| name | multi-platform-architect |
| description | Multi-Platform AI Ecosystem Architect - Master skill for creating ecosystem components across Claude Code, Codex, Auggie, Kilocode, Goose, Gemini-CLI |
| keywords | ["architecture","meta-creation","workflow-design","ecosystem-building","component-generation","multi-platform"] |
Multi-Platform AI Ecosystem Architect
What This Skill Does
Transforms any AI CLI into a master architect capable of designing and implementing complete ecosystem solutions. Automatically detects the current platform, analyzes requirements, recommends optimal component combinations, and generates production-ready components tailored to each platform's specific architecture and conventions.
Supported Platforms
1. Claude Code
- Directory:
.claude/
- Components: Skills, Commands, Hooks, Agents, Plugins
- Triggers:
/command, @agent, automatic detection
- Resource: Token-based optimization
2. Codex
- Directory:
.codex/ or .vscode/codex/
- Components: Workspace config, Commands, Scripts, Extensions
- Triggers:
!command, workspace automation
- Resource: Context-based optimization
3. Auggie
- Directory:
.auggie/
- Components: Agents, Workflows, Integrations, Modules
- Triggers:
agent:, workflow automation
- Resource: Task-based optimization
4. Kilocode
- Directory:
.kilocode/
- Components: Tasks, Workflows, Tools, Configurations
- Triggers:
task:, automation workflows
- Resource: Process-based optimization
5. Goose
- Directory:
.goose/
- Components: Agents, Commands, Modules, Extensions
- Triggers:
goose:, command modules
- Resource: Memory-based optimization
6. Gemini-CLI
- Directory:
.gemini/
- Components: Contexts, Commands, Workflows, Prompts
- Triggers:
gemini:, context loading
- Resource: Context-based optimization
Core Capabilities
1. Automatic Platform Detection
- Scans for platform-specific directories and files
- Detects command patterns and syntax
- Identifies platform capabilities and limitations
- Falls back to generic templates for unknown platforms
2. Multi-Platform Component Generation
- Generates platform-appropriate file structures
- Adapts component types to each platform's conventions
- Creates platform-specific configuration schemas
- Handles different resource models (tokens vs context)
3. Cross-Platform Integration
- Designs components that work across multiple platforms
- Creates migration guides between platforms
- Establishes interoperability patterns
- Handles platform-specific limitations gracefully
Platform Detection Logic
Directory-Based Detection
def detect_platform():
platform_checks = [
('claude-code', ['.claude/']),
('codex', ['.codex/', '.vscode/codex/']),
('auggie', ['.auggie/']),
('kilocode', ['.kilocode/']),
('goose', ['.goose/']),
('gemini-cli', ['.gemini/'])
]
for platform, paths in platform_checks:
if any(os.path.exists(path) for path in paths):
return platform
return 'generic'
Command Pattern Detection
- Claude Code:
/command, @agent delegation
- Codex:
!command, workspace commands
- Auggie:
agent:, workflow triggers
- Kilocode:
task:, automation definitions
- Goose:
goose:, module invocations
- Gemini-CLI:
gemini:, context loading
When This Skill Auto-Triggers
The AI will automatically use this skill when detecting requests for:
- "create a workflow for [platform]"
- "build a [component type] for [platform]"
- "design [component] that works across multiple platforms"
- "automate this process for [platform]"
- "set up project structure for [platform]"
- "optimize my [platform] setup"
- "create templates for [platform]"
- "generate cross-platform components"
- "build ecosystem components"
- "design custom commands for [platform]"
- "create automation for [platform]"
Multi-Platform Component Generation
Platform Analysis Process
Analysis Flow:
1. Detect current platform
2. Analyze user requirements
3. Map requirements to platform-specific components
4. Generate optimized component architecture
5. Create platform-appropriate file structures
Component Mapping Matrix
| Requirement | Claude Code | Codex | Auggie | Kilocode | Goose | Gemini-CLI |
|---|
| Automation | SKILL.md | Script | Agent | Task | Module | Workflow |
| User Control | Command | Command | Workflow | Config | Command | Command |
| Validation | Hook | Extension | Integration | Tool | Extension | Context |
| Specialization | Agent | Extension | Agent | Tool | Agent | Prompt |
Template Adaptation Patterns
1. Directory Structure Adaptation
Template Variables:
- PLATFORM: Detected platform
- BASE_DIR: Platform-specific directory
- FILE_EXT: Platform-appropriate file extension
- SYNTAX: Platform-specific syntax (YAML, JSON, etc.)
