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Compose Ui Testing Patterns

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chrisbanes
compose-ui-testing-patterns

Use when writing or reviewing Jetpack Compose UI tests, screenshot tests or baseline-recording evidence, previews, semantics assertions, fake image loading, keyboard input, focus assertions, interaction state (hover/pressed/focused), or tests for plain state-driven UI composables.

Overview

Publisherchrisbanes
Repositoryskills
Skill namecompose-ui-testing-patterns
Stars
1.1K
Forks
53
Bundled files
1
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.

  • 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 chrisbanes on GitHub. Read the source before you install it.

Installation

Install the Compose Ui Testing Patterns 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/chrisbanes/skills/tree/main/skills/compose-ui-testing-patterns
  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/chrisbanes/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/compose-ui-testing-patterns .claude/skills/compose-ui-testing-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Compose Ui Testing Patterns 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 Compose Ui Testing Patterns 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 Compose Ui Testing Patterns 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.

Compose: UI testing patterns

Core principle

Test the smallest UI contract that proves the behavior. Prefer plain state-driven UI tests with callbacks. Add integration only when lifecycle, navigation, DI, or platform behavior is the thing under test.

Procedure

  1. State the behavior and test concern the task asks you to prove.
  2. Inspect the existing test against that concern and choose the smallest sufficient seam from the table below.
  3. Keep focused edits within the requested test concern. Do not move test-only helpers into production or broaden production APIs unless that production boundary is itself under test.
  4. Drive controlled state or input, synchronize through Compose when needed, and assert an observable semantic, visual, or callback result.
  5. Finish with no edit when the existing test already uses the narrowest valid seam and proves the requested behavior.

Test target choice

What you need to proveTest shape
Text, button, loading/error branch, conditional contentPlain UI Compose test
Callback wiring from click/inputPlain UI Compose test
Focus navigation or keyboard behaviorCompose test with key input
Visual layout, clipping, elevation, typography, image compositionScreenshot test
State holder updates UI correctlyState holder/unit test plus one wiring smoke test
Hover, pressed, focused, dragged interaction statePlain UI test with MutableInteractionSource
Navigation, lifecycle, DI integrationIntegration test

Prefer plain UI tests

If the screen has a state holder/UI split, test the plain UI composable:

kotlin
composeTestRule.setContent {
    ProfileScreen(
        state = ProfileUiState(name = "Ada", canSave = true),
        onNameChange = {},
        onSaveClick = { saved = true },
        onBackClick = {},
    )
}

composeTestRule.onNodeWithText("Ada").assertIsDisplayed()
composeTestRule.onNodeWithText("Save").performClick()

assertThat(saved).isTrue()

This avoids constructing ViewModels, components, repositories, navigation, and dependency graphs for layout behavior.

Semantics first

Assert semantics when behavior is semantic:

  • Text exists: onNodeWithText.
  • Button is enabled/disabled: assertIsEnabled, assertIsNotEnabled.
  • Content is selected/focused/toggled: use semantics assertions.
  • Content is absent: assertDoesNotExist.

Use test tags for nodes that have no stable user-visible text or where multiple nodes share text. Do not use tags as the first choice for all assertions; user-visible semantics are usually stronger.

Callback testing

Use simple counters or captured values:

kotlin
var selectedId: String? = null

composeTestRule.setContent {
    ItemList(
        items = listOf(ItemUi("movie-1", "Movie")),
        onItemClick = { selectedId = it },
    )
}

composeTestRule.onNodeWithText("Movie").performClick()

assertThat(selectedId).isEqualTo("movie-1")

For plain captured callback values, a direct assertion after the action is usually enough. Use runOnIdle when the assertion needs Compose to finish applying snapshot state, recomposition, or queued UI work before reading the result.

Keep UI tests deterministic

For layout, branch, and callback behavior, render controlled state with setContent instead of constructing the production app graph. Production DI, repositories, lifecycle observers, and background effects add asynchronous work that is irrelevant to a plain UI contract and can make the test flaky.

Do not use Thread.sleep to wait for Compose. Drive the UI to a known state, then use semantic assertions and Compose synchronization (waitForIdle, runOnIdle, or a bounded waitUntil for a real asynchronous condition). Reserve full-app integration for behavior that actually depends on navigation, lifecycle, DI, or platform wiring.

Interaction state with MutableInteractionSource

When interaction state is the concern, read the interaction-state procedure completely before writing or reviewing the test. It requires direct, injected interaction state rather than fragile pointer simulation.

Keyboard and focus

For keyboard, TV, and desktop UI, drive navigation with the same input model users use (keys/D-pad), not clicks alone. Assert focused semantics, not colors or scale; reserve screenshots for visual focus treatment. If the inspected composable exposes no input surface that can change the state, name the missing host or parent interaction seam before proposing a key-driven test; do not assume a selector or control exists in the supplied UI.

Details—focus graph, FocusRequester, restoration, key handlers, and test patterns: compose-focus-navigation.

Screenshot tests

Use screenshots for visual contracts that semantics cannot prove:

  • Layout spacing/alignment.
  • Themed colors, typography, elevation, shadows.
  • Image composition, gradients, overlays.
  • Focus highlight appearance.
  • Loading skeletons or dense visual states.

Keep screenshot state deterministic:

  • Use fixed state data.
  • Freeze clocks or animation progress when possible.
  • Replace network/image loading with fake or preview handlers.
  • Avoid asserting dynamic text such as current time unless controlled.

When screenshot defaults change

  • Keep an intentionally changed named preset or default in the test and update its baseline. Do not substitute a raw value to retain the old image; preserve assertions and tolerances unless independently justified.
  • Keep an explicit fixed value when that fixed resolution or geometry is the contract.
  • Verify recording separately from comparison: inspect the expected artifact paths and intentional baseline diff. Read the exact output path from the test, confirm that artifact exists, and name that literal path in the verification evidence. When a verification log reports a pass, state what passed and then check recording independently; a passing comparison does not prove that recording wrote the artifact. If the artifact or baseline diff is unavailable, report that gap rather than claiming recording succeeded. Keep tool-specific commands in the repository runbook.

Fake images and platform services

When image content is irrelevant, fake the loader and assert the requested model if that is the behavior. The exact hook depends on your image library; a project helper might look like this:

kotlin
val requestedModels = mutableListOf<Any?>()

// Example helper, not a Compose API.
setContentWithFakeImageLoader { request ->
    requestedModels += request.data
    errorPainter()
}

When image appearance matters, provide a deterministic local painter/bitmap instead of network data.

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 Compose Ui Testing Patterns AI skill do?

Use when writing or reviewing Jetpack Compose UI tests, screenshot tests or baseline-recording evidence, previews, semantics assertions, fake image loading, keyboard input, focus assertions, interaction state (hover/pressed/focused), or tests for plain state-driven UI composables.

Why use Compose Ui Testing Patterns on TypingMind?

Because you install it once and use it with any model. Compose Ui Testing Patterns 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 Compose Ui Testing Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chrisbanes/skills/tree/main/skills/compose-ui-testing-patterns. 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 Compose Ui Testing Patterns?

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 Compose Ui Testing Patterns?

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

Is the Compose Ui Testing Patterns AI skill free?

Yes. It is published on GitHub by chrisbanes 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.

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