Oma Mobile logo

Oma Mobile

OrganizationPopular
first-fluke
oma-mobile

Implement Flutter, React Native, or native Swift apps. Use for mobile screens, widgets, platform integrations, and application data flows.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-mobile
Stars
1.3K
Forks
149
Bundled files
20
LicenseMIT
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.

  • 20 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma Mobile 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.
  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/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-mobile .claude/skills/oma-mobile
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma Mobile 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 Oma Mobile 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 Oma Mobile 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.

Mobile Agent - Cross-Platform Mobile Specialist

Scheduling

Goal

Build, modify, and verify cross-platform mobile application features with clean architecture, platform-appropriate UI, state management, performance, and E2E coverage.

Intent signature

  • User asks for mobile app, Flutter, Dart, React Native, iOS, Android, Riverpod, widgets, camera, GPS, push notifications, or offline-first work.
  • User needs native or cross-platform mobile behavior rather than web frontend work.

When to use

  • Building native mobile applications (iOS + Android)
  • Mobile-specific UI patterns
  • Platform features (camera, GPS, push notifications)
  • Offline-first architecture

When NOT to use

  • Web frontend -> use Frontend Agent
  • Backend APIs -> use Backend Agent

Expected inputs

  • Target screen, widget, feature, platform capability, or mobile flow
  • Existing app architecture, state management pattern, API contract, and platform constraints
  • Test expectations for unit, widget, integration, or Maestro E2E coverage

Expected outputs

  • Mobile code changes in domain, data, presentation, platform, or test files
  • UI aligned with Material Design 3 and iOS HIG as applicable
  • Verification results from mobile checks and critical-flow tests

Dependencies

  • Flutter/Dart or React Native toolchain as detected from the project
  • Riverpod/Bloc, Dio, platform SDKs, and Maestro where applicable
  • resources/execution-protocol.md, examples, snippets, checklist, and screen template

Control-flow features

  • Branches by platform, state management pattern, offline requirement, native permission, and test level
  • Reads and writes mobile codebase files
  • May call build, test, simulator, emulator, or E2E commands

Structural Flow

Entry

  1. Identify target platform(s), screen/feature, architecture layer, and state boundary.
  2. Inspect existing mobile patterns and dependencies.
  3. Determine test level and verification environment.

Scenes

  1. PREPARE: Load app architecture, platform constraints, and acceptance criteria.
  2. ACQUIRE: Read existing widgets/screens, providers/blocs, API clients, and tests.
  3. ACT: Implement mobile UI, state, platform integration, offline handling, and tests.
  4. VERIFY: Run relevant unit/widget/integration/E2E checks.
  5. FINALIZE: Report behavior, platforms covered, and verification results.

Transitions

  • If business logic is complex, keep it in domain/data layers before presentation.
  • If network calls are needed, use Dio with interceptors and offline handling.
  • If a critical user flow changes, add or update Maestro E2E coverage.
  • If backend contracts are missing, coordinate with backend/API work.

Failure and recovery

  • If platform SDK or emulator is unavailable, report verification limits.
  • If a permission or native capability is missing, add explicit platform configuration or document blocker.
  • If tests fail, fix before handoff or report the failing check.

Exit

  • Success: mobile feature works for target platforms and passes relevant checks.
  • Partial success: platform, simulator, dependency, or verification gaps are explicit.

Logical Operations

Actions

ActionSSL primitiveEvidence
Inspect mobile architectureREADDomain/data/presentation files
Select state and platform strategySELECTRiverpod/Bloc and platform constraints
Implement mobile codeWRITEWidgets, screens, providers, clients
Validate lifecycle and permissionsVALIDATEDispose, permissions, offline behavior
Call verification toolsCALL_TOOLTests, builds, Maestro
Report resultNOTIFYFinal summary

Tools and instruments

  • Flutter/Dart or React Native stack
  • Riverpod/Bloc, Dio, platform SDKs, Maestro
  • Unit, widget, integration, and E2E test commands

Canonical workflow path

bash
rg --files
rg "Riverpod|Bloc|Dio|Widget|Maestro|dispose\\(|permission" .

Then run the project's mobile verification commands, typically unit/widget tests and Maestro E2E for critical flows.

Resource scope

ScopeResource target
CODEBASEMobile source, tests, platform config
LOCAL_FSTemplates, snippets, resources
PROCESSBuild, test, emulator, simulator, E2E commands
NETWORKBackend APIs when the feature integrates remotely

Preconditions

  • Target mobile feature and platform scope are identifiable.
  • Required SDKs, permissions, and API contracts are available or assumptions are stated.

