Expert Prompts

MCP-registered prompts that inject expert-level domain knowledge directly into the AI’s context. Unlike tools, prompts are invoked by name rather than by function call — ask your AI to “use the <prompt-name> prompt” or simply phrase your request naturally and the AI will select the right prompt.

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accessibility-checker

Conduct a WCAG 2.1 accessibility audit of the current screen or a specific composable.

What to ask your AI:

• “check accessibility of this screen”
• “run an a11y audit”
• “are the touch targets large enough?”

Arguments

Name	Values	Description
focus	touch-targets / contrast / screen-reader / all	Scope the audit to a specific concern or run all checks

What it covers

• Touch targets — flags interactive elements smaller than 48 × 48 dp
• Contrast — checks text and icon contrast ratios against the 4.5:1 WCAG AA minimum
• Content descriptions — identifies Image and Icon composables missing contentDescription
• Screen reader order — verifies that the semantics traversal order matches the visual reading order

Tips

• Pair with cp_verify_render checks=[accessibility] to run accessibility checks as part of your golden verification workflow.
• Use focus: touch-targets first — it is the most common issue and the fastest to fix.

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compose-performance

Analyse the current screen or codebase for Compose performance anti-patterns and recomposition traps.

What to ask your AI:

• “are there any performance issues?”
• “find recomposition traps”
• “check for unnecessary allocations in composables”

Arguments

None.

What it covers

• Stability rules — classes that should be annotated @Stable or @Immutable
• Recomposition traps — inline lambdas creating new object instances, List<T> parameters instead of ImmutableList, state reads in the wrong scope
• Unkeyed lazy lists — LazyColumn items missing key parameter, causing full re-composition on data updates
• Layout performance — deep nesting that could trigger multiple measure passes
• Image performance — oversized bitmaps loaded without a size constraint

Tips

• Follow up with cp_track_recompositions to measure the real-world impact of issues the prompt identifies.
• The prompt outputs a prioritised fix list — address high-frequency leaf composables before higher-level screens.

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kmp-architect

Guide the AI through Kotlin Multiplatform architecture decisions for your project.

What to ask your AI:

• “help me structure the shared KMP code”
• “how should I split the data layer across platforms?”
• “architecture review for KMP”

Arguments

None.

What it covers

• Module structure — recommended split between :shared, :shared:data, :shared:domain, and platform-specific modules
• expect/actual — when to use expect/actual vs. dependency injection for platform differences
• Compose Multiplatform UI — sharing UI across Android, iOS, Desktop, and Web targets
• Data layer — Ktor for networking, SQLDelight for local persistence, DataStore for preferences
• Platform boundaries — which APIs must stay platform-specific (camera, biometrics, push notifications)
• Testing — commonTest strategies, runTest, and platform-specific test runners

Tips

• Run this prompt before making structural decisions — it is much cheaper to reorganise modules early than to refactor a monolithic :shared module later.
• Combine with cp_get_context scope=structure so the AI applies recommendations to your actual project layout rather than generic advice.

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ui-reviewer

Perform a visual quality review of the current screen against a design system or general Material 3 guidelines.

What to ask your AI:

• “review this UI”
• “visual quality check against our design system”
• “does this screen follow Material 3?”

Arguments

Name	Values	Description
design_system	material3 / custom / free-text description	The design system to validate against

What it covers

• Layout — padding consistency, alignment, spacing scale adherence
• Typography — correct use of type scale roles (headlineLarge, bodyMedium, etc.)
• Color and theming — use of semantic color tokens vs. hardcoded values; dark-mode compatibility
• Component patterns — correct usage of Material 3 components (cards, FABs, navigation bars)
• Visual hierarchy — whether the primary action is visually prominent relative to secondary actions

Tips

• Pass a screenshot via cp_render or cp_take_device_screenshot before invoking this prompt so the AI has a visual reference alongside the source code.
• For custom design systems, pass a brief description (e.g. "custom: uses 8dp spacing grid, IBM Plex Sans, brand blue #0F62FE") instead of custom.

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screenshot-test-writer

Generate a complete, ready-to-run screenshot test class for a composable or screen.

What to ask your AI:

• “write screenshot tests for HomeScreen”
• “generate Paparazzi tests for ProfileCard”
• “create a ComposeProof test suite for the onboarding flow”

Arguments

Name	Values	Description
framework	paparazzi / roborazzi / composeproof	The screenshot testing framework to target

What it covers

• A complete test class with @RunWith, setup/teardown, and one test method per @Preview annotation found on the target composable
• Correct framework-specific APIs (Paparazzi.snapshot(), captureRoboImage(), cp_diff mode=record)
• Multi-theme and multi-device test variants where the framework supports them
• Import statements and Gradle dependency snippets

Tips

• The generated class uses the same preview function names that cp_list_previews discovers — run that first so the AI knows which previews to cover.
• framework: composeproof generates tests that call cp_render_batch mode=record on first run and mode=verify on subsequent runs — no additional test runner configuration required.

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spec-verifier

Run a structured four-step workflow to verify that the current UI implementation matches a written specification.

