Where are the generated charts stored?

Charts are typically saved as Vega-Lite JSON specifications in the /artifacts directory, making them easy to render in compatible frontends.

How does the validation process work?

The skill uses a 'validation-first' approach where it checks your data structure and chart specs, providing specific suggestions if fields are missing or formats are incorrect.

Can I use this skill with dbt data?

Yes, it is designed to work seamlessly with dbt, allowing you to run queries and immediately visualize the output using the dbt_command tool.

Does it handle large datasets automatically?

While it can process data, the skill recommends using sampling or aggregation for very large datasets to ensure optimal performance and visual clarity.

Which chart types are supported by this skill?

The skill supports a wide range of standard visualizations including bar charts, line graphs, scatter plots, pie charts, and histograms.

Data Visualization Expert

Name: Data Visualization Expert
Author: nerver111

bynerver111

0•

Ciencia de Datos y ML

Transforms raw data into validated Vega-Lite charts with intelligent type recommendation and automated configuration.

This skill empowers Claude to act as a data visualization specialist, capable of reading data from various sources like JSON files or dbt queries and converting them into professional visual assets. It automatically recommends the most effective chart types based on your data's structure, generates precise Vega-Lite specifications, and includes a robust validation loop that provides actionable correction suggestions to ensure accurate and insightful visual outputs every time.

Características Principales

010 GitHub stars

02Support for bar, line, scatter, pie, and histogram chart types

03Real-time validation of Vega-Lite specifications with intelligent error correction

04Smart handling of large datasets through recommended sampling and aggregation

05Seamless integration with dbt query results and local JSON files

06Automated chart type recommendation based on data characteristics

Casos de Uso

01Creating data-driven reports by converting JSON datasets into visual artifacts

02Generating trend charts from application log files or performance metrics

03Visualizing SQL query results from a dbt warehouse for rapid data insights

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nerver111/git-skill-test run-text-to-chart

For use in Claude.ai and ChatGPT

Características Principales

010 GitHub stars

02Support for bar, line, scatter, pie, and histogram chart types

03Real-time validation of Vega-Lite specifications with intelligent error correction

04Smart handling of large datasets through recommended sampling and aggregation

05Seamless integration with dbt query results and local JSON files

06Automated chart type recommendation based on data characteristics

Casos de Uso

01Creating data-driven reports by converting JSON datasets into visual artifacts

02Generating trend charts from application log files or performance metrics

03Visualizing SQL query results from a dbt warehouse for rapid data insights

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nerver111/git-skill-test run-text-to-chart

For use in Claude.ai and ChatGPT