Skip to content
cccskills
FreePublished on GitHub

add-package-tests

Add unit tests to a Datagrok package

The English summary is being prepared. Showing the original description.

See how to install

Files included (1)

  • SKILL.md3.3 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Add Package Tests

Add unit tests to a Datagrok package using the platform's custom test framework.

Usage

/add-package-tests [package-path]

Instructions

When this skill is invoked, help the user set up and write tests for a Datagrok package.

Step 1: Set up test infrastructure

For a new package with tests:

grok create <package-name> --test

For an existing package:

cd <package-name>
grok add tests
npm install

Step 2: Create test files

Create a src/tests/ directory. Each file covers one test category.

import {category, expect, test} from '@datagrok-libraries/utils/src/test';


category('MyCategory', () => {
  test('should do something', async () => {
    expect(actualValue, expectedValue);
  });

  test('should handle edge case', async () => {
    // Test logic
    expect(result, true);
  });
});

Key test framework functions:

  • category(name, fn) - groups related tests
  • test(name, fn, options?) - defines a single test case
  • expect(actual, expected) - assertion that checks equality

Step 3: Register tests in package-test.ts

Import all test files in src/package-test.ts:

import './tests/my-category-tests';
import './tests/another-category-tests';

Step 4: Skip failing tests

Use the skipReason parameter to temporarily skip a test (always provide a reason such as a GitHub issue):

test('flaky test', async () => {
  expect(1, 11);
}, {skipReason: 'public#4256'});

Step 5: Add inline function tests

For simple functions, add test cases directly in the function annotation using the test parameter:

//name: square
//input: int x
//output: int y
//test: square(1) == 1
//test: square(2) == 4
//test: square(3) == 9
export function square(x: number): number {
  return x ** 2;
}

Supported parameter types for //test: annotations: int, double, bool, string, datetime, map. Types like dataframe, column_list, column, file, blob require an additional helper function call.

Step 6: Run tests

# Local testing with datagrok-tools
grok test

# Against a specific server
grok test --host dev

# With visible browser
grok test --gui

# Specific category or test
grok test --category "MyCategory"
grok test --test "should do something"

Tests can also be run from the Datagrok console or Test Manager in the platform UI.

Key points

  • Import category, test, expect from @datagrok-libraries/utils/src/test
  • Always register test files in src/package-test.ts
  • Use skipReason with a GitHub issue (e.g., public#4256) when skipping tests
  • The //test: annotation on functions is for simple input/output validation
  • For real examples, see the Chem package: public/packages/Chem/

Behavior

  1. Check if the package already has test infrastructure (src/package-test.ts, src/tests/)
  2. If not, set up test support using grok add tests
  3. Create test files with category and test blocks for the functionality the user wants to test
  4. Register the test files in src/package-test.ts
  5. Show the user how to run the tests

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Add support for a new curve data format in the Curves package converter system

The English summary is being prepared. Showing the original description.

datagrok-ai/public74Updated Oct 19, 2026

Create info panels that appear in the context panel based on semantic types

The English summary is being prepared. Showing the original description.

datagrok-ai/public74Updated Oct 19, 2026

Add a new single-objective optimization algorithm to the sci-comp library. Use when the user asks to implement a new optimizer (e.g. gradient descent, BFGS, simulated annealing, differential evolution).

The English summary is being prepared. Showing the original description.

datagrok-ai/public74Updated Oct 19, 2026

Answer a "how do I …" question about the platform with a Gherkin scenario on @datagrok-libraries/bdd that is filmed into a how-to video (grok-bdd guide), so the answer is demonstrated, sent as a video plus numbered steps, and kept as a regression test; the same scenarios, filmed in help mode, are the GIFs of the help pages

The English summary is being prepared. Showing the original description.

datagrok-ai/public74Updated Oct 19, 2026

Translate hand-written Playwright specs (or any existing UI tests) into Gherkin features on @datagrok-libraries/bdd, then prove the features test what they claim by backward-matching them against the originals with independent reviewers, and fix the gaps in the core, the library and the features

The English summary is being prepared. Showing the original description.

datagrok-ai/public74Updated Oct 19, 2026

Create a Datagrok application with routing, views, and data access

The English summary is being prepared. Showing the original description.

datagrok-ai/public74Updated Oct 19, 2026

See all skills by datagrok-ai

Report a problem with this skill