Transform natural language descriptions into complete dx configurations using AI. Just describe your workflow, and dx generates the perfect dx.yaml file.

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How it works

Natural Language Input

Describe your workflow in plain English to AI

Intelligent Generation

AI understands context and generates appropriate commands

Smart Validation

Generated configs are validated and safe to use

Instant Workflow

Ready-to-use dx.yaml with proper aliases and structure

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Basic Usage

# Generate dx config from natural language
dx ai generate --prompt "I need commands to build Rust project, run tests, and deploy to staging"
Generated dx.yaml: 
items:
  - name: "Build Project"
    alias: "build"
    description: "Build Rust project in release mode"
    cmd: "cargo build --release"
    
  - name: "Run Tests"
    alias: "test"  
    description: "Execute all test suites"
    cmd: "cargo test"
    
  - name: "Deploy to Staging"
    alias: "deploy"
    description: "Deploy application to staging environment" 
    cmd: "kubectl apply -f k8s/staging/"

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Advanced Prompts

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Project Analysis

# AI analyzes your project and suggests workflow
dx ai analyze
AI examines:
  • Package files (package.json, Cargo.toml, pom.xml)
  • Existing scripts (scripts/, Makefile, .github/workflows/)
  • Documentation (README.md, CONTRIBUTING.md)
  • Project structure and conventions

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Context-Aware Generation

# Provide specific context for better results
dx ai generate --prompt "
Node.js project with TypeScript
Uses Jest for testing  
Deploys to Vercel
Has linting with ESLint
Needs database migrations
"
Generated output: 
items:
  - name: "Development"
    description: "Development workflow commands"
    
    children:
      - name: "Install Dependencies"
        alias: "install"
        cmd: "npm install"
        
      - name: "Start Development Server"
        alias: "dev"
        cmd: "npm run dev"
        external: true
        
      - name: "Build Production"
        alias: "build"  
        cmd: "npm run build"
        
  - name: "Quality Assurance"
    description: "Testing and code quality"
    
    children:
      - name: "Run Tests"
        alias: "test"
        cmd: "npm run test"
        
      - name: "Lint Code"  
        alias: "lint"
        cmd: "npm run lint"
        
      - name: "Type Check"
        alias: "typecheck"
        cmd: "npm run type-check"
        
  - name: "Database"
    description: "Database operations"
    
    children:
      - name: "Run Migrations"
        alias: "migrate"
        cmd: "npm run migrate"
        
      - name: "Seed Database"
        alias: "seed"
        cmd: "npm run seed"
        
  - name: "Deployment"
    description: "Deploy to Vercel"
    alias: "deploy"
    cmd: "vercel --prod"
    external: true

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Provider Configuration

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OpenAI Integration

# config.toml
[ai]
provider = "openai"
api_key = "${OPENAI_API_KEY}"
model = "gpt-4"

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Anthropic Integration

[ai]
provider = "anthropic"
api_key = "${ANTHROPIC_API_KEY}"
model = "claude-3-sonnet-20240229"

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Local Model Support

[ai]
provider = "local"
endpoint = "http://localhost:11434"  # Ollama
model = "codellama:7b"

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Command Examples

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Interactive Generation

# Interactive prompt builder
dx ai generate --interactive

# AI asks clarifying questions:
# "What type of project is this?"
# "What deployment platform do you use?"
# "Do you need database commands?"
# "What testing framework?"

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Template-Based Generation

# Use pre-built templates with customization
dx ai template web-app --framework react --database postgres

# Templates available:
# web-app, api-service, cli-tool, library, mobile-app

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Incremental Updates

# Add new commands to existing config
dx ai extend --prompt "Add Docker commands for containerized deployment"

# AI intelligently merges with existing dx.yaml

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AI Prompt Patterns

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Effective Prompts

# ✅ Good - Specific with context
dx ai generate --prompt "
Python FastAPI service
PostgreSQL database with Alembic migrations  
Docker deployment to AWS ECS
Uses pytest for testing
GitHub Actions for CI/CD
"

# ✅ Good - Workflow focused  
dx ai generate --prompt "
I need commands for:
- Building and testing React app
- Running Storybook for components
- Deploying to Netlify  
- Managing environment configs
"

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Best Practices

  • Be specific about your tech stack
  • Include deployment details (platform, environment)
  • Mention testing approach (framework, types)
  • Describe team workflow (CI/CD, code review)
  • Add context about project structure

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Security & Safety

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Safe Generation

  • Command validation - AI-generated commands are validated before use
  • No secrets - API keys and sensitive data excluded from generation
  • Human review - Generated configs require confirmation before saving
  • Backup creation - Existing configs are backed up before replacement

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Privacy Protection

# Local generation (no data sent to external AI)
dx ai generate --local --prompt "Add build commands for Rust project"

# Prompt sanitization (removes sensitive info)  
dx ai generate --sanitize --prompt "Add deploy to prod with secret key abc123"
# AI receives: "Add deploy to prod with [REDACTED]"

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Integration Examples

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CI/CD Pipeline Generation

dx ai generate --prompt "
Generate CI/CD workflow:
- Run on every PR to main
- Build, test, security scan  
- Deploy to staging on merge
- Manual production deployment
"

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Team Onboarding

# Generate onboarding menu for new developers
dx ai template onboarding --team-size large --project backend-api

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Legacy Project Migration

# Convert existing scripts to dx format
dx ai migrate --scripts ./scripts/ --include-makefiles
Start with simple prompts and let AI ask clarifying questions for better results.
Always review AI-generated commands before executing them, especially those involving deployment or system changes.
Command generation works best when combined with your existing dx.yaml - AI can understand and extend your current workflow patterns.