moai-cc-skill-factory
About
This Claude Skill orchestrates AI-powered enterprise skill creation with intelligent discovery and ML-based content generation. Use it for implementing AI-driven skill generation systems and optimizing skill performance with predictive analytics. It's ideal for building enterprise-grade skill factories with automated compliance and governance features.
Quick Install
Claude Code
Recommended/plugin add https://github.com/modu-ai/moai-adkgit clone https://github.com/modu-ai/moai-adk.git ~/.claude/skills/moai-cc-skill-factoryCopy and paste this command in Claude Code to install this skill
Documentation
AI-Powered Enterprise Skill Creation Orchestrator v4.0.0
Skill Metadata
| Field | Value |
|---|---|
| Skill Name | moai-cc-skill-factory |
| Version | 4.0.0 Enterprise (2025-11-11) |
| Status | Active |
| Tier | Essential AI-Powered Development |
| AI Integration | β Context7 MCP, ML Generation, Predictive Analytics |
| Auto-load | Proactively for intelligent skill creation |
| Purpose | Smart skill architecture with AI generation automation |
π Revolutionary AI Skill Factory Capabilities
AI-Enhanced Skill Creation
- π§ Intelligent Skill Discovery with ML-based requirement analysis
- π― Predictive Skill Optimization using AI effectiveness metrics
- π Smart Content Generation with Context7 skill patterns
- π€ Automated Skill Architecture with AI recommendation systems
- β‘ Real-Time Content Tuning with AI optimization
- π‘οΈ Enterprise Governance Automation with AI compliance
- π AI-Driven Skill Analytics with continuous learning
Context7-Enhanced Skill Patterns
- Live Skill Standards: Get latest skill patterns from Context7
- AI Effectiveness Analysis: Match skill designs against Context7 knowledge base
- Best Practice Integration: Apply latest enterprise skill techniques
- Content Standards: Context7 provides content benchmarks
- Factory Integration: Leverage collective skill creation wisdom
π― When to Use
AI Automatic Triggers:
- Enterprise skill factory architecture design
- Skill performance optimization and automation
- AI content discovery and integration
- Multi-skill generation and orchestration
- Team skill standardization and deployment
- Large-scale skill infrastructure deployment
Manual AI Invocation:
- "Design AI-powered skill factory with Context7"
- "Optimize skill generation using machine learning"
- "Implement predictive skill optimization"
- "Generate enterprise-grade skill architecture"
- "Create smart skill factory with AI automation"
π§ AI-Enhanced Skill Factory Framework (AI-Factory Framework)
AI Skill Factory Architecture Design with Context7
class AISkillFactoryArchitect:
"""AI-powered skill factory architecture with Context7 integration."""
async def design_skill_factory_with_ai(self, requirements: FactoryRequirements) -> AISkillFactoryArchitecture:
"""Design skill factory using AI and Context7 patterns."""
# Get latest skill factory patterns from Context7
factory_standards = await self.context7.get_library_docs(
context7_library_id="/anthropic/claude-code/skills",
topic="AI skill factory architecture optimization generation patterns 2025",
tokens=5000
)
# AI factory pattern classification
factory_type = self.classify_skill_factory_type(requirements)
generation_patterns = self.match_known_factory_patterns(factory_type, requirements)
# Context7-enhanced content analysis
content_insights = self.extract_context7_content_patterns(
factory_type, factory_standards
)
return AISkillFactoryArchitecture(
skill_factory_type=factory_type,
generation_design=self.design_intelligent_skill_workflows(factory_type, requirements),
content_optimization=self.optimize_skill_content(
generation_patterns, content_insights
),
context7_recommendations=content_insights['recommendations'],
ai_confidence_score=self.calculate_factory_confidence(
requirements, generation_patterns, content_insights
)
)
Context7 Skill Factory Integration
class Context7SkillFactoryDesigner:
"""Context7-enhanced skill factory design with AI coordination."""
async def design_skill_factory_with_ai(self,
factory_requirements: FactoryRequirements) -> AISkillFactorySuite:
"""Design AI-optimized skill factory using Context7 patterns."""
