ADR-008: Intelligent Content Population Engine for Diataxis Documentation

id: 008-intelligent-content-population-engine title: 'ADR-008: Intelligent Content Population Engine' sidebar_label: 'ADR-8: Intelligent Content Population Engine' sidebar_position: 8

Status

Accepted

Context

DocuMCP currently creates excellent Diataxis-compliant documentation structures through ADR-004 and ADR-006, but produces only skeleton content with placeholder text. This creates a significant gap between the framework's potential and delivered value, requiring users to manually populate all documentation content despite having comprehensive repository analysis data available.

The current setup-structure tool (from ADR-006) provides:

✅ Professional Diataxis directory structure
✅ SSG-specific configuration and frontmatter
✅ Basic template content explaining Diataxis categories
❌ Missing: Project-specific content analysis and intelligent population
❌ Missing: Repository analysis integration for content suggestions
❌ Missing: Technology-specific documentation generation

Current User Journey:

Repository analysis identifies TypeScript project with Express.js, PostgreSQL, Jest tests
Diataxis structure created with generic placeholder content
User must manually research and write all tutorials, how-to guides, reference docs, and explanations
Result: 8-20 hours of manual documentation work despite intelligent analysis

Target User Journey:

Repository analysis identifies project characteristics and technology stack
Intelligent content population generates project-specific documentation
User reviews and refines 60-80% pre-populated, contextually relevant content
Result: 1-2 hours of refinement work with professional-quality starting point

Key gaps identified:

Repository analysis data (125 files, TypeScript/JavaScript ecosystem, test infrastructure) not leveraged for content generation
Extensive technology detection capabilities underutilized for creating relevant examples
Diataxis framework implementation incomplete without intelligent content planning (ADR-004, lines 153-192)
Competitive disadvantage: users get empty templates instead of intelligent assistance

Decision

We will implement an Intelligent Content Population Engine that bridges repository analysis with Diataxis content generation, creating the missing layer between structural generation and user-ready documentation.

Architecture Overview:

1. Content Intelligence Engine

Purpose: Transform repository analysis into structured content plans Core Capabilities:

Project characteristic analysis (technology stack, architecture patterns, API surfaces)
User journey mapping to appropriate Diataxis categories
Content gap identification and priority assignment
Technology-specific example and code snippet generation

2. Project-Aware Content Generators

Purpose: Create contextually relevant content for each Diataxis category Scope: Four specialized generators aligned with Diataxis framework:

Tutorial Content Generator

Getting Started: Framework-specific installation, setup, and first success
Feature Tutorials: Based on detected APIs, key dependencies, and project complexity
Integration Tutorials: For detected services, databases, and external dependencies

How-To Guide Generator

Common Tasks: Derived from project type and technology stack
Troubleshooting: Based on detected tools, frameworks, and common pain points
Deployment Guides: Technology-specific deployment patterns and best practices

Reference Documentation Generator

API Documentation: Auto-generate from detected API surfaces and endpoints
Configuration Reference: Based on identified config files and environment variables
CLI Reference: For detected command-line tools and scripts

Explanation Content Generator

Architecture Overview: Based on detected patterns, dependencies, and project structure
Design Decisions: Technology choices and their implications
Concept Explanations: Framework and domain-specific concepts

3. Repository Analysis Integration Layer

Purpose: Bridge analysis data with content generation Integration Points:

Language ecosystem analysis → Technology-specific content
Dependency analysis → Framework integration guides
Project structure analysis → Architecture documentation
Complexity assessment → Content depth and sophistication level

Implementation Architecture:

interface ContentPopulationEngine {
  // Core engine interface
  populateContent(
    analysisId: string,
    docsPath: string, 
    options: PopulationOptions
  ): Promise<PopulationResult>;
  
  // Content planning
  generateContentPlan(analysis: RepositoryAnalysis): ContentPlan;
  identifyContentGaps(existing: ExistingContent, plan: ContentPlan): ContentGap[];
  
