dokimos-dev/dokimos/create-experiment
Claude Code and agent skill by dokimos-dev/dokimos
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Checking that the SKILL.md file is still in the repository.
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".
Install
npx vskill@latest install dokimos-dev/dokimos/create-experimentSKILL.md
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 orchestratordokimos-examples/src/main/java/dev/dokimos/examples/basic/BasicEvaluationExample.java— simplest example
Experiment anatomy
An experiment consists of four parts:
- Dataset — the test cases (see
create-datasetskill) - Task — a function that takes an
Exampleand returnsMap<String, Object>of actual outputs - Evaluators — one or more
Evaluatorimplementations that score the outputs - Reporter (optional) — sends results to the Dokimos server or elsewhere
Basic template
With JUnit integration
With server reporting
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
- Determine from
$ARGUMENTSwhat the user wants to evaluate - Choose the right integration (plain Java, LangChain4j, Spring AI, Koog)
- Create or reference a dataset
- Define the task function wrapping the user's system
- Select appropriate evaluators for the use case
- Wire everything together with
Experiment.builder() - Add a JUnit test if the experiment should run in CI
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingplugins/create-experiment/skills/create-experiment/SKILL.md
Popularity
- Repository stars
- 42
- Repository forks
- 4
- 7-day trend score
- 19
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
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