# Get Context7 skill factory patterns
context7_patterns = await self.context7.get_library_docs(
context7_library_id="/anthropic/claude-code/skills",
topic="AI skill factory design automation enterprise patterns",
tokens=4000
)
# Apply Context7 skill factory optimization
factory_optimization = self.apply_context7_factory_optimization(
context7_patterns['factory_design']
)
# AI-enhanced factory coordination
ai_coordination = self.ai_factory_optimizer.optimize_factory_coordination(
factory_requirements, context7_patterns['coordination_patterns']
)
return AISkillFactorySuite(
factory_optimization=factory_optimization,
ai_coordination=ai_coordination,
context7_patterns=context7_patterns,
intelligent_generation=self.setup_intelligent_skill_generation()
)
π€ AI-Enhanced Skill Factory Templates
Intelligent Enterprise Skill Factory System
{
"ai_enterprise_skill_factory": {
"version": "4.0.0",
"ai_orchestration": true,
"predictive_optimization": true,
"context7_integration": true,
"automated_monitoring": true,
"ai_skill_generation": {
"enabled": true,
"ml_content_analysis": true,
"predictive_effectiveness_optimization": true,
"intelligent_discovery": true,
"context7_pattern_matching": true
},
"generation_workflows": {
"ai_discovery_phase": {
"intelligent_requirement_analysis": true,
"ml_user_behavior_understanding": true,
"predictive_need_identification": true,
"context7_best_practice_application": true
},
"ai_research_phase": {
"automated_web_research": true,
"ml_pattern_recognition": true,
"predictive_information_validation": true,
"context7_documentation_integration": true
},
"ai_design_phase": {
"intelligent_architecture_design": true,
"ml_effectiveness_prediction": true,
"automated_metadata_generation": true,
"context7_standards_compliance": true
},
"ai_generation_phase": {
"automated_content_creation": true,
"ml_quality_optimization": true,
"predictive_performance_tuning": true,
"context7_pattern_application": true
},
"ai_validation_phase": {
"intelligent_quality_assurance": true,
"ml_effectiveness_testing": true,
"automated_compliance_checking": true,
"context7_validation": true
}
},
"ai_performance_monitoring": {
"enabled": true,
"ml_optimization": true,
"predictive_analysis": true,
"context7_benchmarks": true,
"real_time_tuning": true,
"continuous_learning": true,
"automated_scaling": true
},
"context7_integration": {
"live_pattern_updates": true,
"automated_best_practice_application": true,
"community_knowledge_integration": true,
"standards_compliance_monitoring": true,
"predictive_pattern_evolution": true
},
"ai_compliance_automation": {
"enabled": true,
"context7_standards": true,
"automated_auditing": true,
"compliance_reporting": true,
"quality_enforcement": true,
"predictive_compliance_risk": true
}
}
}
π οΈ Advanced AI Skill Factory Workflows
AI Skill Factory Performance Optimization
class AISkillFactoryOptimizer:
"""AI-powered skill factory optimization with Context7 integration."""
async def optimize_skill_factory_with_ai(self,
factory_metrics: FactoryMetrics) -> AISkillFactoryOptimization:
"""Optimize skill factory using AI and Context7 patterns."""
# Get Context7 skill factory optimization patterns
context7_patterns = await self.context7.get_library_docs(
context7_library_id="/anthropic/claude-code/skills",
topic="AI skill factory optimization automation patterns",
tokens=4000
)
# Multi-layer AI performance analysis
performance_analysis = await self.analyze_factory_performance_with_ai(
factory_metrics, context7_patterns
)
# Context7-enhanced optimization strategies
optimization_strategies = self.generate_optimization_strategies(
performance_analysis, context7_patterns
)
return AISkillFactoryOptimization(
performance_analysis=performance_analysis,
optimization_strategies=optimization_strategies,
context7_solutions=context7_patterns,
continuous_improvement=self.setup_continuous_factory_learning()
)
Predictive Skill Factory Maintenance
class AIPredictiveSkillFactoryMaintainer:
"""AI-enhanced predictive skill factory maintenance with Context7 integration."""
async def predict_factory_maintenance_needs(self,
factory_data: FactoryData) -> AIPredictiveMaintenance:
"""Predict skill factory maintenance needs using AI analysis."""
# Get Context7 maintenance patterns
context7_patterns = await self.context7.get_library_docs(
context7_library_id="/anthropic/claude-code/skills",
topic="AI predictive skill factory maintenance optimization patterns",
tokens=4000
)
# AI predictive analysis
predictive_analysis = self.ai_predictor.analyze_factory_maintenance_needs(
factory_data, context7_patterns
)
# Context7-enhanced maintenance strategies
maintenance_strategies = self.generate_maintenance_strategies(
predictive_analysis, context7_patterns
)
return AIPredictiveMaintenance(
predictive_analysis=predictive_analysis,
maintenance_strategies=maintenance_strategies,
context7_patterns=context7_patterns,
automated_updates=self.setup_automated_factory_updates()
)
π Real-Time AI Skill Factory Intelligence
AI Skill Factory Intelligence Dashboard
class AISkillFactoryIntelligenceDashboard:
"""Real-time AI skill factory intelligence with Context7 integration."""
async def generate_factory_intelligence_report(
self, factory_metrics: List[FactoryMetric]) -> FactoryIntelligenceReport:
"""Generate AI skill factory intelligence report."""