  // Content generation
  generateTutorialContent(plan: TutorialPlan, context: ProjectContext): TutorialContent;
  generateHowToContent(plan: HowToPlan, context: ProjectContext): HowToContent;
  generateReferenceContent(plan: ReferencePlan, context: ProjectContext): ReferenceContent;
  generateExplanationContent(plan: ExplanationPlan, context: ProjectContext): ExplanationContent;
}

interface PopulationOptions {
  level: 'basic' | 'comprehensive' | 'intelligent';
  includeCodeExamples: boolean;
  projectSpecific: boolean;
  preserveExisting: boolean;
  customizationProfile?: CustomizationProfile;
}

interface ContentPlan {
  tutorials: TutorialSuggestion[];
  howToGuides: HowToSuggestion[];  
  reference: ReferenceSuggestion[];
  explanation: ExplanationSuggestion[];
  crossReferences: ContentRelationship[];
  estimatedEffort: EffortEstimate;
}

interface ProjectContext {
  primaryLanguage: string;
  frameworks: Framework[];
  architecture: ArchitecturePattern;
  apiSurfaces: APIAnalysis[];
  deploymentTargets: DeploymentTarget[];
  testingFrameworks: TestingFramework[];
  dependencies: DependencyAnalysis;
}

Content Generation Algorithms:

Tutorial Generation Algorithm

function generateTutorials(analysis: RepositoryAnalysis): TutorialSuggestion[] {
  const suggestions: TutorialSuggestion[] = [];
  
  // Always include getting started
  suggestions.push({
    title: `Getting Started with ${analysis.metadata.projectName}`,
    description: `Learn ${analysis.recommendations.primaryLanguage} development with ${analysis.metadata.projectName}`,
    priority: 'high',
    sections: generateGettingStartedSections(analysis),
    codeExamples: generateTechnologySpecificExamples(analysis.dependencies.ecosystem)
  });
  
  // Framework-specific tutorials
  if (analysis.dependencies.packages.includes('express')) {
    suggestions.push({
      title: 'Building REST APIs with Express.js',
      description: 'Complete guide to creating RESTful services',
      priority: 'high',
      sections: generateExpressTutorialSections(analysis)
    });
  }
  
  // Database integration tutorials
  const dbDeps = detectDatabaseDependencies(analysis.dependencies.packages);
  dbDeps.forEach(db => {
    suggestions.push({
      title: `Database Integration with ${db.name}`,
      description: `Connect and interact with ${db.name} databases`,
      priority: 'medium',
      sections: generateDatabaseTutorialSections(db, analysis)
    });
  });
  
  return suggestions;
}

Reference Generation Algorithm

function generateReference(analysis: RepositoryAnalysis): ReferenceSuggestion[] {
  const suggestions: ReferenceSuggestion[] = [];
  
  // API documentation from detected endpoints
  const apiSurfaces = detectAPIEndpoints(analysis);
  if (apiSurfaces.length > 0) {
    suggestions.push({
      title: 'API Reference',
      description: 'Complete API endpoint documentation',
      content: generateAPIDocumentation(apiSurfaces),
      format: 'openapi-spec'
    });
  }
  
  // Configuration reference from detected config files
  const configFiles = detectConfigurationFiles(analysis);
  configFiles.forEach(config => {
    suggestions.push({
      title: `${config.type} Configuration`,
      description: `Configuration options for ${config.name}`,
      content: generateConfigurationReference(config),
      format: 'configuration-table'
    });
  });
  
  // CLI reference from detected scripts
  const cliCommands = detectCLICommands(analysis);
  if (cliCommands.length > 0) {
    suggestions.push({
      title: 'Command Line Interface',
      description: 'Available commands and options',
      content: generateCLIReference(cliCommands),
      format: 'cli-documentation'
    });
  }
  
  return suggestions;
}

Technology-Specific Content Templates:

JavaScript/TypeScript Ecosystem

const JAVASCRIPT_TEMPLATES = {
  gettingStarted: {
    prerequisites: ['Node.js 18+', 'npm or yarn', 'Git'],
    installationSteps: [
      'Clone the repository',
      'Install dependencies with npm install',
      'Copy environment variables',
      'Run development server'
    ],
    verificationSteps: [
      'Check server starts successfully',
      'Access application in browser',
      'Run test suite to verify setup'
    ]
  },
  
  expressAPI: {
    sections: [
      'Project Structure Overview',
      'Creating Your First Route',
      'Middleware Configuration', 
      'Database Integration',
      'Error Handling',
      'Testing Your API'
    ],
    codeExamples: generateExpressCodeExamples
  },
  
  testingGuides: {
    jest: generateJestHowToGuides,
    cypress: generateCypressHowToGuides,
    playwright: generatePlaywrightHowToGuides
  }
};

