# Android skills AI-optimized instructions for AI tools and agents to perform specialized Android development workflows using Android best practices. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Leverage Android skills and Gemma 4 in Android Studio Quail 4 DevFeed: [Leverage Android skills and Gemma 4 in Android Studio Quail 4]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-09-01T15:00:00Z Content type: release Language: en Sources: [Android Developers Blog](), [Android Developers Blog]() Topics: [Android skills](), [Android Studio](), [Large Language Model](), [releases](), [gemma4]() Tags: [agent](), [agent-skills](), [android-gradle-plugin](), [android-skills](), [android-studio](), [gemma](), [jetpack](), [llms](), [performance](), [release]() ### AI overview Android Studio Quail 4 is a stable production release that preloads curated Android skills for its agent. The skills help LLMs handle Android-specific workflows such as upgrades, profiling, navigation, and adaptive UI work. ### Source excerpt Posted by Amman Fasil Asfaw, Product Manager, Android Studio Android Studio Quail 4 is now stable and ready for you to use in production. This is the final stable release for Android Studio Quail. The new features in Android Studio enable you to build premium apps with AI efficiently and effectively. Check out the video below to see the most helpful new features from the last 4 releases that can help improve and speed up your development. Here is a deep dive into what's new in Android Studio Quail 4: Android skills bundled into Android Studio While LLMs are incredibly capable at generic coding queries, they frequently write incorrect or outdated code when confronted with rapidly evolving Android APIs, platform-specific migrations, or complex configuration structures.To solve this, we bundle Android skills that have been curated by the team who builds Android, directly into Android Studio. Following the open-standard agent skills specification, these are modular, AI-optimized instructions designed specifically to guide LLMs through complex Android workflows. Android skills are now pre-loaded directly into the IDE, so you can start using them without having to manually download additional files. When you prompt the Android Studio agent, we analyze your prompt and search against the metadata for installed skills, automatically invoking them when they're most relevant. Your agent gains instant domain expertise, applying Google's best practices with less overhead spent on long, manual setup prompts. Android Studio comes preloaded with 23 curated skills, including: Need help upgrading your build? You have the Android Gradle Plugin (AGP) 9 Upgrade skill. Want to profile your app for any performance issues? You have the Android Profiler skill. Ready for a Jetpack Navigation framework upgrade? You have the Navigation3 skill. Adapting your app UI to different Android devices? You have the Adaptive skill. We also encourage you to create your own custom skills to extend Agent M ## Inside Android Skills - Built for deprecation DevFeed: [Inside Android Skills - Built for deprecation]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-08-06T16:00:00Z Content type: article Language: en Sources: [Android Developers Blog](), [Android Developers Blog]() Topics: [Android skills](), [Android](), [Large Language Model](), [Command-line interface]() Tags: [ai-assisted-coding](), [android](), [android-skills](), [cli](), [developer](), [evals](), [gemini](), [llm](), [skills]() ### AI overview This article explains the philosophy behind Android Skills: they target specific, fast-moving Android topics where state-of-the-art models have verifiable knowledge gaps. It discusses token costs, evaluation requirements, model and agent compatibility, and the role of Android documentation and Android CLI. ### Source excerpt Posted by Jose Alcérreca, Developer Relations Engineer, Android Developer Relations We released the official Android Skills in April, and the response surpassed all our expectations. In this blog post, I'll address some of the feedback we received, explaining the philosophy and methodology behind the project. Hopefully, this will also help you understand what happens behind the scenes when you install and use skills, allowing you to make better use of tokens and your own time. Why are there so few official skills? Currently, we only consider new skills when there's a verifiable knowledge gap in state-of-the-art (SOTA) models. Put simply: you don't need to teach the model what it already knows. (Though there are a few exceptions--read on!) We've released around 20 official skills so far, and they intentionally target highly specific, fast-moving areas that standard models aren't fully grounded on yet--things like AGP 9, Navigation 3, advanced Camera APIs, and Perfetto SQL. What about core, more general, skills? Every installed skill injects 100-200 tokens into the baseline context of every task you start. If that skill actually activates, that count can quickly jump into the thousands. In most cases, hoarding basic skills is both counterproductive and expensive. Before installing a skill for writing basic Kotlin or Compose, consider if your LLM of choice really needs it, or if it knows those topics well enough already. Evaluating skills Before their release, each skill is tested against a comprehensive set of evals that prove that the skill delivers clear value. These evals should pass when the skill is active, and fail otherwise. Evals are to skills what integration tests are to code. timeout_s: 1200 repository: url: [redacted - internal git repo] working_dir: wear_compose_m3_empty_app category_ids: - wear prompt: |- Add a horizontal pager to MainActivity.kt. Have three pages in the pager. Each page should contain the text "Page 1", "Page 2", and "Page 3" respectively ## Android Skills: Philosophy, Selection Criteria, and Evaluation Methodology DevFeed: [Android Skills: Philosophy, Selection Criteria, and Evaluation Methodology]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-08-06T16:00:00Z Content type: opinion Language: en Sources: [Android Developers Blog]() Topics: [Android skills](), [Large Language Model](), [Android Studio](), [Command-line interface]() Tags: [agent](), [agents](), [ai-assisted-coding](), [android-skills](), [android-studio](), [antigravity](), [cli](), [docs](), [gemini]() ### AI overview Android Developer Relations explains how official Android Skills are selected, focusing on verifiable knowledge gaps in state-of-the-art models and fast-moving topics. The article also discusses context costs, evaluation requirements, model compatibility, and use of the Android Knowledge Base. ### Source excerpt Posted by Jose Alcérreca, Developer Relations Engineer, Android Developer Relations We released the official Android Skills in April, and the response surpassed all our expectations. In this blog post, I'll address some of the feedback we received, explaining the philosophy and methodology behind the project. Hopefully, this will also help you understand what happens behind the scenes when you install and use skills, allowing you to make better use of tokens and your own time. Why are there so few official skills? Currently, we only consider new skills when there's a verifiable knowledge gap in state-of-the-art (SOTA) models. Put simply: you don't need to teach the model what it already knows. (Though there are a few exceptions--read on!) We've released around 20 official skills so far, and they intentionally target highly specific, fast-moving areas that standard models aren't fully grounded on yet--things like AGP 9, Navigation 3, advanced Camera APIs, and Perfetto SQL. What about core, more general, skills? Every installed skill injects 100-200 tokens into the baseline context of every task you start. If that skill actually activates, that count can quickly jump into the thousands. In most cases, hoarding basic skills is both counterproductive and expensive. Before installing a skill for writing basic Kotlin or Compose, consider if your LLM of choice really needs it, or if it knows those topics well enough already. Evaluating skills Before their release, each skill is tested against a comprehensive set of evals that prove that the skill delivers clear value. These evals should pass when the skill is active, and fail otherwise. Evals are to skills what integration tests are to code. timeout_s: 1200 repository: url: [redacted - internal git repo] working_dir: wear_compose_m3_empty_app category_ids: - wear prompt: |- Add a horizontal pager to MainActivity.kt. Have three pages in the pager. Each page should contain the text "Page 1", "Page 2", and "Page 3" respectively ## Maestro MCP for AI Coding Agents DevFeed: [Maestro MCP for AI Coding Agents]() Original publisher: [Read original article]() Author: Ashish Kharche Published: 2026-07-06T00:00:00Z Content type: tutorial Language: en Sources: [mobile.dev - Medium]() Topics: [Model Context Protocol (MCP)](), [AI-assisted coding](), [Android skills](), [Claude Code](), [cursor](), [codex](), [React Native](), [Android](), [Flutter](), [iOS](), [Web](), [Command-line interface]() Tags: [ai-coding-agents](), [android](), [claude-code](), [cli](), [code](), [codex](), [coding-agents](), [cursor](), [emulator](), [flutter](), [ios](), [maestro](), [mcp](), [simulator](), [ui](), [web]() ### AI overview Maestro MCP connects coding agents to live iOS and Android targets, allowing them to inspect screens, run UI flows, take screenshots, and report what happened within the coding conversation. The article explains setup with the Maestro CLI and MCP, and describes using Maestro Viewer for human observation. ### Source excerpt Give your coding agent a real device. With Maestro MCP, agents like Claude Code, Cursor, and Codex can operate your live app, run flows, and check the UI without leaving the chat. ## Android Skills: Expert Instructions for Your AI Agent DevFeed: [Android Skills: Expert Instructions for Your AI Agent]() Original publisher: [Read original article]() Author: blundell Published: 2026-04-24T22:10:46Z Content type: tutorial Language: en Sources: [Blundell]() Topics: [Android skills](), [Agent Skills](), [AI Agent](), [Claude Code](), [Mobile](), [GitHub]() Tags: [agent-skills](), [ai](), [ai-agent](), [android](), [android-development](), [android-skills](), [androiddev](), [claude-code](), [gemini](), [github](), [intermediate](), [reference](), [reference-ai-androiddev-claude-code-gemini](), [skills]() ### AI overview This tutorial explains Google's Android skills repository and the open agent-skills format, then shows how to make the skills available in Claude Code instead of using the repository's default Gemini CLI setup. ### Source excerpt Google just published a set of Android skills a collection of expert, AI-optimised instructions covering the most common and painful Android development workflows. (Upgrading to AGP 9, migrating XML layouts to Compose, wiring up Navigation 3.) AI automated step-by-step procedures grounded in official Android best practices. The GitHub repo defaults to wiring them into Gemini [...] The post Android Skills: Expert Instructions for Your AI