What to ask your AI:

• “verify the UI against this spec”
• “does the checkout screen match the design doc?”
• “check the implementation against the Figma notes”

Arguments

Name	Values	Description
spec_source	markdown / jira / figma / pr / inline	Where the specification comes from

Workflow steps

1. Parse assertions — extract testable UI assertions from the spec source (e.g. “button label must be ‘Confirm Order’”, “error state shows red banner”)
2. Discover previews — call cp_list_previews to find the relevant @Preview functions
3. Verify — render each preview and check each assertion visually and via the semantics tree
4. Report — produce a pass/fail summary with annotated screenshots for any failures

Tips

• spec_source: inline lets you paste the spec directly in your message — useful for quick ad-hoc checks without a linked document.
• spec_source: pr instructs the AI to read the PR description as the spec — good for pre-merge visual reviews.
• Failures include a diff image and the exact assertion that failed, making it straightforward to file a bug or request a design update.

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compose-skill

Source-backed Compose best practices from actual androidx framework source code. 13 topic guides covering state, modifiers, side effects, lists, navigation, animation, theming, performance, accessibility, and deprecated pattern migration.

What to ask your AI:

• “best practices for Compose state management”
• “how should I use modifiers correctly?”
• “what’s the right way to handle side effects?”

Arguments

Name	Values	Description
topic	state / modifiers / side-effects / lists / navigation / animation / theming / performance / accessibility / composition-locals / deprecated / styles-api / all	Specific topic to look up

What it covers

• State — remember vs rememberSaveable, derivedStateOf, collectAsStateWithLifecycle, state hoisting
• Modifiers — ordering rules (outside-in), click area vs padding, size constraints
• Side effects — LaunchedEffect keys, DisposableEffect cleanup, rememberCoroutineScope for callbacks
• Lists — keys, contentType, stable item types, derivedStateOf for filtering
• Navigation — pass IDs not objects, launchSingleTop, deep links
• Animation — graphicsLayer for performance, API selection guide
• Theming — semantic tokens, dynamic color, dark theme testing
• Performance — stability, skipping, deferred reads, lambda stability
• Accessibility — 48dp touch targets, semantics, Roles, focus management
• Deprecated — accompanist migration paths with exact replacement code

Tips

• Based on compose-skill — 13 guides + 2.3MB of actual androidx source code analysis.
• Ask for a specific topic (e.g. topic: state) for focused guidance, or topic: all for an overview.

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rebound-basics

Guide to using Compose Rebound for recomposition budget monitoring — setup, budget classes, annotations, and violation interpretation.

What to ask your AI:

• “how do I set up Rebound?”
• “what budget class should my composable use?”
• “what does this Rebound violation mean?”

Arguments

None.

What it covers

• Setup — one-line Gradle plugin addition
• Budget classes — SCREEN (3/s), CONTAINER (10/s), INTERACTIVE (30/s), LIST_ITEM (60/s), ANIMATED (120/s), LEAF (5/s)
• Dynamic scaling — budgets multiply during scrolling (2x), animation (1.5x), user input (1.5x)
• @ReboundBudget annotation — manual budget class override for composables the heuristic misclassifies
• CLI tools — snapshot, summary, watch, ping
• Violation interpretation — what violations mean and common root causes

Tips

• Based on compose-rebound — a Kotlin compiler plugin for recomposition budget monitoring.
• Pair with cp_get_recomposition_stats or cp_track_recompositions for data-backed diagnosis.

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Pro Prompts

The following prompts require a ComposeProof Pro license. They add an intelligence layer on top of the free tools and prompts — combining multiple data sources for deeper analysis.

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stability-doctor (Pro)

Deep stability diagnosis combining Rebound budget data with framework knowledge. Traces unstable parameters to root cause and prescribes source-backed fixes.

What to ask your AI:

• “why is my HomeScreen recomposing so much?”
• “diagnose the stability issues in this screen”
• “which parameters are causing recompositions?”

What it does

1. Gathers evidence — uses cp_analyze_stability, cp_track_recompositions, cp_inspect_compose_state
2. Identifies the pattern — unstable parameter, unstable lambda, state read too high, missing key, rapid state updates
3. Prescribes fix — before/after code with root cause explanation
4. Verifies — confirms recomposition rate dropped below budget after fix

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rebound-advisor (Pro)

Interprets Rebound budget violations with severity triage and context-aware, actionable fixes.

What to ask your AI:

• “triage my Rebound violations”
• “which budget violations should I fix first?”
• “is this violation a false positive?”

What it does

1. Collects metrics via ComposeProof tools
2. Classifies each violation — checks if budget class is correct, if violation is contextual (scrolling/animation), identifies the recomposition driver
3. Triages by severity — Critical (>10x budget), High (>5x), Medium (>2x), Low, False positive
4. Prescribes targeted fix matched to the violation type and composable class

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migration-assistant (Pro)

Source-backed migration from deprecated Compose patterns to modern replacements. Scans codebase, counts occurrences, and provides exact before/after code.

What to ask your AI:

• “migrate from accompanist to built-in APIs”
• “find deprecated Compose patterns in my project”
• “replace SwipeRefresh with PullToRefreshBox”

What it does

1. Scans — uses cp_semantic_search to find deprecated imports and API usage
2. Maps — matches each deprecated pattern to its modern replacement (accompanist-systemuicontroller → enableEdgeToEdge(), accompanist-pager → Foundation HorizontalPager, etc.)
3. Generates migration plan — files affected, risk level, migration order
4. Verifies — suggests cp_render_batch to confirm no visual regressions after migration