Multi-Language Framework Support

JavaScript/TypeScript Ecosystem

const JAVASCRIPT_TEMPLATES = {
  gettingStarted: {
    prerequisites: ['Node.js 18+', 'npm or yarn', 'Git'],
    installationSteps: [
      'Clone the repository',
      'Install dependencies with npm install',
      'Copy environment variables',
      'Run development server'
    ],
    verificationSteps: [
      'Check server starts successfully',
      'Access application in browser',
      'Run test suite to verify setup'
    ]
  },
  
  frameworks: {
    express: {
      tutorials: ['REST API Development', 'Middleware Configuration', 'Database Integration'],
      howToGuides: ['Performance Optimization', 'Error Handling', 'Authentication Setup'],
      reference: ['Route Configuration', 'Middleware Reference', 'Configuration Options'],
      explanation: ['Express Architecture', 'Middleware Pattern', 'Async Handling']
    },
    react: {
      tutorials: ['Component Development', 'State Management', 'React Router'],
      howToGuides: ['Performance Optimization', 'Testing Components', 'Deployment'],
      reference: ['Component API', 'Hooks Reference', 'Build Configuration'],
      explanation: ['Component Architecture', 'State Flow', 'Rendering Lifecycle']
    },
    nestjs: {
      tutorials: ['Dependency Injection', 'Controllers and Services', 'Database Integration'],
      howToGuides: ['Custom Decorators', 'Microservices', 'GraphQL Integration'],
      reference: ['Decorator Reference', 'Module System', 'Configuration'],
      explanation: ['DI Architecture', 'Module Design', 'Enterprise Patterns']
    }
  }
};

Python Ecosystem Support

const PYTHON_TEMPLATES = {
  gettingStarted: {
    prerequisites: ['Python 3.8+', 'pip or poetry', 'Virtual environment'],
    installationSteps: [
      'Create virtual environment',
      'Activate virtual environment',
      'Install dependencies from requirements.txt/pyproject.toml',
      'Set up environment variables',
      'Run development server'
    ],
    verificationSteps: [
      'Check application starts successfully',
      'Run test suite with pytest',
      'Verify API endpoints respond correctly'
    ]
  },
  
  frameworks: {
    django: {
      tutorials: [
        'Django Project Setup and Configuration',
        'Models and Database Integration',
        'Views and URL Routing',
        'Django REST Framework APIs',
        'User Authentication and Permissions'
      ],
      howToGuides: [
        'Deploy Django to Production',
        'Optimize Database Queries',
        'Implement Caching Strategies',
        'Handle File Uploads',
        'Configure CORS and Security'
      ],
      reference: [
        'Django Settings Reference',
        'Model Field Types',
        'URL Configuration Patterns',
        'Middleware Reference',
        'Management Commands'
      ],
      explanation: [
        'Django MTV Architecture',
        'ORM Design Decisions',
        'Security Model',
        'Scalability Patterns'
      ]
    },
    fastapi: {
      tutorials: [
        'FastAPI Application Structure',
        'Pydantic Models and Validation',
        'Dependency Injection System',
        'Database Integration with SQLAlchemy',
        'Authentication and Security'
      ],
      howToGuides: [
        'Optimize FastAPI Performance',
        'Implement Background Tasks',
        'Handle File Processing',
        'Set up Monitoring and Logging',
        'Deploy with Docker and Kubernetes'
      ],
      reference: [
        'FastAPI Decorators Reference',
        'Pydantic Model Configuration',
        'Dependency System Reference',
        'Security Utilities',
        'Testing Utilities'
      ],
      explanation: [
        'ASGI vs WSGI Architecture',
        'Type Hints and Validation',
        'Dependency Injection Benefits',
        'Performance Characteristics'
      ]
    },
    flask: {
      tutorials: [
        'Flask Application Factory Pattern',
        'Blueprint Organization',
        'Database Integration with SQLAlchemy',
        'User Session Management',
        'RESTful API Development'
      ],
      howToGuides: [
        'Structure Large Flask Applications',
        'Implement Rate Limiting',
        'Handle Background Jobs',
        'Configure Production Deployment',
        'Debug Flask Applications'
      ],
      reference: [
        'Flask Configuration Reference',
        'Request and Response Objects',
        'Template Engine Reference',
        'Extension Integration',
        'CLI Commands'
      ],
      explanation: [
        'Flask Philosophy and Design',
        'WSGI Application Structure',
        'Extension Ecosystem',
        'Microframework Benefits'
      ]
    }
  }
};

