concepts.

This skill provides navigation to USGS gauge integration workflows for HEC-RAS hydraulic modeling. It points to existing documentation and code examples rather than duplicating content, covering spatial discovery, data retrieval, boundary condition generation, and model validation with metrics like NSE and KGE.

This skill provides comprehensive guidance for using Polars, a high-performance DataFrame library. It covers core concepts like lazy evaluation, common operations, performance optimization, and file handling. The documentation includes practical code examples, best practices for different scenarios, and debugging techniques for data workflows.

This skill provides tools for retrieving AORC historical precipitation data and generating Atlas 14 design storms for HEC-RAS/HMS models. It handles spatial averaging over watersheds, temporal aggregation, DSS export, and SCS temporal distributions. The documentation clearly directs users to authoritative sources with complete workflows.

This skill provides Python code templates for exporting pandas DataFrames and bioinformatics data to formatted Excel files using openpyxl. It includes examples for AnnData objects, DEG results, multi-sheet workbooks, and conditional formatting. The documentation covers installation, troubleshooting, and performance considerations for different dataset sizes.

This skill provides Python tools to parse and modify HEC-RAS geometry files (.g##) using fixed-width FORTRAN format. It handles cross sections, storage areas, bridges, culverts, lateral structures, and Manning's n tables. The documentation includes clear examples for common workflows like reading station-elevation data, updating roughness values, and extracting structure information.

This Skill provides comprehensive documentation for using GeoPandas, a Python library for geospatial data analysis. It covers installation, core concepts, common operations, and visualization with practical code examples. The documentation includes detailed sections on data structures, I/O operations, CRS management, spatial joins, and map creation.

A well-structured documentation skill that effectively guides users to comprehensive HEC-RAS result extraction resources, providing clear navigation to authoritative sources and practical workflows.

Specializes in reading HEC-DSS files (V6/V7) to extract boundary condition data for hydraulic modeling. Automates catalog reading and time series extraction, converting DSS data to pandas DataFrames with metadata. Requires Java JVM and pyjnius, uses lazy loading to minimize overhead until first DSS operation.