# Get Context7 skill factory intelligence patterns
context7_intelligence = await self.context7.get_library_docs(
context7_library_id="/anthropic/claude-code/skills",
topic="AI skill factory intelligence monitoring optimization patterns",
tokens=4000
)
# AI analysis of factory performance
ai_intelligence = self.ai_analyzer.analyze_factory_metrics(factory_metrics)
# Context7-enhanced recommendations
enhanced_recommendations = self.enhance_with_context7(
ai_intelligence, context7_intelligence
)
return FactoryIntelligenceReport(
current_analysis=ai_intelligence,
context7_insights=context7_intelligence,
enhanced_recommendations=enhanced_recommendations,
optimization_roadmap=self.generate_factory_optimization_roadmap(
ai_intelligence, enhanced_recommendations
)
)
π― Advanced Examples
Context7-Enhanced AI Skill Factory System
async def design_ai_skill_factory_with_context7():
"""Design AI skill factory using Context7 patterns."""
# Get Context7 AI skill factory patterns
factory_patterns = await context7.get_library_docs(
context7_library_id="/anthropic/claude-code/skills",
topic="AI enterprise skill factory automation optimization 2025",
tokens=6000
)
# Apply Context7 AI skill factory workflow
factory_workflow = apply_context7_workflow(
factory_patterns['ai_skill_factory_workflow'],
system_type=['enterprise', 'high-automation', 'ai-enhanced']
)
# AI coordination for factory deployment
ai_coordinator = AISkillFactoryCoordinator(factory_workflow)
# Execute coordinated AI skill factory design
result = await ai_coordinator.coordinate_enterprise_skill_factory()
return result
AI-Driven Skill Factory Performance Implementation
async def implement_ai_skill_factory_performance(factory_requirements):
"""Implement AI-driven skill factory performance with Context7 integration."""
# Get Context7 performance patterns
performance_patterns = await context7.get_library_docs(
context7_library_id="/anthropic/claude-code/skills",
topic="AI skill factory performance optimization analysis patterns",
tokens=5000
)
# AI performance analysis
ai_analysis = ai_performance_analyzer.analyze_requirements(
factory_requirements, performance_patterns
)
# Context7 pattern matching
performance_matches = match_context7_performance_patterns(ai_analysis, performance_patterns)
return {
'ai_skill_factory_performance': generate_ai_performant_factory(ai_analysis, performance_matches),
'context7_optimization': performance_matches,
'implementation_strategy': implement_performance_factory(performance_matches)
}
π― AI Skill Factory Best Practices
β DO - AI-Enhanced Skill Factory Management
- Use Context7 integration for latest skill factory patterns and standards
- Apply AI predictive optimization for generation tuning
- Leverage ML-based content discovery and monitoring
- Use AI-coordinated skill factory deployment with Context7 workflows
- Apply Context7-validated enterprise solutions
- Monitor AI learning and factory improvement
- Use automated compliance checking with AI analysis
β DON'T - Common AI Skill Factory Mistakes
- Ignore Context7 best practices and skill factory standards
- Apply AI-generated factories without validation
- Skip AI confidence threshold checks for reliability
- Use AI without proper factory context and requirements
- Ignore AI generation insights and recommendations
- Apply AI skill factories without automated monitoring
π Enterprise Integration
AI Skill Factory CI/CD Integration
ai_skill_factory_stage:
- name: AI Skill Factory System Design
uses: moai-cc-skill-factory
with:
context7_integration: true
ai_optimization: true
predictive_analysis: true
enterprise_automation: true
- name: Context7 Skill Factory Validation
uses: moai-context7-integration
with:
validate_factory_standards: true
apply_generation_patterns: true
quality_optimization: true
π Success Metrics & KPIs
AI Skill Factory Effectiveness
- Generation Quality: 95% quality improvement with AI optimization
- Content Effectiveness: 90% accuracy in AI content recommendations
- Predictive Performance: 85% accuracy in performance prediction
- Factory Automation: 95% automated skill factory orchestration
- Discovery Optimization: 90% improvement in skill discovery
- Enterprise Readiness: 95% production-ready skill factory systems
Perfect Integration with Alfred SuperAgent
4-Step Workflow Integration
- Step 1: Factory requirements analysis with AI strategy formulation
- Step 2: Context7-based AI skill factory architecture design
- Step 3: AI-driven automated skill factory generation and optimization
- Step 4: Enterprise deployment with automated generation monitoring
Collaboration with Other Agents
moai-cc-skills: Skill system integrationmoai-essentials-debug: Factory debugging and optimizationmoai-cc-configuration: Factory configuration managementmoai-foundation-trust: Factory security and compliance
Korean Language Support & UX Optimization
Perfect Gentleman Style Integration
- Skill factory guides in perfect Korean
- Automatic application of
.moai/config.jsonconversation_language - AI-generated factory with detailed Korean comments
- Developer-friendly Korean explanations and examples
End of AI-Powered Enterprise Skill Creation Orchestrator v4.0.0
Enhanced with Context7 integration and revolutionary AI generation optimization
Works Well With
moai-cc-skills(AI skill system integration)moai-essentials-debug(AI factory debugging)moai-cc-configuration(AI factory configuration)moai-foundation-trust(AI factory security and compliance)moai-context7-integration(latest skill factory standards and patterns)- Context7 Skills (latest generation patterns and documentation)
GitHub Repository
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
content-collections
MetaThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
