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Agent Skill · 开发

moai-system-universal(摩艾系统通用)

moai-system-universal 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

moai-system-universal 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 21,271。

总安装 192周安装 8GitHub Stars 公开资料未说明下载 64核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:moai-system-universal(摩艾系统通用)
来源仓库:https://github.com/rdmptv/adbautoplayer
仓库路径:skills/moai-system-universal
安装命令:
npx skills add https://github.com/rdmptv/adbautoplayer --skill moai-system-universal
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/rdmptv/adbautoplayer --skill moai-system-universal

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

moai-system-universal 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。

通过 npx skills add 命令从 adbautoplayer 仓库安装并使用。安装前建议确认权限范围和维护状态,注意是否会触发联网或文件读写操作。具体用法可结合来源仓库 README 进一步核验实际功能边界。

来源仓库内路径:skills/moai-system-universal

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/rdmptv/adbautoplayer --skill moai-system-unive…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

moai-system-universal 怎么安装?

在终端执行 npx skills add https://github.com/rdmptv/adbautoplayer --skill moai-system-universal。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

moai-system-universal 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

moai-system-universal 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

moai-system-universal 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

moai-system-universal 用的人多吗?

公开数据是累计安装 192 次,近一周安装 8 次,下载 64 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

moai-system-universal 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
moai-system-universal

摩艾系统通用 · 本页

192—需要联网MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

🚀 MoAI Universal Ultimate: The Complete Development Intelligence Platform


Quick Reference (30 seconds)

The Ultimate Development Intelligence Platform - AI-powered orchestration across the entire technology stack from concept to production with enterprise-grade security and compliance built-in.

Revolutionary Capabilities:

  • 🧠 Universal AI Intelligence: Understands relationships across all technology domains
  • 🔄 End-to-End Orchestration: SPEC → Implementation → Testing → Deployment → Security → Compliance
  • ⚡ Predictive Technology Roadmapping: AI recommends optimal stacks and migration paths
  • 🔒 Enterprise Security by Design: Zero-trust architecture with automated validation
  • 📊 Complete Compliance Automation: OWASP, SOC 2, ISO 27001, GDPR all automated
  • 🌐 Multi-Provider Intelligence: Seamless integration across any technology stack

Core Intelligence Matrix:

┌─────────────────┬─────────────────┬─────────────────┬─────────────────┐
│   Languages     │      BaaS       │  Development    │    Security     │
├─────────────────┼─────────────────┼─────────────────┼─────────────────┤
│ Python 3.13     │ Auth0           │ Debug + Fix     │ OWASP Top 10    │
│ TypeScript 5.9  │ Clerk           │ Refactor        │ Zero Trust      │
│ Go 1.23         │ Supabase        │ Performance     │ Threat Modeling │
│ Rust 1.91       │ Firebase        │ Review          │ Cryptography    │
│ Java 21 LTS     │ Neon            │ Testing         │ DevSecOps       │
│ + 20 more       │ + 4 more        │ Profiling       │ + 10 more       │
└─────────────────┴─────────────────┴─────────────────┴─────────────────┘

When to Use:

  • New Project Development: AI-driven technology stack selection and architecture
  • Enterprise Applications: Complete compliance and security automation
  • Multi-Language Systems: Unified development across diverse technology stacks
  • Digital Transformation: End-to-end migration and modernization
  • Startup Scaling: From MVP to enterprise with automated best practices
  • Security-Critical Systems: Comprehensive threat protection and compliance

Implementation Guide

Phase 1: Universal Project Intelligence

AI-Powered Project Analysis & Stack Selection:

class UniversalProjectIntelligence:
    """AI-powered comprehensive project analysis and optimization."""

    async def analyze_project_requirements(
        self,
        requirements: ProjectRequirements,
        constraints: ProjectConstraints
    ) -> ComprehensiveAnalysis:
        """Analyze project requirements across all domains."""