Agent first appeared on Blundell. ## Android CLI meet Gemini CLI DevFeed: [Android CLI meet Gemini CLI]() Original publisher: [Read original article]() Published: 2026-04-18T19:20:59Z Content type: tutorial Language: en Sources: [maiatoday]() Topics: [Command-line interface](), [Android skills](), [Emulator](), [Testing](), [Android Studio]() Tags: [agent](), [ai-agents](), [android](), [android-studio](), [cli](), [emulator](), [gemini-cli](), [testing]() ### AI overview This tutorial introduces Android CLI and matching agent skills for Gemini CLI and other agents. It covers installation, initialization, Android knowledge-base access, emulator interaction, and testing workflows with screenshots and interactions. ### Source excerpt In recent Android AI experiments I always felt I missed something. If I was in the IDE using an agent with Gemini in Android Studio, I loved the access to the knowledge base and the special tools but I could only spin up one instance. If I was in my let's say gemini-cli, I missed the quality of life improvements that made me have to jump back into the IDE. Yes I could install an adb extension into gemini-cli or write some custom skill to try to achieve what I wanted or paste a bunch of Android official urls into the prompt. It was always fiddly to open the IDE from a worktree or test from an orchestrator session. As of last week there is a new tool and a bunch of matching skills that solve this. Android CLI NOTE I see 2 main benefits of using android-cli easy access to accurate android knowledge base that is agent friendly and will reduce token use easy emulator interaction and improved testing and iteration with screenshots and interactions How do I get it The installation is pretty simple, follow the Android cli docs TLDR; Download and install make sure you are using the new android executable not the old one, type which android and you are looking for something like /usr/local/bin/android If not then check your path setup. Or ask an agent to do it with this prompt. read the file that sets up my path and modify it such that the android sdk paths are not added in the the beginning but at the end of the path. I need this because the path `/usr/local/bin` is in the path before the android sdk and it needs to be first run android init to make sure all agents on your system will use this tool. It sets up a skill for claude, gemini and more optionally install all the skills created for this tool android skills add --all Show me some cools things I can do with it I am going to use a repo with a demo app I built with Gemini in Android Studio to take it for a spin. This app is a simple mood tracking app. You don't need to try it on my app, ## Making AI Write Android Code Our Way: A Practical Guide to Agent Skills DevFeed: [Making AI Write Android Code Our Way: A Practical Guide to Agent Skills]() Original publisher: [Read original article]() Author: Pierrick CAEN Published: 2026-03-17T08:25:04Z Content type: tutorial Language: en Sources: [Medium]() Topics: [Android skills](), [Agent Skills](), [AI Agent](), [cursor](), [Kotlin](), [Jetpack Compose](), [GraphQL]() Tags: [agent](), [agent-skills](), [ai](), [ai-coding](), [android](), [coding](), [cursor](), [graphql](), [jetpack-compose](), [kotlin](), [skills](), [software-engineering]() ### AI overview A Medium Android team describes how it uses AGENTS.md and reusable agent skills to encode project architecture, coding conventions, testing practices, and workflows for AI-assisted Kotlin development. The article focuses on making generated code follow the team's established patterns. ### Source excerpt Generated by DALL-E Turning knowledge into reusable AI agent instructions for a small, fast-moving team. We're a small Android team at Medium, just a handful of engineers maintaining and evolving the Medium Android app. Our codebase follows Clean Architecture with Kotlin, Jetpack Compose, Hilt, Apollo GraphQL, and a growing number of feature modules. Like most Android teams, we have strong opinions about how code should be structured: where ViewModels get their data, how analytics events flow, how feature flags are checked, what a "new screen" looks like from Fragment to preview function. The problem? Those opinions lived in PR review comments, Slack threads, and the heads of engineers who'd been around long enough to know the patterns. When AI coding assistants arrived, they could generate Kotlin code but not our Kotlin code. The output was generic. It missed our conventions, our component library, our testing style. Six months ago we started using Cursor as our companion IDE. What changed the game wasn't Cursor itself, it was skills and AGENTS.md: a way to encode our team's playbook so the AI follows it every time. This post walks through what we built, how we structured it, and what impact it's had. The Foundation: AGENTS.md as Project Context Before skills, we wrote an AGENTS.md file at the root of our Android project. Think of it as a README for the AI, a document that's automatically loaded into context whenever any Agents works on our code. Our AGENTS.md covers: Architecture overview: Module structure (data, domain, design, feature modules), layer responsibilities Key patterns: How we do dependency injection (Hilt), state management (StateFlow + SharedFlow), navigation (centralized Router), repository pattern (Apollo + Result) Conventions: Compose best practices, ViewModel patterns, testing strategy Common commands: Gradle tasks for building, testing, and running Detekt This gives Agent baseline awareness of our project. When it generates a ViewModel, it a