Mobile Ci Cd
Mobile CI/CD automates building, testing, and deploying mobile applications. This guide covers GitHub Actions, Fastlane automation, code signing, and App Store submission automation for streamlining m
Best use case
Mobile Ci Cd is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Mobile CI/CD automates building, testing, and deploying mobile applications. This guide covers GitHub Actions, Fastlane automation, code signing, and App Store submission automation for streamlining m
Teams using Mobile Ci Cd should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/mobile-ci-cd/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Mobile Ci Cd Compares
| Feature / Agent | Mobile Ci Cd | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Mobile CI/CD automates building, testing, and deploying mobile applications. This guide covers GitHub Actions, Fastlane automation, code signing, and App Store submission automation for streamlining m
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Mobile Ci Cd
## Skill Profile
*(Select at least one profile to enable specific modules)*
- [ ] **DevOps**
- [x] **Backend**
- [ ] **Frontend**
- [ ] **AI-RAG**
- [ ] **Security Critical**
## Overview
Mobile CI/CD automates building, testing, and deploying mobile applications. This guide covers GitHub Actions, Fastlane automation, code signing, and App Store submission automation for streamlining mobile app development and release processes.
## Why This Matters
- **Consistency**: Automated builds produce consistent results
- **Speed**: Faster feedback cycles with automated testing
- **Quality**: Automated tests catch issues early
- **Reliability**: Automated deployments reduce human error
---
## Core Concepts & Rules
### 1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs
### 2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment
## Inputs / Outputs / Contracts
* **Inputs**:
- Source code repository
- Build configuration
- Signing credentials
- App store credentials
* **Entry Conditions**:
- GitHub repository configured
- Fastlane installed
- Developer accounts set up
* **Outputs**:
- Built app binaries (IPA/AAB)
- Test results
- Deployment status
- Release notes
* **Artifacts Required (Deliverables)**:
- CI/CD pipeline configuration
- Fastlane configuration
- Build scripts
- Deployment documentation
* **Acceptance Evidence**:
- Successful builds on main branch
- Tests pass in CI
- Automated deployments work
* **Success Criteria**:
- Build time < 30 minutes
- Test execution < 10 minutes
- Deployment success rate > 95%
## Skill Composition
* **Depends on**: [App Distribution](31-mobile-development/app-distribution/SKILL.md), [Testing](16-testing/)
* **Compatible with**: [Flutter Patterns](31-mobile-development/flutter-patterns/SKILL.md), [React Native Patterns](31-mobile-development/react-native-patterns/SKILL.md)
* **Conflicts with**: None
* **Related Skills**: [DevOps Infrastructure](15-devops-infrastructure/), [Release Engineering](59-release-engineering/)
---
## Quick Start / Implementation Example
1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions
```python
# Example implementation following best practices
def example_function():
# Your implementation here
pass
```
## Assumptions / Constraints / Non-goals
* **Assumptions**:
- Development environment is properly configured
- Required dependencies are available
- Team has basic understanding of domain
* **Constraints**:
- Must follow existing codebase conventions
- Time and resource limitations
- Compatibility requirements
* **Non-goals**:
- This skill does not cover edge cases outside scope
- Not a replacement for formal training
## Compatibility & Prerequisites
* **Supported Versions**:
- Python 3.8+
- Node.js 16+
- Modern browsers (Chrome, Firefox, Safari, Edge)
* **Required AI Tools**:
- Code editor (VS Code recommended)
- Testing framework appropriate for language
- Version control (Git)
* **Dependencies**:
- Language-specific package manager
- Build tools
- Testing libraries
* **Environment Setup**:
- `.env.example` keys: `API_KEY`, `DATABASE_URL` (no values)
## Test Scenario Matrix (QA Strategy)
| Type | Focus Area | Required Scenarios / Mocks |
| :--- | :--- | :--- |
| **Unit** | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage |
| **Integration** | DB / API | All external API calls or database connections must be mocked during unit tests |
| **E2E** | User Journey | Critical user flows to test |
| **Performance** | Latency / Load | Benchmark requirements |