2. Resource Budget Optimization
Resource Models:
- Claude Code: Token-based (30-600 tokens)
- Codex: Context-based (100-400 units)
- Auggie: Task-based (50-300 units)
- Kilocode: Process-based (100-500 units)
- Goose: Memory-based (100-512MB)
- Gemini-CLI: Context-based (200-2048 tokens)
3. Trigger Pattern Adaptation
Trigger Adaptation:
- Claude Code: /command, @agent, automatic
- Codex: !command, workspace, automation
- Auggie: agent:, workflow, automation
- Kilocode: task:, automation, workflow
- Goose: goose:, command, module
- Gemini-CLI: gemini:, context, workflow
Cross-Platform Workflow Examples
Example 1: Testing Workflow
User Input: "Create a testing workflow that works in Claude Code and Goose"
Generated Solution:
- Claude Code Version:
- Skill:
testing-automation (auto-trigger)
- Command:
/run-tests [type] (manual)
- Hook:
pre-commit-test-validation (blocking)
- Goose Version:
- Agent:
testing-agent (task specialist)
- Module:
test-runner.py (execution)
- Command:
goose:test (invocation)
Example 2: Documentation System
User Input: "Build documentation generation for Codex and Gemini-CLI"
Generated Solution:
- Codex Version:
- Command:
!generate-docs (workspace automation)
- Script:
doc-generator.py (generation logic)
- Extension:
docs-viewer.js (display)
- Gemini-CLI Version:
- Context:
documentation-expert (specialist)
- Workflow:
doc-generation.yaml (process)
- Prompt:
doc-template.txt (format)
Example 3: Deployment Pipeline
User Input: "Create deployment automation for Auggie and Kilocode"
Generated Solution:
- Auggie Version:
- Agent:
deployment-manager (orchestration)
- Workflow:
deploy-pipeline.yaml (process)
- Integration:
k8s-deploy.py (infrastructure)
- Kilocode Version:
- Task:
deploy-app (deployment action)
- Workflow:
ci-cd-pipeline.yaml (process)
- Tool:
infrastructure.json (config)
Platform-Specific Best Practices
Claude Code
- Focus on token efficiency and progressive loading
- Use slash commands for user-controlled workflows
- Implement hooks for quality gates and validation
- Leverage subagents for specialized tasks
Codex
- Integrate with Visual Studio ecosystem
- Use workspace configurations for team settings
- Leverage extensions for IDE integration
- Focus on automation within development workflows
Auggie
- Design agent-based architectures
- Use YAML configurations for declarative workflows
- Implement modular integration patterns
- Focus on task automation and orchestration
Kilocode
- Create task-based automation systems
- Use JSON configurations for structured data
- Implement workflow orchestration patterns
- Focus on process automation and tool integration
Goose
- Design modular agent systems
- Use Python modules for extensibility
- Implement memory-efficient workflows
- Focus on command-based interactions
Gemini-CLI
- Create context-based expertise systems
- Use YAML for structured prompts
- Implement workflow-based automation
- Focus on conversation and context management
Cross-Platform Migration
Migration Patterns
Claude Code โ Codex:
SKILL.md โ workspace.json + script.py
/command โ !command + extension.js
@agent โ agent.yaml + module.py
Codex โ Claude Code:
workspace.json โ AGENTS.md
!command โ /command
extension.js โ SKILL.md
Generic Template:
Adaptable to any platform with minimal modifications
Platform-agnostic file structures
Universal component patterns
Interoperability Strategies
- Adapter Components: Create wrapper components that bridge platforms
- Shared Configurations: Use common configuration formats (YAML, JSON)
- Standard Interfaces: Implement consistent API patterns
- Migration Tools: Generate platform-specific migration scripts
Platform Detection and Adaptation
Auto-Detection Algorithm
def detect_and_adapt(user_input):
platform = detect_platform()
requirements = analyze_requirements(user_input)
adapted_specs = adapt_to_platform(requirements, platform)
components = generate_components(adapted_specs)
return components
Constraint Handling
Platform Constraints:
claude-code:
- Token limits
- Context window size
- MCP integration
codex:
- Workspace limitations
- IDE integration
- Extension compatibility
auggie:
- Task isolation
- Workflow complexity
- Memory usage
kilocode:
- Process coordination
- Tool availability
- Configuration complexity
