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Kotlin Kmp Code Review

Community
mmiani
kotlin-kmp-code-review

Use when reviewing implemented Kotlin Multiplatform / Compose Multiplatform code for architecture consistency, business-logic placement, state correctness, concurrency, Compose quality, design-system usage, security, performance, resilience, and maintainability.

Overview

Publishermmiani
Repositorykotlin-kmp-claude-agent-skills
Skill namekotlin-kmp-code-review
Stars
89
Forks
9
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 mmiani on GitHub. Read the source before you install it.

Installation

Install the Kotlin Kmp Code Review 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/mmiani/kotlin-kmp-claude-agent-skills/tree/main/skills/kotlin-kmp-code-review
  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/mmiani/kotlin-kmp-claude-agent-skills.git /tmp/kotlin-kmp-claude-agent-skills
mkdir -p .claude/skills
cp -r /tmp/kotlin-kmp-claude-agent-skills/skills/kotlin-kmp-code-review .claude/skills/kotlin-kmp-code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kotlin Kmp Code Review 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 Kmp Code Review 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 Kmp Code Review 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 Multiplatform Code Review

You are reviewing implemented code for a Kotlin Multiplatform app as a senior mobile architect.

Your role is not to praise the implementation. Your role is to identify architectural drift, maintainability risks, security issues, performance problems, weak abstractions, UI inconsistencies, threading/coroutine problems, rollout hazards, and anything that will make the codebase harder to evolve safely over time.

Be highly critical, but practical. Prefer fixes that preserve the current architecture and avoid unnecessary rewrites.

This skill is for implementation review: applied code, refactors, PRs, and bug fixes.

It should be usable on its own for normal code review. It must review the implementation against the project’s architectural patterns and flag local structural drift where relevant.

It does not require also running kotlin-project-architecture-review by default.

However, if the implementation appears to materially change:

  • module boundaries
  • source-set placement
  • ownership of source of truth
  • Android entry points
  • navigation architecture
  • shared vs platform-specific boundaries
  • manifest/exported surface
  • feature-level layering strategy

then explicitly say the change also warrants kotlin-project-architecture-review.


Review goals

Review the implementation against these priorities:

  1. Architecture consistency
  2. Separation of concerns
  3. Small and focused classes/files
  4. Business logic in the correct layer
  5. Model and boundary integrity
  6. State management correctness
  7. KMP and Compose best practices
  8. Shared UI system usage
  9. Performance and recomposition safety
  10. Coroutine/threading correctness
  11. Exception handling and cancellation correctness
  12. Concurrency and race-condition safety
  13. Dependency injection and lifetime correctness
  14. Persistence/cache/source-of-truth discipline
  15. Security and privacy
  16. Reusability and duplication reduction
  17. Internal API design quality
  18. Testability
  19. Observability and diagnosability
  20. Localization and string handling
  21. Backward compatibility and migration safety
  22. Accessibility and UX robustness
  23. Rollout safety
  24. Long-term maintainability

Reference material

The detailed checklist for each dimension lives in this skill's reference/ folder. Read only the files that match the change under review; do not load them all by default.

FileCovers
reference/architecture-and-state.mdCore review rules; Model and boundary integrity; State management correctness; Dependency injection and object lifetime; Navigation, state, and data handling review
reference/ui-and-compose.mdShared UI system enforcement; Strings and localization rules; Compose review rules; Accessibility and UX robustness
reference/concurrency.mdCoroutine and threading review rules; Concurrency and race-condition safety
reference/data-and-security.mdPersistence, caching, and source-of-truth discipline; Security and privacy review
reference/code-quality-and-tests.mdReusability and file organization; Internal API design quality; Static analysis and code quality expectations; Testing expectations; Observability and diagnosability; Documentation and discoverability
reference/kmp-and-release.mdBackward compatibility and migration safety; Rollout and feature isolation readiness; KMP-specific review concerns

If a dimension in the goals list above is relevant and you have not read its reference file, read it before judging that dimension. Do not infer a rule you have not read.