| **Security** | Vuln / Auth | SAST/DAST or dependency audit |
| **Frontend** | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |
## Technical Guardrails & Security Threat Model
### 1. Security & Privacy (Threat Model)
* **Top Threats**: Injection attacks, authentication bypass, data exposure
- [ ] **Data Handling**: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
- [ ] **Secrets Management**: No hardcoded API keys. Use Env Vars/Secrets Manager
- [ ] **Authorization**: Validate user permissions before state changes
### 2. Performance & Resources
- [ ] **Execution Efficiency**: Consider time complexity for algorithms
- [ ] **Memory Management**: Use streams/pagination for large data
- [ ] **Resource Cleanup**: Close DB connections/file handlers in finally blocks
### 3. Architecture & Scalability
- [ ] **Design Pattern**: Follow SOLID principles, use Dependency Injection
- [ ] **Modularity**: Decouple logic from UI/Frameworks
### 4. Observability & Reliability
- [ ] **Logging Standards**: Structured JSON, include trace IDs `request_id`
- [ ] **Metrics**: Track `error_rate`, `latency`, `queue_depth`
- [ ] **Error Handling**: Standardized error codes, no bare except
- [ ] **Observability Artifacts**:
- **Log Fields**: timestamp, level, message, request_id
- **Metrics**: request_count, error_count, response_time
- **Dashboards/Alerts**: High Error Rate > 5%
## Agent Directives & Error Recovery
*(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)*
- **Thinking Process**: Analyze root cause before fixing. Do not brute-force.
- **Fallback Strategy**: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
- **Self-Review**: Check against Guardrails & Anti-patterns before finalizing.
- **Output Constraints**: Output ONLY the modified code block. Do not explain unless asked.
## Definition of Done (DoD) Checklist
- [ ] Tests passed + coverage met
- [ ] Lint/Typecheck passed
- [ ] Logging/Metrics/Trace implemented
- [ ] Security checks passed
- [ ] Documentation/Changelog updated
- [ ] Accessibility/Performance requirements met (if frontend)
## Anti-patterns / Pitfalls
* ⛔ **Don't**: Log PII, catch-all exception, N+1 queries
* ⚠️ **Watch out for**: Common symptoms and quick fixes
* 💡 **Instead**: Use proper error handling, pagination, and logging
## Reference Links & Examples
* Internal documentation and examples
* Official documentation and best practices
* Community resources and discussions
## Versioning & Changelog
* **Version**: 1.0.0
* **Changelog**:
- 2026-02-22: Initial version with complete template structureRelated Skills
adynato-mobile-api
API integration patterns for Adynato mobile apps. Covers data fetching with TanStack Query, authentication flows, offline support, error handling, and optimistic updates in React Native/Expo apps. Use when integrating APIs into mobile applications.
bgo
Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.
partner-revenue-desk
Operating model for tracking, attributing, and accelerating partner-sourced revenue.
parallel-data-enrichment
Structured company and entity data enrichment using Parallel AI Task API with core/base processors. Returns typed JSON output. No binary install — requires PARALLEL_API_KEY in .env.local.
parallel-agents
Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.
paper-writing-assistant
Assist in drafting research papers and meeting notes, enforcing academic rigor and formatting.
pandas-data-manipulation-rules
Focuses on pandas-specific rules for data manipulation, including method chaining, data selection using loc/iloc, and groupby operations.
pagent
Guide for using pagent - a PRD-to-code orchestration tool. Use when users ask how to use pagent, run agents, create PRDs, or transform requirements into code.
page-annotator
AI驱动的网页标注工具,支持高亮元素和添加文字批注。智能防重复、自动滚动、碰撞检测。兼容 GitHub 等严格 CSP 网站。适用场景:(1) 标记网页元素进行讲解 (2) 添加文字批注和注释 (3) 代码审查和设计评审 (4) 教学演示和用户引导 (5) Bug 报告和问题标记
package-json-modification-protection
Protects lines with the specific 'Do not touch this line Cursor' comment within package.json.
orchestrator
Multi-agent orchestrator that delegates all work to specialized subagents. Enforces parallelism, tracks progress, and coordinates agent teams for complex tasks.
orchestrator-conductor
This skill should be used when the user asks to "orchestrate agents", "run /orchestrate", "manage parallel agents", "coordinate multiple agents", "decompose this task", or needs patterns for multi-agent workflows with parallel execution and task decomposition.