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Kotlin App Config

Organization
navikt
kotlin-app-config

Sealed class-konfigurasjon for Kotlin-applikasjoner med miljøspesifikke innstillinger

Overview

Publishernavikt
Repositorycopilot
Skill namekotlin-app-config
Stars
60
Forks
16
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by navikt on GitHub. Read the source before you install it.

Installation

Install the Kotlin App Config AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/navikt/copilot/tree/main/skills/kotlin-app-config
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/navikt/copilot.git /tmp/copilot
mkdir -p .claude/skills
cp -r /tmp/copilot/skills/kotlin-app-config .claude/skills/kotlin-app-config
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kotlin App Config in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kotlin App Config on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kotlin App Config is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Kotlin Application Configuration Skill

This skill provides patterns for type-safe environment configuration using Kotlin sealed classes.

Sealed Class Configuration Pattern

kotlin
sealed class Environment(
    val name: String,
    val databaseUrl: String,
    val kafkaBrokers: String,
    val azureAdIssuer: String
) {
    data object Local : Environment(
        name = "local",
        databaseUrl = "jdbc:postgresql://localhost:5432/myapp",
        kafkaBrokers = "localhost:9092",
        azureAdIssuer = "http://localhost:8080/azuread"
    )

    data class Dev(
        private val env: Map<String, String>
    ) : Environment(
        name = "dev",
        databaseUrl = env.getValue("DATABASE_URL"),
        kafkaBrokers = env.getValue("KAFKA_BROKERS"),
        azureAdIssuer = env.getValue("AZURE_OPENID_CONFIG_ISSUER")
    )

    data class Prod(
        private val env: Map<String, String>
    ) : Environment(
        name = "prod",
        databaseUrl = env.getValue("DATABASE_URL"),
        kafkaBrokers = env.getValue("KAFKA_BROKERS"),
        azureAdIssuer = env.getValue("AZURE_OPENID_CONFIG_ISSUER")
    )

    companion object {
        fun from(env: Map<String, String>): Environment {
            return when (env["NAIS_CLUSTER_NAME"]) {
                "dev-gcp" -> Dev(env)
                "prod-gcp" -> Prod(env)
                else -> Local
            }
        }
    }
}

Using Configuration

kotlin
fun main() {
    val env = Environment.from(System.getenv())

    val dataSource = createDataSource(env.databaseUrl)
    val kafkaProducer = createKafkaProducer(env.kafkaBrokers)

    logger.info("Starting application in ${env.name} environment")
}

With Konfig Library

kotlin
import com.natpryce.konfig.*

data class AppConfig(
    val database: DatabaseConfig,
    val kafka: KafkaConfig,
    val azure: AzureConfig
)

data class DatabaseConfig(
    val url: String
)
data class KafkaConfig(
    val brokers: String
)
data class AzureConfig(
    val issuer: String
)

val config = EnvironmentVariables()

val appConfig = AppConfig(
    database = DatabaseConfig(
        url = config.getOrNull(Key("DATABASE_URL", stringType))
            ?: "jdbc:postgresql://localhost:5432/myapp"
    ),
    kafka = KafkaConfig(
        brokers = config.getOrNull(Key("KAFKA_BROKERS", stringType))
            ?: "localhost:9092"
    ),
    azure = AzureConfig(
        issuer = config.getOrNull(Key("AZURE_OPENID_CONFIG_ISSUER", stringType))
            ?: "http://localhost:8080/azuread"
    )
)

Alternative: Sealed Interface Pattern (navikt/hotlibs)

Production pattern from navikt/hotlibs supporting multiple cluster types:

kotlin
sealed interface Environment {
    val cluster: String
    val tier: Tier

    enum class Tier { TEST, LOCAL, DEV, PROD }

    companion object {
        private val all: List<Environment> = listOf(
            TestEnvironment,
            LocalEnvironment,
            GcpEnvironment.DEV,
            GcpEnvironment.PROD
        )

        val current: Environment by lazy {
            val cluster = System.getenv("NAIS_CLUSTER_NAME")
            all.find { it.cluster == cluster } ?: LocalEnvironment
        }
    }
}

sealed class DefaultEnvironment(
    override val cluster: String,
    override val tier: Environment.Tier
) : Environment

object TestEnvironment : DefaultEnvironment("test", Environment.Tier.TEST)
object LocalEnvironment : DefaultEnvironment("local", Environment.Tier.LOCAL)

enum class GcpEnvironment(
    override val cluster: String,
    override val tier: Environment.Tier
) : Environment {
    DEV("dev-gcp", Environment.Tier.DEV),
    PROD("prod-gcp", Environment.Tier.PROD)
}
kotlin
data class DatabaseConfig(
    val url: String,
    val username: String,
    val password: String
)

data class KafkaConfig(
    val brokers: String,
    val topic: String
)

data class AzureConfig(
    val clientId: String,
    val issuer: String,
    val jwksUri: String
)

fun loadConfig(): AppConfig {
    val config = ConfigurationProperties.systemProperties() overriding
                 EnvironmentVariables()

    return AppConfig(
        database = DatabaseConfig(
            url = config[Key("database.url", stringType)],
            username = config[Key("database.username", stringType)],
            password = config[Key("database.password", stringType)]
        ),
        kafka = KafkaConfig(
            brokers = config[Key("kafka.brokers", stringType)],
            topic = config[Key("kafka.topic", stringType)]
        ),
        azure = AzureConfig(
            clientId = config[Key("azure.client.id", stringType)],
            issuer = config[Key("azure.issuer", stringType)],
            jwksUri = config[Key("azure.jwks.uri", stringType)]
        )
    )
}

Benefits

  • Type Safety: Compile-time validation of configuration
  • Environment Separation: Clear boundaries between local/dev/prod
  • Testability: Easy to create test configurations
  • Documentation: Configuration structure is self-documenting

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Kotlin App Config AI skill do?

Sealed class-konfigurasjon for Kotlin-applikasjoner med miljøspesifikke innstillinger

Why use Kotlin App Config on TypingMind?

Because you install it once and use it with any model. Kotlin App Config is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kotlin App Config in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/navikt/copilot/tree/main/skills/kotlin-app-config. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Kotlin App Config?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kotlin App Config?

As many as you like. As long as a model supports skills, you can use Kotlin App Config with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kotlin App Config AI skill free?

Yes. It is published on GitHub by navikt under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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