From evaluate-koog
Sets up Dokimos evaluations for Koog AI agents in Kotlin, as system under test or judge using ExactMatchEvaluator, LLMJudgeEvaluator, or DSL.
npx claudepluginhub dokimos-dev/dokimos --plugin evaluate-koogThis skill uses the workspace's default tool permissions.
Set up Dokimos evaluation for a Koog AI agent. The user will describe their agent and evaluation goals via `$ARGUMENTS`.
Sets up Dokimos evaluation for AI agents using tools, assessing tool call validity, correctness, task completion, argument hallucinations, and tool definition quality.
Builds AI agent evaluations using Anthropic patterns: code/model/human graders, tasks, trials, benchmarks for coding, conversational, research agents.
Builds evaluation frameworks for agent systems to test performance systematically, validate context engineering choices, and measure improvements over time.
Share bugs, ideas, or general feedback.
Set up Dokimos evaluation for a Koog AI agent. The user will describe their agent and evaluation goals via $ARGUMENTS.
dokimos-koog/src/main/kotlin/dev/dokimos/koog/KoogSupport.ktdokimos-koog/src/test/kotlin/dev/dokimos/koog/dev.dokimos:dokimos-koogBefore writing code, read KoogSupport.kt to understand the available utilities.
KoogSupport.kt provides:
asJudge(agentCall: suspend (String) -> String) — wraps any suspend function into a JudgeLMasJudge(agent: () -> AIAgent<String, String>) — wraps a Koog agent factory into a JudgeLMAIAgent.runBlocking(input, context) — extension to run a Koog agent synchronouslyval agent: () -> AIAgent<String, String> = { createMyAgent() }
val task = Task { example ->
val input = example.inputs()["input"] as String
val output = agent().runBlocking(input)
mapOf("output" to output)
}
val result = Experiment.builder()
.name("Koog Agent Evaluation")
.dataset(dataset)
.task(task)
.evaluator(ExactMatchEvaluator.builder().build())
.build()
.run()
val judge = asJudge { prompt -> myAgent().run(prompt) }
// or
val judge = asJudge { createMyAgent() }
val evaluator = LLMJudgeEvaluator.builder()
.name("helpfulness")
.judge(judge)
.criteria("Is the response helpful and accurate?")
.evaluationParams(listOf(
EvalTestCaseParam.INPUT,
EvalTestCaseParam.ACTUAL_OUTPUT,
EvalTestCaseParam.EXPECTED_OUTPUT
))
.threshold(0.7)
.build()
If the user has dokimos-kotlin as a dependency, use the DSL:
val result = experiment {
name = "Koog Agent Eval"
dataset = Dataset.fromJson(Path.of("datasets/qa.json"))
task { example ->
val output = agent().runBlocking(example.input())
mapOf("output" to output)
}
evaluator(ExactMatchEvaluator.builder().build())
}
The user needs dokimos-koog:
<dependency>
<groupId>dev.dokimos</groupId>
<artifactId>dokimos-koog</artifactId>
<version>${dokimos.version}</version>
</dependency>
Koog itself is a provided-scope dependency — the user must bring their own version.
$ARGUMENTS what the Koog agent does and how it's constructedKoogSupport utilities