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// Skill profile

ECharts Chart Skill

name: echarts-chart-skill

by davaded · published 2026-03-22

开发工具数据处理
Total installs
0
Stars
★ 0
Last updated
2026-03
// Install command
$ claw add gh:davaded/davaded-echarts-ai-skill
View on GitHub
// Full documentation

---

name: echarts-chart-skill

description: Generate charts from natural language or tabular data, recommend chart types, and export ECharts-based HTML or SVG. Use when users ask for one-sentence chart generation, auto chart selection from data, or embeddable chart previews.

metadata:

short-description: Skill-first ECharts toolkit for agent chart workflows

openclaw:

slug: echarts-ai-skill

version: 0.1.4

license: MIT

homepage: https://github.com/davaded/Echarts-AI-Skill

repository: https://github.com/davaded/Echarts-AI-Skill

---

# ECharts Chart Skill

Use this skill when the user wants chart output from a short description or from table-like data.

Workflow

1. Translate the user's request into a `ChartRequest` JSON object.

2. If the chart type is unclear, run the recommendation command first.

3. Run the generation command to produce a stable ECharts option.

4. Run the render command when the user wants an embeddable `html` or `svg`.

Files

  • Core types: `src/types/chart.ts`
  • Chart recommendation: `src/core/recommend.ts`
  • Option generation: `src/core/spec-to-option.ts`
  • Rendering: `src/core/render.ts`
  • Sample input: `examples/study-progress.request.json`
  • Universal metadata: `manifest.yaml`, `agents/openai.yaml`
  • Setup

    npm install

    Output rules

  • `--out` writes to an exact file path.
  • `--out-dir` writes the default file into a directory you choose.
  • `desktop` and `home` are valid aliases for `--out-dir` when the user explicitly asks for those locations.
  • `~` is expanded to the current user's home directory.
  • If no output path is provided, files default to the current working directory.
  • Commands

    node dist/cli/recommend-chart.js --input examples\study-progress.request.json
    node dist/cli/generate-chart.js --input examples\study-progress.request.json
    node dist/cli/render-chart.js --input option.json --format html
    node dist/cli/render-chart.js --input option.json --format svg --out D:\reports\study-chart.svg

    Default output filenames:

  • Recommendation: `spec.json`
  • Option: `option.json`
  • HTML preview: `preview.html`
  • SVG preview: `preview.svg`
  • `ChartRequest` shape

    {
      "title": "Monthly study completion",
      "dataset": [
        { "day": "2026-03-01", "completionRate": 62, "targetRate": 75 },
        { "day": "2026-03-02", "completionRate": 68, "targetRate": 75 }
      ],
      "goal": "trend",
      "xField": "day",
      "yField": "completionRate",
      "series": [
        { "name": "Completion", "field": "completionRate" },
        { "name": "Target", "field": "targetRate" }
      ]
    }

    Guidance

  • Prefer deterministic field mapping over free-form inference when the user has already named fields.
  • For pie charts, keep one category field and one metric field.
  • For scatter charts, require numeric `xField` and `yField`.
  • If the user only gave natural language, construct the smallest valid `ChartRequest` before calling scripts.
  • If the user needs a report artifact, render `html` first and `svg` second.
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