Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/nphausg/ai-agent-skills/android_clean_architecturegit clone --depth 1 https://github.com/nphausg/ai-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/nphausg/ai-agent-skills/android_clean_architecture)<a href="https://agentmods.dev/rules/nphausg/ai-agent-skills/android_clean_architecture"><img src="https://agentmods.dev/badge/rules/nphausg/ai-agent-skills/android_clean_architecture.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00122 | $0.00122 |
| Opus 5 | $0.00061 | $0.00061 |
| Sonnet 5 | $0.00024 | $0.00024 |
| Haiku 4.5 | $0.00012 | $0.00012 |
Grade A, and why
android_clean_architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
- Follow domain/data/presentation separation: domain is business logic, data is data sources, presentation is UI.
- Keep domain layer free of Android framework dependencies.
- Use UseCases (Interactors) for business rules, triggered from ViewModels or Controllers.
- Repositories should expose interfaces, not implementations.
- Avoid directly accessing data sources in presentation layer.
- Prefer dependency inversion: inject interfaces rather than concrete classes.
- Maintain simple, testable domain models.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 14 lines · 122 tokens per session scan A 9327438c05a2
android_clean_architecture is a cursor rule published in the GitHub repository nphausg/ai-agent-skills (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 122 tokens to every session, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other cursor rules, from other repositories
swift-observable
Migration guide for updating SwiftUI apps from ObservableObject to the @Observable macro.
modern-swift
Write idiomatic SwiftUI code following Apple's latest architectural recommendations and best practices.
flutter-riverpod-cursorrules-prompt-file
Cursor rules for Flutter Riverpod.
flutter-app-expert-cursorrules-prompt-file
Cursor rules for Flutter development with expert integration.
nativescript
NativeScript best practices and patterns for mobile applications.
flutter-development-guidelines-cursorrules-prompt-file
Cursor rules for Flutter development with MVVM architecture, Riverpod state management, Material widgets, and Dart style guidelines.