        # Get Context7 documentation for all relevant technologies
        context7_docs = await self.fetch_comprehensive_docs(requirements)

        # AI analysis across all domains
        analysis = await self.ai_analyzer.analyze_comprehensive(
            requirements=requirements,
            constraints=constraints,
            context7_docs=context7_docs
        )

        return ComprehensiveAnalysis(
            language_recommendations=analysis.languages,
            baas_recommendations=analysis.baas,
            security_requirements=analysis.security,
            development_workflow=analysis.workflow,
            compliance_needs=analysis.compliance,
            migration_paths=analysis.migrations,
            cost_projections=analysis.costs
        )

Predictive Technology Roadmapping:

async def generate_technology_roadmap(
    project_context: ProjectContext,
    timeline_months: int = 12
) -> TechnologyRoadmap:
    """Generate AI-powered technology evolution roadmap."""

    # Analyze current state vs future needs
    current_analysis = await self.analyze_current_stack(project_context)
    future_predictions = await self.predict_tech_evolution(project_context, timeline_months)

    # Generate comprehensive roadmap
    roadmap = TechnologyRoadmap(
        phase_1=current_analysis.optimizations,  # Immediate improvements
        phase_2=future_predictions.short_term,   # 3-6 months
        phase_3=future_predictions.medium_term,  # 6-12 months
        phase_4=future_predictions.long_term,    # 12+ months
        migration_strategies=await self.generate_migration_strategies(),
        risk_assessments=await self.assess_migration_risks()
    )

    return roadmap

Phase 2: Unified Development Orchestration

Multi-Domain Development Workflow:

class UniversalDevelopmentOrchestrator:
    """Orchestrates development across all technology domains."""

    async def orchestrate_development(
        self,
        spec_id: str,
        project_config: ProjectConfig
    ) -> DevelopmentResult:
        """Orchestrate complete development lifecycle."""

        # Phase 1: Intelligent Implementation
        implementation = await self.intelligent_implementation(spec_id, project_config)

        # Phase 2: Cross-Domain Testing
        testing = await self.cross_domain_testing(implementation, project_config)

        # Phase 3: Security Integration
        security = await self.integrated_security(implementation, project_config)

        # Phase 4: Performance Optimization
        performance = await self.universal_optimization(implementation, testing, security)

        # Phase 5: Compliance Validation
        compliance = await self.automated_compliance(performance, project_config)

        return DevelopmentResult(
            implementation=implementation,
            testing=testing,
            security=security,
            performance=performance,
            compliance=compliance,
            deployment_ready=compliance.is_compliant
        )

AI-Powered Code Generation with Security:

async def generate_secure_code(
    specification: Specification,
    language: str,
    security_level: SecurityLevel = SecurityLevel.ENTERPRISE
) -> SecureCodeGeneration:
    """Generate code with built-in security and best practices."""

    # Get language-specific security patterns from Context7
    security_patterns = await self.context7.get_security_patterns(language)

    # AI code generation with security considerations
    code_generation = await self.ai_generator.generate_with_security(
        specification=specification,
        language=language,
        security_patterns=security_patterns,
        security_level=security_level
    )

    # Automated security validation
    security_validation = await self.security_validator.validate(
        code_generation.code,
        security_patterns
    )

    return SecureCodeGeneration(
        code=code_generation.code,
        security_measures=security_validation.implemented_measures,
        vulnerabilities=security_validation.found_issues,
        compliance_status=security_validation.compliance_status
    )

Phase 3: Enterprise Security & Compliance

Comprehensive Security Integration:

class UniversalSecurityManager:
    """Manages security across all domains and compliance frameworks."""

    async def implement_comprehensive_security(
        self,
        codebase: Codebase,
        compliance_requirements: List[ComplianceFramework]
    ) -> SecurityImplementation:
        """Implement security across all applicable frameworks."""

        security_implementations = {}

        # OWASP Top 10 2021
        if ComplianceFramework.OWASP_TOP_10 in compliance_requirements:
            security_implementations['owasp'] = await self.implement_owasp_protection(codebase)

        # Zero Trust Architecture
        if ComplianceFramework.ZERO_TRUST in compliance_requirements:
            security_implementations['zero_trust'] = await self.implement_zero_trust(codebase)

