Create Cell Renderer

SkillDev tools

Create a custom grid cell renderer for Datagrok

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Create Cell Renderer skill

What this skill tells your AI

The instructions your AI receives, as published by datagrok-ai/public in .claude/skills/create-cell-renderer/SKILL.md and read by ahel’s review.

Create a custom cell renderer for the Datagrok grid/table viewer.

Usage

/create-cell-renderer [cell-type] [package-path]

Instructions

When this skill is invoked, help the user create a custom cell renderer that extends DG.GridCellRenderer.

Step 1: Create the renderer class

Create a new TypeScript file in the package's src/ directory (e.g., src/renderers/my-renderer.ts).

The class must extend DG.GridCellRenderer and implement:

  • get name() - unique renderer name
  • get cellType() - the cell type string (e.g., 'piechart', 'barchart')
  • render(g, x, y, w, h, gridCell, cellStyle) - main drawing method using CanvasRenderingContext2D
  • renderSettings(gridColumn) (optional) - returns an HTMLElement for renderer settings UI
export class MyRenderer extends DG.GridCellRenderer {
  get name() { return 'My Renderer'; }
  get cellType() { return 'mytype'; }

  render(g: CanvasRenderingContext2D, x: number, y: number, w: number, h: number,
    gridCell: DG.GridCell, cellStyle: DG.GridCellStyle): void {
    // Draw using Canvas 2D API within the bounds (x, y, w, h)
  }

  renderSettings(gridColumn: DG.GridColumn): HTMLElement | null {
    // Optional: return UI element for settings
    return null;
  }
}

Step 2: Register the renderer

Use the decorator approach (preferred for datagrok-tools >= 4.12.x):

@grok.decorators.cellRenderer({
  cellType: 'mytype',
  virtual: true,
})
export class MyRenderer extends DG.GridCellRenderer {
  /* ... */
}

The virtual: true flag is used for summary/calculated columns.

Or use the function annotation approach in package.ts:

//name: myRenderer
//tags: cellRenderer
//meta.cellType: mytype
//meta.virtual: true
//output: grid_cell_renderer result
export function myRendererFunc() {
  return new MyRenderer();
}

Step 3: Build and publish

The decorator approach auto-generates a package.g.ts file via FuncGeneratorPlugin during build. This file must be committed.

npm run build
grok publish

Key points

  • The cellType string links the renderer to summary columns of that type
  • The render method receives a Canvas 2D context; draw within the bounding box (x, y, w, h)
  • For real examples, see the PowerGrid package: public/packages/PowerGrid/src/sparklines/
  • Both decorator and function annotation approaches are equivalent; prefer decorators

Behavior

  1. Ask the user what type of cell visualization they want if not specified
  2. Create the renderer class file with the appropriate drawing logic
  3. Register it using the decorator approach (or function annotation if the user prefers)
  4. Ensure the package imports are correct (import * as DG from 'datagrok-api/dg', import * as grok from 'datagrok-api/grok')
  5. Remind the user to build and publish the package

Signals

GitHub stars
72
Forks
32
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
create-cell-renderer
Source
github.com/datagrok-ai/public