goose:
- Memory constraints
- Module loading
- Extension system
gemini-cli:
- Context length
- Model limitations
- Prompt management
Advanced Features
1. Platform-Hopping Workflows
Create workflows that span multiple platforms, using each platform's strengths:
- Claude Code: Complex reasoning and token optimization
- Codex: IDE integration and development workflows
- Goose: Memory-intensive processing
- Gemini-CLI: Context management and conversation
2. Hybrid Component Systems
Design components that combine capabilities from multiple platforms:
- Shared Configuration: Common configs stored in neutral formats
- Bridge Components: Adapters that enable inter-platform communication
- Fallback Mechanisms: Alternative approaches for platform failures
3. Progressive Enhancement
Build solutions that start simple and add complexity as needed:
- Basic: Generic templates that work everywhere
- Intermediate: Platform-specific optimizations
- Advanced: Full platform-native capabilities
Usage Examples
Cross-Platform Testing
User Input: "I need a testing solution that works in both Claude Code and Auggie"
Generated Solution:
- Analysis: Detect current platform, generate appropriate components
- Claude Code Version:
- Skill:
universal-testing (auto-triggering)
- Command:
/test-everything (manual)
- Hook:
validate-quality (blocking)
- Auggie Version:
- Agent:
testing-coordinator (orchestration)
- Workflow:
testing-pipeline (process)
- Module:
quality-checker (validation)
Multi-Platform Documentation
User Input: "Create documentation that works in Codex, Goose, and Gemini-CLI"
Generated Solution:
- Codex Components:
- Workspace config for VS Code integration
- JavaScript extension for display
- Automated script for generation
- Goose Components:
- Agent for content processing
- Python module for generation logic
- Command for manual invocation
- Gemini-CLI Components:
- Context specialist for content expertise
- Workflow for generation process
- Prompts for formatting templates
Platform Migration Assistant
User Input: "Help me migrate my Claude Code setup to work with Goose"
Generated Solution:
- Analysis Tool: Scripts to analyze existing Claude Code setup
- Migration Generator: Automatic component conversion
- Bridge Components: Adapters to maintain functionality
- Validation System: Testing frameworks to ensure compatibility
- Documentation: Migration guides and best practices
Platform-Specific Troubleshooting
Common Issues and Solutions
Claude Code
- Token Exhaustion: Implement progressive loading and caching
- Context Window Issues: Use subagents for isolation
- MCP Integration: Configure external tool access
Codex
- Extension Conflicts: Manage extension compatibility
- Workspace Issues: Resolve VS Code integration problems
- Automation Limitations: Work within IDE constraints
Auggie
- Agent Isolation: Handle context separation properly
- Workflow Complexity: Break down complex workflows
- Memory Management: Optimize task resource usage
Kilocode
- Process Coordination: Handle task dependencies
- Tool Integration: Ensure tool compatibility
- Configuration Complexity: Simplify setup processes
Goose
- Memory Constraints: Optimize for memory efficiency
- Module Loading: Handle dynamic module imports
- Extension Conflicts: Resolve dependency issues
Gemini-CLI
- Context Limits: Manage conversation length
- Model Constraints: Work within model capabilities
- Prompt Management: Optimize prompt efficiency
Future Platform Support
Extensible Architecture
The skill is designed to easily support new platforms:
- Add Platform Detection: Extend platform checks
- Create Templates: Build platform-specific templates
- Define Components: Specify component types and structures
- Implement Patterns: Create platform-specific patterns
Community Contributions
Users can contribute:
- Platform Templates: Create templates for new platforms
- Component Types: Define new component categories
- Integration Patterns: Document best practices
- Migration Guides: Share platform migration experiences
This multi-platform architect skill transforms any AI CLI into a comprehensive ecosystem builder, capable of generating optimized components for any supported platform while maintaining cross-platform compatibility and interoperability.
