What specific types of data is this skill optimized for?

While it can handle various experimental datasets, it is specifically optimized for correlating typographic error rates with semantic distance in translation experiments.

Can I use the output of this skill in my GitHub documentation?

Yes, the skill outputs standard Markdown files, which are perfectly suited for GitHub READMEs, project logs, and documentation sites.

Where are the generated charts and reports saved?

All visualizations and comprehensive reports are automatically saved as markdown files in the 'results/' directory of your repository for easy access.

Does this skill require Python or Matplotlib to function?

No, this skill uses Claude's native text processing capabilities to generate ASCII art and markdown tables, requiring no external dependencies or libraries.

Chart Generator

Name: Chart Generator
Author: aviferdman

byaviferdman

데이터 과학 및 ML

Generates text-based visualizations and comprehensive reports to analyze the relationship between typographic errors and semantic drift in experimental data.

소개

The Chart Generator skill enables Claude to transform raw experimental data into structured markdown reports and ASCII-based visualizations without requiring external plotting libraries or Python code. Specifically designed for researchers and developers analyzing translation semantic drift, it provides clear visual evidence of how typo rates impact meaning through terminal-friendly line charts, bar graphs, and tabular data. This skill is ideal for documenting AI model robustness and experimental findings directly within your codebase and documentation directories.

주요 기능

ASCII-based line and bar charts for terminal-friendly data visualization
Comprehensive summary reports with automated trend analysis and interpretation
Structured Markdown tables correlating typo rates with semantic distance
Automated file management for experimental results and trend observations
0 GitHub stars
Zero-dependency output requiring no Python or external plotting libraries

사용 사례

Generating quick, terminal-ready progress reports for NLP experiments
Documenting semantic drift trends for research papers and project documentation
Visualizing the impact of spelling errors on machine translation accuracy

소개

주요 기능

ASCII-based line and bar charts for terminal-friendly data visualization
Comprehensive summary reports with automated trend analysis and interpretation
Structured Markdown tables correlating typo rates with semantic distance
Automated file management for experimental results and trend observations
0 GitHub stars
Zero-dependency output requiring no Python or external plotting libraries

사용 사례

Generating quick, terminal-ready progress reports for NLP experiments
Documenting semantic drift trends for research papers and project documentation
Visualizing the impact of spelling errors on machine translation accuracy