kotlin-data-kmp-data-layer

name	kotlin-data-kmp-data-layer
description	Use when implementing or reviewing KMP data layers, including repositories, data sources, source-of-truth design, API exposure, conflict resolution, error handling, and main-safe data operations.
license	Apache-2.0
metadata	{"author":"Mariano Miani","version":"1.1.0"}

Kotlin Multiplatform Data Layer

Use this skill when designing, implementing, or reviewing the data layer in a Kotlin Multiplatform project.

This skill is intentionally strict. Its purpose is to keep repositories meaningful, data ownership explicit, source-of-truth decisions clear, and upper layers insulated from transport and persistence details.

Primary goals

The data-layer design should optimize for:

• clear ownership of application data
• a single source of truth per repository
• repositories as the entry points to data access
• clean separation between repositories and data sources
• conflict resolution across multiple sources in one place
• API shapes that fit Kotlin best practices
• immutable exposed data
• main-safe repository and data-source APIs
• predictable failure handling
• testable mapping and coordination logic

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Official defaults to prefer

Unless the project has a strong reason not to, prefer these defaults:

• the data layer contains application data and business logic
• repositories expose data to the rest of the app
• repositories centralize changes to data
• repositories resolve conflicts between multiple data sources
• repositories abstract data sources from the rest of the app
• repositories act as the entry points to the data layer
• each data source is responsible for one source only
• each repository defines a single source of truth
• one-shot operations use suspend functions
• updates over time use Flow
• exposed data is immutable
• repository and data-source APIs are main-safe

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Review and implementation dimensions

1. Data-layer responsibility

The data layer is not only a transport layer.

Check whether:

• the data layer owns application data concerns
• business logic that belongs with data ownership is located here
• upper layers are protected from storage and transport details

Flag as a concern when:

• the data layer is treated as only an HTTP wrapper
• business rules around creation, storage, reconciliation, or change tracking leak upward
• repositories are too thin to add meaningful ownership or coordination

2. Repository responsibility

Repositories should be meaningful architectural boundaries.

Check whether each repository is responsible for:

• exposing data to the rest of the app
• centralizing changes to that data
• resolving conflicts between multiple sources
• abstracting data sources from callers
• containing business logic related to data ownership and coordination

Flag as a concern when:

• repositories merely mirror endpoint methods
• repositories expose transport-layer details directly
• upper layers coordinate local and remote sources themselves
• conflict resolution is duplicated outside the repository

3. Data-source responsibility

Each data source should work with exactly one source of data.

Examples:

• a network source
• a database source
• a file source
• an in-memory source

Check whether:

• each data source has a narrow responsibility
• repositories depend on data sources
• other layers do not depend on data sources directly

Flag as a concern when:

• ViewModels, presenters, use cases, or UI call data sources directly
• one data source mixes several unrelated storage/transport concerns
• repositories do not meaningfully sit between callers and sources

4. Source of truth

Each repository should define a single source of truth.

Check whether:

• the source of truth is explicit
• the data exposed from the repository comes from that source of truth
• the repository updates that source of truth consistently
• multi-source reconciliation feeds back into the source of truth

Strong candidates:

• local database
• in-memory cache for specific bounded cases

For offline-first behavior, prefer a local data source such as a database as the source of truth.

Flag as a concern when:

• multiple sources are treated as simultaneously authoritative
• UI consumes remote responses directly while local state is supposed to be canonical
• the source-of-truth choice is implicit or unstable

5. API shape

The data layer should expose APIs based on the kind of operation.

Prefer:

• suspend functions for one-shot CRUD-style operations
• Flow for observing changes over time

Check whether:

• one-shot work is modeled as one-shot APIs
• long-lived observation uses Flow
• callers receive stable APIs that match how the data behaves

Flag as a concern when:

• streaming data is exposed as repeated polling by upper layers without reason
• one-shot operations are modeled as long-lived observable streams unnecessarily
• API style is inconsistent across similar repositories

6. Immutability of exposed data

The data exposed by the data layer should be immutable.

Check whether:

• repositories expose immutable models or collections
• callers cannot mutate shared state directly
• data crossing layer boundaries is safe to use concurrently

Flag as a concern when:

• mutable state is exposed directly from repositories
• collections are shared in mutable form across layers
• upper layers can tamper with repository-owned state

7. Main-safety and threading

Repository and data-source APIs should be safe to call from the main thread.

That means these classes are responsible for shifting blocking or expensive work to the proper thread when needed.

