What types of raw data formats can I input into this skill?

This skill supports structured CSV and JSON files, as well as raw text tables or Markdown. It is designed to handle messy data inputs by automatically parsing and structuring them before the visualization phase.

How does this perform better than just asking an AI to write a plotting script?

Unlike direct prompting which often produces static or buggy images, this skill uses a deterministic Python pipeline to generate interactive HTML files. This ensures you can hover, zoom, and export data directly from the chart without code hallucinations.

What exactly is included in the purchase?

You will receive the 'data-to-chart' Python wrapper, the integration scripts for your AI agent, and a setup guide. The package includes everything needed to bridge your agent's output with the Plotly rendering engine.

Are there any specific system requirements or dependencies?

The skill requires Python 3.10 or higher and the Plotly library installed on your local environment or server. The 'data-to-chart' wrapper must also be added to your system PATH so the agent can execute the rendering command.

Does the skill decide which chart type to use, or do I have to specify it?

Yes, the skill includes an auto-selection logic that analyzes your data distribution. It will automatically determine if a bar chart, line graph, or scatter plot is most appropriate for the trends found in your specific dataset.

Data to Chart: Interactive Visualizer 📊

Example session with this skill installed

I have a CSV file at ./sales_data.csv with columns Month, Revenue, and Expenses. Can you create a chart showing the trend?

Read your context and instructions
Compiled the data to chart
Generated the data export

I have parsed the sales data and generated an interactive trend chart. The data was processed into a JSON specification and rendered into a self-contained HTML file.

Sales Performance Visualization

Metric	Details
Input File	`./sales_data.csv`
Chart Type	Multi-series Line Chart
Output File	`sales_trend_report.html`
Interactive Features	Hover tooltips, zoom, pan, and legend toggling

{
  "chart_type": "line",
  "title": "Monthly Revenue vs Expenses",
  "x_axis": {
    "label": "Month",
    "data": ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
  },
  "y_axis": {
    "label": "USD",
    "series": [
      {
        "name": "Revenue",
        "values": [45000, 48000, 52000, 49000, 55000, 60000]
      },
      {
        "name": "Expenses",
        "values": [30000, 31000, 35000, 32000, 33000, 34000]
      }
    ]
  }
}

Next steps

Open sales_trend_report.html in any web browser to view the interactive chart.
Run data-to-chart --update if you add July data to the CSV.
Use the export command if you need a static PNG version for a PDF report.

sales_data.csv

CSV · data export

Generated

Example file from a real run - the skill writes it into your workspace.

Connects securely to your tools. The creator never sees your data.

About this skill

Manual data visualization is slow and prone to formatting errors. This skill automates the pipeline from raw data to interactive HTML charts, ensuring consistent output without writing custom plotting logic every time.

What it does

Data parsing extracts structured information from CSV, JSON, or pasted text tables.
Auto-selection determines the optimal chart type based on data distribution and types.
JSON specification generates a structured intermediate spec for deterministic rendering.
HTML generation produces self-contained, interactive files for browser viewing.

How it works

Provide raw data or a file path to the agent.
The agent validates the data and outputs a JSON chart specification.
The data-to-chart wrapper processes the spec to generate the visualization.
Access the resulting HTML file in your browser for interaction.

Frameworks & tools

This tool requires Python 3.10 or higher and uses the Plotly library for rendering. It integrates via a Bash wrapper and a dedicated Python script.

Why this beats prompting it yourself

Standard prompting often results in buggy Matplotlib code or hallucinated data points. This skill uses a strict two-stage pipeline that separates data processing from rendering, ensuring the final chart is valid and interactive.

Use cases

Convert CSV sales exports into interactive line graphs for reports.
Visualize JSON API responses to identify trends or outliers.
Turn messy markdown tables into clean, shareable HTML bar charts.

Known limitations

Requires Python 3.10+ and the data-to-chart wrapper to be installed on the system PATH. Only generates HTML-based interactive charts.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Data to Chart: Interactive Visualizer 📊