create-experiment

name	create-experiment
description	Scaffolds a Dokimos Experiment that wires together a dataset, task, evaluators, and optional reporter. Use this skill when the user wants to create an evaluation experiment, set up an eval pipeline, run evaluations against a dataset, or build an end-to-end evaluation workflow. Also use when the user says "evaluate my LLM" or "test my model".

Create Experiment

Scaffold a Dokimos Experiment. The user will describe the evaluation goal via $ARGUMENTS.

Where things live

• Experiment class: dokimos-core/src/main/java/dev/dokimos/core/Experiment.java
• Task interface: dokimos-core/src/main/java/dev/dokimos/core/Task.java
• Example experiments: dokimos-examples/src/main/java/dev/dokimos/examples/

Before writing code, read these files to understand the API:

• Experiment.java — the orchestrator
• dokimos-examples/src/main/java/dev/dokimos/examples/basic/BasicEvaluationExample.java — simplest example

Experiment anatomy

An experiment consists of four parts:

1. Dataset — the test cases (see create-dataset skill)
2. Task — a function that takes an Example and returns Map<String, Object> of actual outputs
3. Evaluators — one or more Evaluator implementations that score the outputs
4. Reporter (optional) — sends results to the Dokimos server or elsewhere

Basic template

Dataset dataset = Dataset.fromJson(Path.of("src/test/resources/datasets/my-dataset.json"));

Task task = example -> {
    String input = example.input();
    String output = callYourLLM(input);
    return Map.of("output", output);
};

List<Evaluator> evaluators = List.of(
        ExactMatchEvaluator.builder()
                .name("Exact Match")
                .threshold(1.0)
                .build()
);

ExperimentResult result = Experiment.builder()
        .name("My Evaluation")
        .description("Evaluating my LLM on QA tasks")
        .dataset(dataset)
        .task(task)
        .evaluators(evaluators)
        .build()
        .run();

System.out.println("Pass rate: " + result.passRate());

With JUnit integration

public class MyEvaluationTest {

    @ParameterizedTest(name = "[{index}] {0}")
    @DatasetSource("classpath:datasets/my-dataset.json")
    void testMyLLM(Example example) {
        String actualOutput = callYourLLM(example.input());
        EvalTestCase testCase = example.toTestCase(actualOutput);

        List<Evaluator> evaluators = List.of(
                ExactMatchEvaluator.builder().build()
        );

        assertEval(testCase, evaluators);
    }
}

With server reporting

Reporter reporter = DokimosServerReporter.builder()
        .baseUrl("http://localhost:8080")
        .build();

ExperimentResult result = Experiment.builder()
        .name("My Evaluation")
        .dataset(dataset)
        .task(task)
        .evaluators(evaluators)
        .reporter(reporter)
        .build()
        .run();

Builder options

Method	Description	Default
.name(String)	Experiment name	"unnamed"
.description(String)	Description	""
.dataset(Dataset)	Test dataset	required
.task(Task)	System under test	required
.evaluator(Evaluator)	Add a single evaluator	—
.evaluators(List)	Add multiple evaluators	—
.reporter(Reporter)	Result reporter	NoOpReporter
.parallelism(int)	Concurrent examples	1
.runs(int)	Repeat count for variance reduction	1
.metadata(String, Object)	Add experiment metadata	—

Steps

1. Determine from $ARGUMENTS what the user wants to evaluate
2. Choose the right integration (plain Java, LangChain4j, Spring AI, Koog)
3. Create or reference a dataset
4. Define the task function wrapping the user's system
5. Select appropriate evaluators for the use case
6. Wire everything together with Experiment.builder()
7. Add a JUnit test if the experiment should run in CI