Analyze tabular workspace data with DuckDB queries and optional Plotly charts.
npx skills add https://github.com/plc1220/HelpUDoc --skill data-analysis-plc1220
Please help me install this Agent Skill. Skill: data-analysis Source: https://github.com/plc1220/HelpUDoc/tree/main/skills/data-analysis Command: npx skills add https://github.com/plc1220/HelpUDoc --skill data-analysis-plc1220
End-to-end data analysis is often manual and fragmented. This Skill enables analysts to run DuckDB queries over workspace datasets, generate optional Plotly visualizations, and produce artifact-aware summaries.
Run an end-to-end analysis over your workspace data using DuckDB queries and optionally generate a Plotly chart to accompany a concise artifact-backed summary.
High-intent search queries and answers about installing and using this skill.
Run end-to-end data analysis by executing DuckDB queries over workspace tabular datasets to explore segmentation, drivers, and outliers, then generate concise summaries with optional Plotly visualizations.
Yes, you can generate optional Plotly charts from DuckDB query results to visually accompany your analysis and deliver artifact-backed summaries with concrete evidence.
Artifact-aware data summarization uses DuckDB query results to produce concise summaries backed by verifiable datasets and charts, ensuring all findings cite concrete evidence from the analysis.
Yes, the structured analysis workflow includes dataset snapshots, segmentation, driver analysis, interaction analysis, and outlier detection, all executed via DuckDB queries with explicit chart generation rules.
The best way is using DuckDB queries to analyze workspace tabular data, generating optional Plotly charts and delivering artifact-aware summaries that cite concrete evidence directly from the query results.
No explicit pre-processing dependencies are required. The workflow operates directly on tabular data in the workspace using DuckDB queries to perform structured analysis including snapshots, segmentation, and outlier detection.
takusaotomeProfile CSV, Parquet, and JSON datasets with DuckDB SQL.
hanlinlibhamCreate and visualize 2D/3D charts and maps with Plotly in Python.
silvainfmRun SQL analytics directly on CSV, Parquet, and JSON files and Pandas or Polars DataFrames.
youyinnnGenerate statistical visualizations from pandas DataFrames using the seaborn Python library.
