About this skill

Problem

Tabular data often remains in exported files: operations may have a CSV but still draws trend lines by hand, product may see JSON metrics but cannot express share and trend clearly, and analysts often need a quick readable chart summary during exploration. @user_a10ab3ad/data-visual-report targets the gap between raw tabular data and a usable report by turning Excel, CSV, or JSON input into structured visualization notes.

How It Works

The skill first parses table fields, then selects chart types by data shape: time-series fields become line or area charts, categorical fields become share charts such as pie or donut charts, and numeric fields support TopN rankings as bar or column charts. It then adds three to five written conclusions from metrics such as average, peak, and growth, and can insert [needs verification: field_name] placeholders when values are missing or types are unclear.

Boundaries

It does not read tables from images or PDFs, and it does not perform cleaning such as deduplication or imputation. The output is oriented toward static chart descriptions and summary conclusions, not interactive dashboards or predictive models. It fits weekly reports, monthly reports, and early product-data exploration, but fields and anomalies still need manual review before formal use.

Screenshots

Use Cases

Operations turn an activity CSV into trend line notes and TopN bar chart notes.
Product converts usage JSON into share donut notes and three conclusions.
Analysts draft an Excel metric report with chart notes and missing-field flags.

Best For

Operations reporting owners who want trend, TopN, and conclusion summaries from campaign tables.
Product owners doing feature reviews who need share-chart notes from usage JSON.
Analysts doing early data exploration who want quick static chart notes and verification flags.