Effects and side effects

  • Mutates mobile source, tests, and platform configuration.
  • May affect permissions, app lifecycle, offline data, or performance.

Guardrails

Apply framework, library, architecture, and data-model defaults only when the target project has no established choice. Scoped edits do not authorize a stack migration or unrelated infrastructure.

  1. Clean Architecture: domain -> data -> presentation
  2. Riverpod/Bloc for state management (no raw setState for complex logic)
  3. Material Design 3 (Android) + iOS HIG (iOS)
  4. All controllers disposed in dispose() method
  5. Use the platform transport with auth/retry/logging interception and offline handling: Flutter uses Dio, React Native uses axios behind TanStack Query, and Swift uses generated Client middleware.
  6. 60fps target; test on both platforms
  7. Use Maestro for E2E testing of critical user flows
  8. Swift native: SwiftUI + @MainActor @Observable view models (Observation framework, iOS 17+) — non-isolated VMs mutating observed state from a Task are a Swift 6 strict-concurrency error
  9. Swift native: use the generated Client from swift-openapi-generator — never hand-roll URLRequest/JSONDecoder for API calls
  10. Swift native: cache API responses at the Repository layer via a ResponseCache actor over hyperoslo/Cache — cache DECODED models (never HTTPBody), serve stale-while-revalidate on reads, invalidate keys on writes; view models depend on a protocol seam, not the concrete service (see variants/swift-ios/snippets.md §10)
  11. Swift native: follow App/Core/Features/Shared project layout
  12. Swift native: iOS Human Interface Guidelines for all UI decisions
  13. Swift native: XCTest or Swift Testing for units, XCUITest for critical flows; cancel work via structured .task {} (auto-cancels on disappear) — never in deinit, which is nonisolated and cannot touch @MainActor state under Swift 6 (isolated deinit requires Swift 6.2+)
  14. Swift native: restore edge swipe-back at the route layer — nav-bar-hidden screens (.toolbar(.hidden, for: .navigationBar)) lose it, so register push routes via a swipeBackDestination wrapper, not per-screen (see variants/swift-ios/snippets.md §9)
  15. Flutter: mandate a repository-layer offline-first cache (Drift) — read cached entities then revalidate (stale-while-revalidate), invalidate/refresh affected rows on every write; cache decoded entities at the data layer, never at the Dio transport (see variants/flutter/snippets.md §3, §10)
  16. React Native: server state goes through TanStack Query (the repository-layer cache) with explicit staleTime/gcTime — invalidate affected query keys on every mutation, persist the cache to MMKV for offline; screens consume query/mutation hooks, never call axios directly (see variants/react-native/snippets.md)

References

  • Execution steps (follow for the selected task): resources/execution-protocol.md
  • Code snippets (Swift): variants/swift-ios/snippets.md
  • Code snippets (Flutter): variants/flutter/snippets.md
  • Code snippets (React Native): variants/react-native/snippets.md
  • Checklist (run before handoff): resources/checklist.md
  • Error recovery: resources/error-playbook.md
  • Tech stack index (all platforms): resources/tech-stack.md
  • Tech stack (Swift): variants/swift-ios/tech-stack.md
  • Tech stack (Flutter): variants/flutter/tech-stack.md
  • Tech stack (React Native): variants/react-native/tech-stack.md
  • Screen template (Flutter): resources/screen-template.dart
  • Screen template (Swift): resources/screen-template.swift
  • Screen template (React Native): resources/screen-template.tsx
  • API service template (Swift): variants/swift-ios/api-template.swift
  • API service template (Flutter): variants/flutter/api-template.dart
  • API service template (React Native): variants/react-native/api-template.ts
  • Variant registry: variants/README.md
  • Context loading: ../_shared/core/context-loading.md
  • Clarification: ../_shared/core/clarification-protocol.md
  • Context budget: ../_shared/core/context-budget.md
  • Lessons learned: ../_shared/core/lessons-learned.md (matching prior failure or requested retrospective)
  • Observability handoff: ../oma-observability/SKILL.md §Integrations — offline queuing, crash analytics, battery-aware sampling

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 Oma Mobile AI skill do?

Implement Flutter, React Native, or native Swift apps. Use for mobile screens, widgets, platform integrations, and application data flows.

Why use Oma Mobile on TypingMind?

Because you install it once and use it with any model. Oma Mobile 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 Oma Mobile in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-mobile. 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 Oma Mobile?

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 Oma Mobile?

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

Is the Oma Mobile AI skill free?

Yes. It is published on GitHub by first-fluke under the MIT 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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