class PythonContentGenerator implements FrameworkContentGenerator {
  detectFramework(analysis: RepositoryAnalysis): Framework[] {
    const frameworks: Framework[] = [];
    
    // Django detection
    if (this.hasDependency(analysis, 'django') || 
        this.hasFile(analysis, 'manage.py') ||
        this.hasFile(analysis, 'settings.py')) {
      frameworks.push({
        name: 'django',
        version: this.extractVersion(analysis, 'django'),
        configFiles: ['settings.py', 'urls.py', 'wsgi.py'],
        appStructure: this.analyzeDjangoApps(analysis)
      });
    }
    
    // FastAPI detection
    if (this.hasDependency(analysis, 'fastapi') ||
        this.hasImport(analysis, 'from fastapi import')) {
      frameworks.push({
        name: 'fastapi',
        version: this.extractVersion(analysis, 'fastapi'),
        configFiles: this.getFastAPIConfigFiles(analysis),
        routerStructure: this.analyzeFastAPIRouters(analysis)
      });
    }
    
    // Flask detection
    if (this.hasDependency(analysis, 'flask') ||
        this.hasImport(analysis, 'from flask import')) {
      frameworks.push({
        name: 'flask',
        version: this.extractVersion(analysis, 'flask'),
        configFiles: this.getFlaskConfigFiles(analysis),
        blueprintStructure: this.analyzeFlaskBlueprints(analysis)
      });
    }
    
    return frameworks;
  }
  
  generateFrameworkContent(framework: Framework, context: ProjectContext): FrameworkContent {
    const templates = PYTHON_TEMPLATES.frameworks[framework.name];
    
    return {
      tutorials: templates.tutorials.map(title => ({
        title: `${title} for ${context.projectName}`,
        content: this.generatePythonTutorialContent(framework, title, context),
        codeExamples: this.generatePythonCodeExamples(framework, title, context)
      })),
      howToGuides: templates.howToGuides.map(title => ({
        title,
        content: this.generatePythonHowToContent(framework, title, context),
        tasks: this.generatePythonTasks(framework, title, context)
      })),
      reference: templates.reference.map(title => ({
        title,
        content: this.generatePythonReferenceContent(framework, title, context)
      })),
      explanation: templates.explanation.map(title => ({
        title,
        content: this.generatePythonExplanationContent(framework, title, context)
      }))
    };
  }
}

Framework-Specific Content Generation

interface FrameworkContentGenerator {
  detectFramework(dependencies: string[]): Framework | null;
  generateFrameworkContent(framework: Framework, context: ProjectContext): FrameworkContent;
}

const FRAMEWORK_GENERATORS: Record<string, FrameworkContentGenerator> = {
  // JavaScript/TypeScript frameworks
  'express': new ExpressContentGenerator(),
  'react': new ReactContentGenerator(), 
  'vue': new VueContentGenerator(),
  'angular': new AngularContentGenerator(),
  'nestjs': new NestJSContentGenerator(),
  'fastify': new FastifyContentGenerator(),
  
  // Python frameworks
  'django': new DjangoContentGenerator(),
  'fastapi': new FastAPIContentGenerator(),
  'flask': new FlaskContentGenerator(),
  'pyramid': new PyramidContentGenerator(),
  
  // Future language support
  'spring-boot': new SpringBootContentGenerator(), // Java
  'gin': new GinContentGenerator(), // Go
  'actix-web': new ActixContentGenerator() // Rust
};

Alternatives Considered

Manual Content Creation Only

Pros: Simple implementation, full user control, no AI dependency
Cons: Massive user effort, inconsistent quality, underutilizes analysis capabilities
Decision: Rejected - provides minimal value over generic templates