On Android/JVM targets, this typically means dispatching to Dispatchers.IO or a similar dispatcher inside repositories and data sources.

On Kotlin/Native targets (iOS, macOS, etc.), the threading model changed significantly in Kotlin 1.7.20+ with the new memory model. The old frozen-object restrictions have been removed. Coroutines on Kotlin/Native now behave much more like on JVM. However, some platform SDKs (UIKit, certain iOS APIs) still require main-thread access, so threading discipline remains relevant at the platform boundary.

Check whether:

• file, database, network, or expensive filtering work is not forced onto callers
• repositories and data sources encapsulate threading concerns appropriately
• upper layers are not made responsible for knowing threading details of the data layer
• platform-specific threading requirements (e.g., main-thread-only iOS APIs) are respected at the edge, not inside shared business logic

Flag as a concern when:

• callers must manually move work off the main thread for normal repository usage
• repositories perform blocking work without appropriate dispatching
• threading policy is inconsistent or undocumented across targets
• Kotlin/Native-specific threading behavior is assumed to be the same as the old frozen-object model

8. Error handling model

The official guidance allows repository and data-source interactions to either succeed or throw exceptions, using Kotlin’s normal error-handling mechanisms such as try/catch for suspend functions and catch for flows.

Check whether:

• the project has a consistent error model
• failures are surfaced deliberately
• custom exceptions are used when domain-specific failure meaning matters
• result-wrapper patterns are used only when the project deliberately wants that API shape

Flag as a concern when:

• errors are swallowed
• every repository invents a different failure contract
• strings are used as the primary error model
• upper layers cannot distinguish meaningful failure cases when they need to

9. Naming and ownership clarity

Prefer repository names based on the data they own.

Prefer data-source names based on:

• the data they handle
• and the type of source

Check whether:

• repository names describe owned data, not transport details
• data-source names describe role and source clearly
• implementation details do not leak into architecture unnecessarily

Flag as a concern when:

• naming obscures ownership
• class names couple callers to a storage technology without reason
• repository/data-source boundaries are hard to infer from names

10. Upper-layer insulation

The data layer should shield the rest of the app from implementation detail.

Check whether:

• UI/state-holder/domain code depends on repository contracts rather than raw network/database details
• storage/transport migrations would stay localized inside the data layer
• data-source technology choices do not leak upward

Flag as a concern when:

• UI models mirror backend payloads directly
• persistence schemas leak into presentation
• changing a storage or network implementation would force changes across many layers

11. Testability

The data layer should be testable in isolation.

Check whether:

• repository coordination logic can be tested
• mappers are pure and testable
• source-of-truth behavior can be validated
• conflict-resolution logic can be exercised with fakes
• error paths are testable

Flag as a concern when:

• repository logic depends on hidden globals
• source-of-truth behavior is implicit and hard to verify
• coordination logic only works in large integration tests

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Required output format

When using this skill, respond with:

1. Data-layer summary

   • repository structure
   • data-source structure
   • source-of-truth design
   • API shape

2. What is structurally sound

   • concrete strengths only

3. Issues by dimension

   • data-layer responsibility
   • repository responsibility
   • data-source responsibility
   • source of truth
   • API shape
   • immutability
   • main-safety
   • error handling
   • naming/ownership clarity
   • upper-layer insulation
   • testability

4. Severity for each issue

   • high / medium / low

5. Concrete recommendations

   • exact repository/data-source restructuring steps
   • source-of-truth corrections
   • API-shape corrections
   • threading/error-model corrections

6. Suggested target structure

   • proposed repository/data-source layout if useful

7. Open risks

   • migration cost
   • compatibility concerns
   • remaining architectural ambiguity

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Anti-patterns to flag aggressively

• repositories that only mirror endpoints
• upper layers depending directly on data sources
• no explicit single source of truth
• multiple writable authorities for the same data
• mutable data exposed from repositories
• blocking or expensive work forced onto callers
• transport or persistence details leaking into UI/state holders
• silent error swallowing
• inconsistent API shapes for similar operations

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References

• Android data layer: https://developer.android.com/topic/architecture/data-layer
• Android architecture recommendations: https://developer.android.com/topic/architecture/recommendations
• Kotlin Multiplatform project structure: https://kotlinlang.org/docs/multiplatform/multiplatform-discover-project.html
• Kotlin coroutines: https://kotlinlang.org/docs/coroutines-overview.html