# 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](<https://devfeed.tech/articles/making-ai-write-android-code-our-way-a-practical-guide-to-agent-skills-20323.md>)

Original publisher: [Read original article](<https://medium.engineering/making-ai-write-android-code-our-way-a-practical-guide-to-agent-skills-4e7b085d8e50?source=rss----2817475205d3---4>)

Author: Pierrick CAEN

Published: 2026-03-17T08:25:04Z

Content type: tutorial

Language: en

Sources: [Medium](<https://devfeed.tech/sources/medium.md>)

Topics: [Android skills](<https://devfeed.tech/topics/android-skills.md>), [Agent Skills](<https://devfeed.tech/topics/agent-skills.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [cursor](<https://devfeed.tech/topics/cursor.md>), [Kotlin](<https://devfeed.tech/topics/kotlin.md>), [Jetpack Compose](<https://devfeed.tech/topics/jetpack-compose.md>), [GraphQL](<https://devfeed.tech/topics/graphql.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agent-skills](<https://devfeed.tech/tags/agent-skills.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-coding](<https://devfeed.tech/tags/ai-coding.md>), [android](<https://devfeed.tech/tags/android.md>), [coding](<https://devfeed.tech/tags/coding.md>), [cursor](<https://devfeed.tech/tags/cursor.md>), [graphql](<https://devfeed.tech/tags/graphql.md>), [jetpack-compose](<https://devfeed.tech/tags/jetpack-compose.md>), [kotlin](<https://devfeed.tech/tags/kotlin.md>), [skills](<https://devfeed.tech/tags/skills.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>)

## 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<T>) 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