mcpField
C
chart

chart

github.com/starchild-ai-agentData & databases303.9k installsUpdated 6d ago
Repository

About chart

A project-based charting skill for creating interactive Apache ECharts pages and reproducible chart assets. It serves analysts and builders who need reusable chart templates, saved data and scripts, and PNG exports.

Key features

  1. 01Build charts from reusable templates
  2. 02Create custom chart layouts
  3. 03Store each chart in its own project folder
  4. 04Save source data and generation scripts
  5. 05Export charts as PNG images

Use cases

  1. 01Visualize time-series trends and price data
  2. 02Compare categories or show composition
  3. 03Build multi-chart KPI dashboards
  4. 04Save a reproducible chart page and image

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/starchild-ai-agent/official-skills --skill chart

After installing, type/chartin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

scripts/
base-export.js
base-styles.css
build_chart.py
chart_server.py
templates/
bar.html
candlestick.html
dashboard.html
dual-axis.html
heatmap.html
line.html
multi-panel.html
pie.html
radar.html
scatter.html
waterfall.html

Details

From the project README

Chart — Project-Based Interactive Charting

Generate interactive chart pages with Apache ECharts. Each chart lives in a dedicated project folder under output/chart-html/, making it easy to reuse and iterate.

When to Use

Any time the user wants a visual chart: price charts, comparisons, dashboards, business analytics, etc.

Architecture

  • ECharts (CDN) for rendering
  • ECharts native export (getDataURL) + canvas merge for reliable PNG output
  • Project-based storage: one folder per chart project
  • No gallery mode: all artifacts stay in the project folder

Project Structure (Required)

Each chart project should follow:

output/chart-html/
  <project-name>/
    index.html        # chart page
    generate.py       # generation script (for reproducibility)
    README.md         # title / description / data source notes
    data.json         # data snapshot
    screenshot.png    # saved image

Example folder name: btc-90d-20260401

Workflow

Step 1: Pick template or custom layout

Available templates:

TemplateBest for
line.htmlTime-series trends, multi-series comparisons
bar.htmlCategory comparisons, rankings
pie.htmlComposition / share breakdown
candlestick.htmlOHLCV price charts
scatter.htmlCorrelation, distribution
dashboard.htmlKPI cards + 2×2 multi-chart grid
radar.htmlMulti-dimension scoring
heatmap.htmlMatrix / calendar intensity
dual-axis.htmlTwo series with very different scales (e.g. market cap vs stablecoin supply) — left and right Y axes, each with its own label color
multi-panel.htmlStacked panels sharing one X axis (e.g. price + volume + RSI) — single ECharts instance, tooltip/zoom synced across all panels
waterfall.htmlIncremental contribution breakdown (e.g. P&L attribution, budget variance) — positive/negative bars stacked on a floating base
Step 2: Create project folder

Use create_project(name, description, data_sources) from scripts/build_chart.py.

Step 3: Build and save chart page

Use either:

  • build_chart(template_name, ...)
  • build_chart_custom(...)

Then save as index.html in the project folder:

  • save_chart(html, project_dir=project_dir)
Step 4: Save reproducible assets

Also save:

  • save_generate_script(script_content, project_dir) → generate.py
  • save_data(data, project_dir) → data.json
  • project README is created by create_project(...)
Step 5: Serve preview

Use project-root serving (recommended):

preview_serve(
  title="Chart Preview",
  dir="skills/chart/scripts",
  command="python3 chart_server.py /data/workspace/output/chart-html 7860",
  port=7860
)

Then open: /preview/<id>/<project-name>/index.html

Important behavior in v3.0.1:

  • chart_server.py now rewrites preview-prefixed static paths internally (/preview/<id>/... → /...) before filesystem lookup.
  • This guarantees the preview iframe resolves the real project index.html instead of falling back to root directory listing.
  • Keep project pages under output/chart-html/<project>/index.html (do not serve output/chart-html directly as a static preview without chart_server.py).
Step 6: Export image

Two modes:

  1. User wants web page + image: click "💾 Save Image" in page toolbar, saves to current project as screenshot.png
  2. User wants image only: call screenshot_chart(project_dir) (Playwright) and send screenshot.png directly

Toolbar Requirements

Every chart page must include these buttons:

<div class="actions">
  <button onclick="downloadPNG(this)">📥 Download PNG</button>
  <button onclick="copyToClipboard(this)">📋 Copy Image</button>
  <button onclick="saveToProject(this)">💾 Save Image</button>
</div>

Do not include gallery entry.

Key Files

FilePurpose
skills/chart/scripts/base-styles.cssBase dark theme CSS
skills/chart/scripts/base-export.jsExport helpers: download/copy/save-to-project
skills/chart/scripts/build_chart.pyProject creation, HTML build, data/script save, screenshot
skills/chart/scripts/chart_server.pyStatic server + /save-chart API
skills/chart/templates/*.htmlReusable chart templates
output/chart-html/<project>/*All generated chart artifacts

Notes

  • Embed data directly in HTML (const DATA = ...) to avoid iframe CORS issues.
  • For multi-chart pages, register all chart instances in window.CHART_INSTANCES.
  • Use meaningful project names (topic-range-date) for easy lookup.

Comments

Markdown supported