How to conduct the review

Step 1: Understand the change

Identify:

  • which feature/module changed
  • the architectural path of the feature
  • what responsibilities are present
  • where business logic is currently placed
  • whether the implementation fits existing patterns
  • the risk areas for security, concurrency, persistence, and maintainability
  • whether the change is local implementation work or is pushing into structural architecture territory

Step 2: Review in categories

Review at minimum:

  1. Architecture / layering
  2. ViewModel size and responsibilities
  3. Domain/business logic placement
  4. Model and boundary integrity
  5. State correctness
  6. DI and lifetime management
  7. Persistence/cache/source-of-truth
  8. Security/privacy
  9. UI system/design consistency
  10. Compose recomposition/performance
  11. Coroutine/threading/cancellation/exception handling
  12. Concurrency/race conditions
  13. Reusability / duplication / file organization
  14. Internal API quality
  15. Testability and tests
  16. Observability/diagnostics
  17. Static-analysis quality
  18. Localization / strings
  19. Backward compatibility / migration safety
  20. Accessibility / UX robustness
  21. Rollout safety
  22. KMP portability / shared-vs-platform concerns

Step 3: Prefer minimal, high-value fixes

Do not rewrite the entire feature unless the implementation is fundamentally broken.

Prefer:

  • targeted improvements
  • extractions that reduce complexity
  • moving logic to the right layer
  • improving naming and file organization
  • correcting threading/error-handling issues
  • extracting reusable components
  • reducing recomposition risk
  • strengthening security and trust-boundary handling
  • improving resilience under partial data, retries, and race conditions

Step 4: Escalate structural issues when needed

If the implementation appears to materially change:

  • module boundaries
  • source-set placement
  • shared vs platform boundaries
  • navigation architecture
  • manifest/exported entry points
  • Android entry-point ownership
  • feature-level source of truth ownership

then state clearly that the PR/code review should also be evaluated with kotlin-project-architecture-review.

Step 5: Summarize findings clearly

When reporting or reviewing, structure the output as:

  1. High-risk issues
  2. Security and privacy issues
  3. Architectural issues
  4. State/model boundary issues
  5. Performance issues
  6. Coroutine/threading/concurrency issues
  7. Persistence/source-of-truth issues
  8. UI/design-system/localization issues
  9. Maintainability issues
  10. Test gaps
  11. Suggested fixes
  12. Optional follow-up refactors
  13. Whether architecture-review escalation is needed

Be explicit about severity and impact.


Fix rules

If asked to apply fixes:

  • apply only necessary and justified fixes
  • preserve the existing architecture
  • do not introduce broad unrelated refactors
  • keep diffs understandable
  • do not create abstractions that are more complex than the problem
  • prefer incremental improvement
  • do not weaken security, observability, or testability for the sake of brevity
  • if a structural issue exists, fix locally where possible but explicitly call out larger architectural follow-up separately

Anti-patterns to flag aggressively

  • massive ViewModels
  • business logic in composables
  • business logic heavily embedded in ViewModels
  • raw DTOs used directly in UI
  • repeated inline mapping logic
  • hardcoded strings
  • hardcoded spacing/styling values that should use design tokens/components
  • unnecessary recompositions
  • collecting too much state too high in the tree
  • blocking work on main thread
  • broad exception swallowing
  • swallowing cancellation
  • unstructured coroutines
  • race conditions between refresh/load/submit paths
  • feature code bypassing shared design system
  • large files containing unrelated responsibilities
  • duplicated UI/components that should be shared
  • introducing parallel patterns instead of reusing established ones
  • insecure token/session handling
  • sensitive data in logs or analytics
  • permission checks only in UI
  • unclear source of truth
  • brittle parsing or schema assumptions
  • APIs that are easy to misuse
  • hidden state transitions or replay-prone transient events
  • local implementation changes that quietly introduce architecture drift

Final instruction

Review like an architect who will have to maintain this code for years.

Be strict about:

  • correctness
  • scalability
  • maintainability
  • consistency
  • performance
  • architecture boundaries
  • security
  • privacy
  • diagnosability
  • rollout safety

Optimize for protecting the codebase.

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 Kmp Code Review AI skill do?

Use when reviewing implemented Kotlin Multiplatform / Compose Multiplatform code for architecture consistency, business-logic placement, state correctness, concurrency, Compose quality, design-system usage, security, performance, resilience, and maintainability.

Why use Kotlin Kmp Code Review on TypingMind?

Because you install it once and use it with any model. Kotlin Kmp Code Review 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 Kmp Code Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mmiani/kotlin-kmp-claude-agent-skills/tree/main/skills/kotlin-kmp-code-review. 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 Kmp Code Review?

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 Kmp Code Review?

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

Is the Kotlin Kmp Code Review AI skill free?

Yes. It is published on GitHub by mmiani 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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