How does it handle data verification?

It avoids relying on existing summaries by inspecting raw data file headers and running Python analysis scripts to confirm signal quality and statistics.

What language are the reports generated in?

By default, this skill generates reports in Simplified Chinese, utilizing specialized translation references to ensure technical terminology is accurate.

Does this skill support LaTeX?

Yes, it uses LaTeX for mathematical formulas and isotopic notation to ensure scientific accuracy in all generated reports.

Can it run my existing analysis code?

Absolutely. The skill is designed to execute scripts using 'uv' to verify raw data and extract the latest performance metrics directly from your environment.

Does it create new files for the reports?

Yes, it typically consolidates all findings, methodologies, and quantitative results into a new Markdown file located at 'docs/analysis-report.md'.

Scientific Analysis & Reporting

Name: Scientific Analysis & Reporting
Author: ryanchen01

byryanchen01

Ciencia de Datos y ML

Generates structured, data-driven scientific research reports by analyzing repository data and verifying metrics against technical standards.

Acerca de

This skill transforms Claude into a specialized data analyst and technical writer capable of performing deep repository dives to create comprehensive research reports. It automates the process of mapping scientific contexts, verifying raw data through code execution, and cross-referencing findings with industry standards or reference materials. It is particularly useful for researchers and developers who need to document experimental results, performance benchmarks, or comparative studies with high precision, professional formatting (including LaTeX), and standardized structures in Simplified Chinese.

Características Principales

0 GitHub stars
Standardized multi-section report generation following academic and professional guidelines
Automated repository mapping to identify data sources, physical models, and reference standards
Native support for Simplified Chinese documentation with precise technical terminology
Dynamic data verification through Python script execution and real-time metric extraction
Mathematical and isotopic notation rendering using LaTeX for scientific accuracy

Casos de Uso

Comparing performance metrics between different software algorithms or experimental methods
Analyzing sensor data stability and cross-referencing results with manufacturer datasheets
Generating formal documentation for scientific experiments stored in GitHub repositories

Acerca de

Características Principales

0 GitHub stars
Standardized multi-section report generation following academic and professional guidelines
Automated repository mapping to identify data sources, physical models, and reference standards
Native support for Simplified Chinese documentation with precise technical terminology
Dynamic data verification through Python script execution and real-time metric extraction
Mathematical and isotopic notation rendering using LaTeX for scientific accuracy

Casos de Uso

Comparing performance metrics between different software algorithms or experimental methods
Analyzing sensor data stability and cross-referencing results with manufacturer datasheets
Generating formal documentation for scientific experiments stored in GitHub repositories