ai-enablement-intelligence

Files (10)

• SKILL.md
• ai-adoption-template.md · assets/
• ai-readiness-rubric.md · assets/
• copilot-integration-checklist.md · assets/
• result-aggregator.js · orchestration/
• skill-router.js · orchestration/
• adr-generator.js · scripts/
• ai-detector.js · scripts/
• copilot-analyzer.js · scripts/
• maturity-scoring.js · scripts/

SKILL.md (first lines)

# AI Enablement Intelligence

Perform comprehensive repository intelligence analysis with constraint discovery and research-backed
AI enablement recommendations that help teams accelerate their product goals within their actual
constraints and context.

## Core Process

### Phase 1: Repository Context Analysis

- **Technology Stack Assessment**: Analyze languages, frameworks, and dependency maturity
- **Team Size & Workflow Detection**: Evaluate development patterns and team structure
- **Product Lifecycle Analysis**: Identify repository age, maintenance profile, and development
  velocity
- **Testing Strategy Evaluation**: Assess testing maturity and automation levels

### Phase 2: Constraint Discovery

- **External Constraints**: Identify corporate policies, security requirements, and budget
  limitations
- **Technical Constraints**: Analyze infrastructure limitations, tool availability, and access
  restrictions
- **Repository Constraints**: Evaluate existing patterns, processes, and team skill gaps
- **YOLO Mode Testing**: Probe actual AI tool availability and connectivity

### Phase 3: AI Opportunity Mapping

- **Context-Aware Analysis**: Map AI opportunities to specific repository characteristics
- **Constraint-Compatible Recommendations**: Identify AI approaches that work within detected
  limitations
- **Research-Backed Tool Selection**: Provide market analysis and implementation research for team
  profiles
- **Product Acceleration Focus**: Prioritize recommendations that accelerate specific team goals

### Phase 4: Enablement Guidance Generation

- **Implementation-Ready Artifacts**: Generate ready-to-use code snippets, prompts, and workflows
- **Spec-Driven Development Guidance**: Promote specification-first AI workflows
- **Adversarial Reasoning Encouragement**: Foster critical thinking with AI assistance
- **Team Learning Enablement**: Build continuous improvement capabilities

## Repository Context Analysis

### Technology Stack Maturity

**AI-Ready Technologies:**

- JavaScript/TypeScript: Rich ecosystem of AI tools and Copilot integration
- Python: Extensive AI/ML libraries and automation frameworks
- React/Vue/Angular: Component generation and UI pattern assistance
- Node.js: API development and backend automation opportunities

**Maturity Assessment:**

- Dependency stability and update frequency analysis
- Framework version and ecosystem maturity evaluation
- Community support and documentation quality assessment
- Integration complexity and learning curve analysis

### Team Context Detection

**Team Size Indicators:**

- Commit frequency and contributor patterns
- Code review processes and collaboration patterns
- Documentation depth and onboarding resources
- Branch strategy and release cadence analysis

**Workflow Maturity:**

- CI/CD pipeline sophistication and automation levels
- Testing coverage and quality assurance processes
- Development tooling and productivity patterns
- Communication and coordination mechanisms

## Constraint Discovery Framework

### External Constraint Detection

**Corporate Policy Analysis:**

- AI usage guidelines and policy documents
- Security and compliance requirements
- Data privacy and handling restrictions
- Budget and procurement constraints

**Security & Compliance:**

- Network restrictions and firewall policies
- Data handling and privacy requirements
- Audit trail and monitoring needs
- Regulatory compliance frameworks

### Technical Constraint Analysis

**Infrastructure Limitations:**

- Development environment restrictions
- Build system and deployment constraints
- Tool installation and permission limitations
- Network connectivity and API access

**Tool Availability Assessment:**

- Existing AI tool integration and usage
- Development environment capabilities
- External service access and authentication
- Plugin and extension compatibility

### Repository Constraint Evaluation

**Process Constraints:**

- Existing development workflows and resistance to change
- Code review processes and quality gates
- Documentation standards and communication patterns
- Team skill gaps and learning capacity

**Technical Debt Assessment:**

From Ankh-Studio/copilot-enterprise-eval-plugin, MIT.

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