        # SOC 2 Type II
        if ComplianceFramework.SOC2 in compliance_requirements:
            security_implementations['soc2'] = await self.implement_soc2_controls(codebase)

        # ISO 27001
        if ComplianceFramework.ISO_27001 in compliance_requirements:
            security_implementations['iso27001'] = await self.implement_iso27001_controls(codebase)

        # GDPR Compliance
        if ComplianceFramework.GDPR in compliance_requirements:
            security_implementations['gdpr'] = await self.implement_gdpr_compliance(codebase)

        return SecurityImplementation(
            implementations=security_implementations,
            validation_results=await self.validate_all_security(security_implementations),
            audit_trail=await self.generate_security_audit_trail(security_implementations)
        )

Automated Threat Modeling:

async def automated_threat_modeling(
    system_architecture: SystemArchitecture,
    attack_vectors: List[AttackVector] = None
) -> ThreatModel:
    """Automated threat modeling using STRIDE and PASTA frameworks."""

    # STRIDE Analysis
    stride_analysis = await self.stride_analyzer.analyze(system_architecture)

    # PASTA Analysis
    pasta_analysis = await self.pasta_analyzer.analyze(system_architecture)

    # AI-powered threat prediction
    predicted_threats = await self.ai_threat_predictor.predict(
        architecture=system_architecture,
        historical_data=await self.get_threat_intelligence(),
        context7_patterns=await self.context7.get_threat_patterns()
    )

    # Generate comprehensive threat model
    threat_model = ThreatModel(
        stride_threats=stride_analysis.threats,
        pasta_risks=pasta_analysis.risks,
        predicted_threats=predicted_threats,
        mitigation_strategies=await self.generate_mitigation_strategies(threat_model),
        implementation_priority=await self.prioritize_mitigations(threat_model)
    )

    return threat_model

Phase 4: Multi-Provider Integration & Optimization

For advanced multi-provider orchestration patterns, see Provider Integration Module.


Advanced Patterns

Pattern 1: Predictive Development Intelligence

AI-Powered Development Prediction:

class PredictiveDevelopmentIntelligence:
    """Predicts optimal development approaches and technologies."""

    async def predict_optimal_approach(
        self,
        project_context: ProjectContext,
        success_metrics: List[SuccessMetric]
    ) -> DevelopmentPrediction:
        """Predict optimal development approach using AI analysis."""

        # Analyze similar projects from Context7 and historical data
        similar_projects = await self.find_similar_projects(project_context)

        # AI prediction based on multiple factors
        prediction = await self.ai_predictor.predict({
            'project_context': project_context,
            'success_metrics': success_metrics,
            'similar_projects': similar_projects,
            'context7_patterns': await self.context7.get_success_patterns(),
            'market_trends': await self.get_market_trends()
        })

        return DevelopmentPrediction(
            recommended_stack=prediction.optimal_stack,
            success_probability=prediction.success_rate,
            risk_assessment=prediction.risks,
            timeline_estimate=prediction.timeline,
            resource_requirements=prediction.resources,
            alternative_approaches=prediction.alternatives
        )

Pattern 2: Enterprise Compliance Automation

Automated Compliance Framework:

class EnterpriseComplianceAutomation:
    """Automates compliance across multiple frameworks."""

    async def automated_compliance_validation(
        self,
        codebase: Codebase,
        frameworks: List[ComplianceFramework]
    ) -> ComplianceReport:
        """Automated validation across multiple compliance frameworks."""

        compliance_results = {}

        for framework in frameworks:
            # Framework-specific validation
            validation = await self.validate_framework(codebase, framework)
            compliance_results[framework.name] = validation

            # Automated remediation
            if not validation.is_compliant:
                remediation = await self.generate_compliance_remediation(
                    codebase, validation.issues, framework
                )
                compliance_results[framework.name].remediation = remediation

        # Cross-framework validation
        cross_compliance = await self.validate_cross_framework_consistency(
            compliance_results
        )

        return ComplianceReport(
            framework_results=compliance_results,
            overall_compliance=all(r.is_compliant for r in compliance_results.values()),
            cross_framework_issues=cross_compliance.issues,
            audit_readiness=cross_compliance.audit_ready,
            compliance_score=self.calculate_compliance_score(compliance_results)
        )

