Create standardized charts from analytics query results with CSV and HTML or PNG exports.
npx skills add https://github.com/cline/skills --skill plotting-cline
Please help me install this Agent Skill. Skill: plotting Source: https://github.com/cline/skills/tree/main/skills/data-analyst/skills/plotting Command: npx skills add https://github.com/cline/skills --skill plotting-cline
This Skill eliminates the inconsistency and poor interpretability of ad-hoc data visualizations created from analytics query results, ensuring all charts are clear, consistent, and appropriate for either exploratory analysis or formal report use.
Use the plotting skill to create a multi-line time-series chart of daily event counts by top 5 categories from the last 30 days of analytics data, saved as an interactive HTML file with an accompanying CSV of the underlying plotted data.
High-intent search queries and answers about installing and using this skill.
To convert CSV to chart, you provide bounded analytics query results to apply standardized visualization rules. This process ensures proper axis labeling and factual titling, outputting clean local artifacts like interactive HTML files or static PNG exports alongside underlying data CSVs.
Visualizing time-series trends for exploratory analysis is best achieved by generating interactive, dependency-free HTML/CSS/JS/SVG charts. This approach allows dynamic data review while enforcing accessible standards and avoiding misleading patterns like spaghetti plots or inappropriate dual axes.
You can use matplotlib or seaborn for static report generation to create print-ready exports. These frameworks are supported when specialized features are needed, ensuring visualizations remain brand-agnostic and consistently labeled for formal stakeholder reporting.
Ad-hoc charting supports category comparison and distribution reviews by applying standardized quality guardrails to bounded query results. This prevents common errors like misleading visual patterns, ensuring your charts remain clear, factual, and appropriate for both exploratory and production-ready use cases.
Data visualization outputs lack proper axis labeling and factual titling due to the absence of enforced charting standards. Applying standardized quality guardrails during generation enforces neutral styling, correct axis labeling, and factual titles, while outputting local interactive HTML and static PNG files.
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