AI-Generated Content via External APIs

Pros: Advanced content generation, natural language processing
Cons: External dependencies, costs, inconsistent quality, latency issues
Decision: Rejected for initial version - adds complexity without guaranteed quality

Community-Contributed Content Templates

Pros: Diverse perspectives, battle-tested content, community engagement
Cons: Quality control challenges, maintenance overhead, incomplete coverage
Decision: Considered for future enhancement - focus on algorithmic generation first

Generic Template Expansion

Pros: Easier implementation, consistent structure
Cons: Still requires significant manual work, doesn't leverage analysis intelligence
Decision: Rejected - doesn't address core value proposition gap

Consequences

Positive

Dramatic User Value Increase: 60-80% content pre-population vs. empty templates
Competitive Differentiation: Only documentation tool with intelligent content generation
Analysis ROI: Comprehensive repository analysis finally delivers proportional value
Framework Completion: Fulfills ADR-004 vision for content planning intelligence
User Experience: Transform from "structure generator" to "documentation assistant"

Negative

Implementation Complexity: Significant engineering effort for content generation algorithms
Content Quality Risk: Generated content may require refinement for accuracy
Technology Coverage: Initial version limited to well-known frameworks and patterns
Maintenance Overhead: Content templates require updates as technologies evolve

Risks and Mitigations

Quality Control: Implement content validation and user review workflows
Technology Coverage: Start with most common frameworks, expand based on usage
Algorithm Accuracy: Validate generated content against project reality
User Expectations: Clear communication about generated vs. curated content

Implementation Details

MCP Tool Interface

// New tool: populate_diataxis_content
interface PopulateDiataxisContentTool {
  name: "populate_diataxis_content";
  description: "Intelligently populate Diataxis documentation with project-specific content";
  inputSchema: {
    type: "object";
    properties: {
      analysisId: {
        type: "string";
        description: "Repository analysis ID from analyze_repository tool";
      };
      docsPath: {
        type: "string"; 
        description: "Path to documentation directory";
      };
      populationLevel: {
        type: "string";
        enum: ["basic", "comprehensive", "intelligent"];
        default: "comprehensive";
        description: "Level of content generation detail";
      };
      includeProjectSpecific: {
        type: "boolean";
        default: true;
        description: "Generate project-specific examples and code";
      };
      preserveExisting: {
        type: "boolean";
        default: true;
        description: "Preserve any existing content";
      };
      technologyFocus: {
        type: "array";
        items: { type: "string" };
        description: "Specific technologies to emphasize in content";
      };
    };
    required: ["analysisId", "docsPath"];
  };
}

Content Generation Pipeline

class ContentPopulationEngine {
  async populateContent(args: PopulationArgs): Promise<PopulationResult> {
    // 1. Retrieve and validate repository analysis
    const analysis = await this.getRepositoryAnalysis(args.analysisId);
    this.validateAnalysis(analysis);
    
    // 2. Generate content plan based on project characteristics
    const contentPlan = await this.generateContentPlan(analysis, args.populationLevel);
    
    // 3. Generate content for each Diataxis category
    const [tutorials, howTos, reference, explanation] = await Promise.all([
      this.generateTutorialContent(contentPlan.tutorials, analysis),
      this.generateHowToContent(contentPlan.howToGuides, analysis),
      this.generateReferenceContent(contentPlan.reference, analysis), 
      this.generateExplanationContent(contentPlan.explanation, analysis)
    ]);
    
    // 4. Write content to documentation structure
    const filesCreated = await this.writeContentToStructure(
      args.docsPath, 
      { tutorials, howTos, reference, explanation },
      args.preserveExisting
    );
    
    // 5. Generate cross-references and navigation updates
    await this.updateNavigationAndCrossReferences(args.docsPath, contentPlan);
    
    return {
      success: true,
      filesCreated,
      contentPlan,
      populationMetrics: this.calculatePopulationMetrics(filesCreated),
      nextSteps: this.generateNextSteps(analysis, contentPlan)
    };
  }
}