Pattern 3: Universal Performance Intelligence

Cross-Domain Performance Optimization:

class UniversalPerformanceIntelligence:
    """Optimizes performance across all technology domains."""

    async def universal_performance_optimization(
        self,
        system: SystemArchitecture,
        performance_targets: PerformanceTargets
    ) -> OptimizationPlan:
        """Optimize performance across the entire technology stack."""

        optimizations = {}

        # Language-specific optimizations
        for language in system.languages:
            optimizations[language] = await self.optimize_language_performance(
                language, performance_targets
            )

        # Database optimizations
        for database in system.databases:
            optimizations[database] = await self.optimize_database_performance(
                database, performance_targets
            )

        # Network and infrastructure optimizations
        optimizations['infrastructure'] = await self.optimize_infrastructure_performance(
            system.infrastructure, performance_targets
        )

        # AI-powered predictive optimization
        predictive_optimizations = await self.ai_optimizer.predict_optimizations(
            current_performance=await self.measure_current_performance(system),
            target_performance=performance_targets,
            context7_patterns=await self.context7.get_optimization_patterns()
        )

        return OptimizationPlan(
            immediate_optimizations=optimizations,
            predictive_optimizations=predictive_optimizations,
            implementation_order=await self.prioritize_optimizations(optimizations),
            expected_improvements=await self.estimate_improvements(optimizations),
            roi_analysis=await self.calculate_optimization_roi(optimizations)
        )

Integration Examples

Example 1: Complete E-Commerce Platform Development

AI-Driven Full-Stack Development:

# Step 1: Universal Project Analysis
project_intelligence = UniversalProjectIntelligence()
analysis = await project_intelligence.analyze_project_requirements(
    requirements=ECommerceRequirements(),
    constraints=ProjectConstraints(budget="$50k", timeline="3 months")
)

# Step 2: AI Stack Selection
selected_stack = analysis.recommendations
# Result: {
#   languages: ["TypeScript", "Python"],
#   baas: {"auth": "Clerk", "database": "Supabase", "deployment": "Vercel"},
#   security: ["OWASP", "SOC2", "GDPR"],
#   compliance: ["PCI-DSS", "SOX"]
# }

# Step 3: Universal Development Orchestration
orchestrator = UniversalDevelopmentOrchestrator()
development_result = await orchestrator.orchestrate_development(
    spec_id="ECOM-001",
    project_config=selected_stack
)

# Step 4: Automated Security & Compliance
security_manager = UniversalSecurityManager()
security_implementation = await security_manager.implement_comprehensive_security(
    codebase=development_result.implementation,
    compliance_requirements=[ComplianceFramework.OWASP_TOP_10, ComplianceFramework.SOC2]
)

# Step 5: Provider Integration & Deployment

**Works Well With**:
- `moai-core-agent-factory` - Create specialized agents for unique requirements
- `moai-cc-configuration` - Environment and settings management
- `moai-context7-integration` - Latest documentation and best practices
- `moai-foundation-trust` - Quality assurance and TRUST principles
- `moai-docs-generation` - Automated documentation creation
- All domain-specific skills - Specialized knowledge integration

---

**Status**: Production Ready (Enterprise)
**Intelligence Level**: Universal AI-Powered Development
**Security Level**: Enterprise-Grade with Zero Trust
**Compliance Coverage**: OWASP, SOC 2, ISO 27001, GDPR, PCI-DSS, SOX
**Performance**: Optimized for Large-Scale Enterprise Applications

**Generated with**: MoAI-ADK Universal Skill Factory
**Last Updated**: 2025-11-25
**Maintained by**: MoAI-ADK Enterprise Team

*For complete implementation examples and advanced patterns, see: [modules/](modules/) and [examples/](examples/)*

平台分布

Claude Code

28.11%
按下载量换算18

windsurf

23.01%
按下载量换算15

OpenCode

19.98%
按下载量换算13

Codex

12.37%
按下载量换算8

Antigravity

7.82%
按下载量换算5

Gemini CLI

3.35%
按下载量换算2

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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