Kotlin Tooling Native Build Performance logo

Kotlin Tooling Native Build Performance

OrganizationPopular
Kotlin
kotlin-tooling-native-build-performance

Diagnoses and fixes slow Kotlin/Native compilation and linking in Kotlin Multiplatform projects that target iOS. Use when the user reports slow iOS or shared-framework builds, long linkDebug*/linkRelease* or XCFramework tasks, cold CI builds that re-download the Kotlin/Native toolchain, KSP or other generated code on the native path, transitiveExport usage, or asks for a local-development versus CI build performance plan.

Overview

PublisherKotlin
Repositorykotlin-agent-skills
Skill namekotlin-tooling-native-build-performance
Stars
1.1K
Forks
43
Bundled files
6
LicenseApache-2.0
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Kotlin Tooling Native Build Performance 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/Kotlin/kotlin-agent-skills/tree/main/skills/kotlin-tooling-native-build-performance
  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/Kotlin/kotlin-agent-skills.git /tmp/kotlin-agent-skills
mkdir -p .claude/skills
cp -r /tmp/kotlin-agent-skills/skills/kotlin-tooling-native-build-performance .claude/skills/kotlin-tooling-native-build-performance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kotlin Tooling Native Build Performance 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 Tooling Native Build Performance 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 Tooling Native Build Performance 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/Native Build Performance

Turn "the iOS build is slow" into a measured diagnosis and a small set of safe fixes. Two rules apply throughout:

  1. Never trade away required release behavior. A faster local loop must not change what CI publishes.
  2. Measure before and after with the same command and the same build state. An unmeasured fix is a guess.

Step 0: Classify the Slow Scenario

Establish four facts before editing anything: where (local or CI), what (debug feedback loop or release/distribution artifact), state (first build, clean, warm, or no-op), and phase (which tasks dominate the log). Then match the dominant symptom:

Symptom in the build logLikely causeRead
linkRelease* or *ReleaseXCFramework tasks in a local development loopBuilding distribution artifacts for developmentartifacts-and-targets
Kotlin/Native compiler distribution downloaded on every CI run~/.konan not preserved between runscaching-and-gradle
Long pause before the first task startsConfiguration phase, no configuration cachecaching-and-gradle
All iOS targets build when only one simulator is neededBroad task (build, assemble, assemble*XCFramework) or unused targetsartifacts-and-targets
ksp* tasks ahead of compileKotlinIos*Generated-code work on the native pathexports-and-generated-code
Small source edit recompiles and relinks everythingCompiler caches disabled, or missing incrementalitycaching-and-gradle, experimental
Machine overloaded while several link* tasks run at onceParallel native linkingcaching-and-gradle, worker-limit caveat

Step 1: Audit and Measure

  1. Run the static audit from the project root:

    bash
    scripts/audit-native-build.sh /path/to/project

    It is read-only and prints file:line findings (disabled caches, broad local tasks, transitiveExport, broad KSP configuration, missing CI .konan cache), each pointing at the reference file with the fix. Findings are leads, not verdicts — confirm each against project policy.

  2. Find the command the user actually waits for: a script, a CI step, or the Gradle invocation inside an Xcode build phase. Optimize that command, not a task you picked yourself.

  3. Run it twice when practical. The first build downloads Kotlin/Native components and fills caches; only the second and later runs are representative. Attribute time per task before blaming the compiler:

    properties
    kotlin.build.report.output=file   # writes build/reports/kotlin-build/

    Gradle's --scan or --profile work too.

  4. If you cannot run the build (no macOS host, no Xcode), analyze logs, build scans, or checked-in metrics instead — and state explicitly that the conclusion is static.

Step 2: Fix in Safe Order

Apply fixes one at a time, re-measuring as you go:

  1. Restore healthy defaults — remove cache/daemon workarounds, enable Gradle build and configuration caches, keep ~/.konan warm in CI, update Kotlin: references/caching-and-gradle.md
  2. Build only what the feedback loop needs — one specific task per loop, correct integration method, justified target matrix: references/artifacts-and-targets.md
  3. Cut export and generated-code cost — drop transitiveExport, narrow export(...), scope KSP work to the native compilations that need it: references/exports-and-generated-code.md
  4. Experimental switches last, with the user's agreement: references/experimental.md

Worked Example

A developer on an Apple Silicon Mac complains that "every shared-module change costs 12 minutes". Their loop runs ./gradlew :shared:assembleXCFramework. A build scan of the second (warm) run shows:

:shared:linkReleaseFrameworkIosArm64             348s
:shared:linkReleaseFrameworkIosX64               341s
:shared:compileKotlinIosX64                       96s
:shared:linkDebugFrameworkIosSimulatorArm64       41s
:shared:compileKotlinIosSimulatorArm64            38s
configuration phase                               64s

Reasoning chain:

  • The loop is local + debug + warm, but ~690s goes to linkRelease* — release linking is an order of magnitude slower than debug and only CI needs it. Replace the local command with :shared:linkDebugFrameworkIosSimulatorArm64 (or the Xcode embed task if Xcode drives the build). (artifacts-and-targets)
  • All iosX64 work serves Intel simulators; ask whether the team still supports them before removing the target. (artifacts-and-targets)
  • 64s of configuration on every run disappears behind org.gradle.configuration-cache=true once trialed. (caching-and-gradle)
  • Expected loop after the change: ~40s compile + ~40s link on warm builds — confirm by re-running the new command twice and comparing.
  • CI keeps assembleXCFramework untouched; note that explicitly in the report.

Verify

  • Re-run the exact baseline command; compare warm build against warm build, not warm against cold.
  • Second run with the configuration cache reports it is being reused.
  • The local development log no longer contains linkRelease*, *ReleaseXCFramework, or removed generator tasks.
  • CI still produces every required release artifact, unchanged.
  • Tests pass and the app still runs from Xcode.
  • scripts/audit-native-build.sh reports no findings you have not consciously accepted and documented.

Report Your Changes

Close with a short performance note:

  • The slow scenario (local/CI, debug/release, cold/warm) and the measured evidence — or a statement that the analysis was static.
  • Each change, and why it is safe for release behavior.
  • The before/after commands the user can run to confirm the win.
  • Remaining tradeoffs: experimental flags enabled, targets removed under a policy assumption, worker limits, or generated-code work deferred.
  • Links to the relevant official documentation below.

Official Documentation

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 Tooling Native Build Performance AI skill do?

Diagnoses and fixes slow Kotlin/Native compilation and linking in Kotlin Multiplatform projects that target iOS. Use when the user reports slow iOS or shared-framework builds, long linkDebug*/linkRelease* or XCFramework tasks, cold CI builds that re-download the Kotlin/Native toolchain, KSP or other generated code on the native path, transitiveExport usage, or asks for a local-development versus CI build performance plan.

Why use Kotlin Tooling Native Build Performance on TypingMind?

Because you install it once and use it with any model. Kotlin Tooling Native Build Performance 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 Tooling Native Build Performance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Kotlin/kotlin-agent-skills/tree/main/skills/kotlin-tooling-native-build-performance. 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 Tooling Native Build Performance?

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 Tooling Native Build Performance?

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

Is the Kotlin Tooling Native Build Performance AI skill free?

Yes. It is published on GitHub by Kotlin under the Apache-2.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