Technology Detection and Content Mapping

interface TechnologyMapper {
  detectTechnologies(analysis: RepositoryAnalysis): TechnologyProfile;
  mapToContentTemplates(technologies: TechnologyProfile): ContentTemplateSet;
  generateTechnologySpecificExamples(
    technology: Technology, 
    context: ProjectContext
  ): CodeExample[];
}

class JavaScriptTechnologyMapper implements TechnologyMapper {
  detectTechnologies(analysis: RepositoryAnalysis): TechnologyProfile {
    const profile: TechnologyProfile = {
      runtime: this.detectRuntime(analysis), // Node.js, Deno, Bun
      framework: this.detectFramework(analysis), // Express, Fastify, Koa
      frontend: this.detectFrontend(analysis), // React, Vue, Angular
      database: this.detectDatabase(analysis), // PostgreSQL, MongoDB, Redis
      testing: this.detectTesting(analysis), // Jest, Mocha, Playwright
      deployment: this.detectDeployment(analysis), // Docker, Kubernetes, Vercel
      devops: this.detectDevOpsTools(analysis) // Ansible, Tekton, OpenShift, Podman
    };
    
    return profile;
  }
  
  mapToContentTemplates(technologies: TechnologyProfile): ContentTemplateSet {
    return {
      tutorials: this.generateTutorialTemplates(technologies),
      howToGuides: this.generateHowToTemplates(technologies),
      reference: this.generateReferenceTemplates(technologies),
      explanation: this.generateExplanationTemplates(technologies)
    };
  }
}

DevOps and Infrastructure Tooling Support

DevOps Tool Detection and Content Generation

interface DevOpsToolMapper {
  detectDevOpsTools(analysis: RepositoryAnalysis): DevOpsToolProfile;
  generateDevOpsContent(tools: DevOpsToolProfile, context: ProjectContext): DevOpsContent;
  createInfrastructureDocumentation(
    infrastructure: InfrastructureProfile,
    deploymentPattern: DeploymentPattern
  ): InfrastructureDocumentation;
}

interface DevOpsToolProfile {
  containerization: ContainerTechnology[]; // Docker, Podman, Buildah
  orchestration: OrchestrationTechnology[]; // Kubernetes, OpenShift, Nomad
  cicd: CICDTechnology[]; // Tekton, GitHub Actions, Jenkins, GitLab CI
  configuration: ConfigManagementTechnology[]; // Ansible, Terraform, Helm
  monitoring: MonitoringTechnology[]; // Prometheus, Grafana, Jaeger
  security: SecurityTechnology[]; // Falco, OPA, Vault
}

class DevOpsContentGenerator implements DevOpsToolMapper {
  detectDevOpsTools(analysis: RepositoryAnalysis): DevOpsToolProfile {
    return {
      containerization: this.detectContainerization(analysis),
      orchestration: this.detectOrchestration(analysis),
      cicd: this.detectCICD(analysis),
      configuration: this.detectConfigManagement(analysis),
      monitoring: this.detectMonitoring(analysis),
      security: this.detectSecurity(analysis)
    };
  }
  
  private detectContainerization(analysis: RepositoryAnalysis): ContainerTechnology[] {
    const detected: ContainerTechnology[] = [];
    
    // Docker detection
    if (this.hasFile(analysis, 'Dockerfile') || 
        this.hasFile(analysis, 'docker-compose.yml') ||
        this.hasFile(analysis, 'docker-compose.yaml')) {
      detected.push({
        name: 'docker',
        version: this.extractDockerVersion(analysis),
        configFiles: this.getDockerFiles(analysis),
        usage: this.analyzeDockerUsage(analysis)
      });
    }
    
    // Podman detection
    if (this.hasFile(analysis, 'Containerfile') ||
        this.hasReference(analysis, 'podman') ||
        this.hasFile(analysis, 'podman-compose.yml')) {
      detected.push({
        name: 'podman',
        version: this.extractPodmanVersion(analysis),
        configFiles: this.getPodmanFiles(analysis),
        usage: this.analyzePodmanUsage(analysis)
      });
    }
    
    return detected;
  }
  
  private detectOrchestration(analysis: RepositoryAnalysis): OrchestrationTechnology[] {
    const detected: OrchestrationTechnology[] = [];
    
    // Kubernetes detection
    if (this.hasDirectory(analysis, 'k8s/') ||
        this.hasDirectory(analysis, 'kubernetes/') ||
        this.hasFilePattern(analysis, '*.yaml', 'apiVersion: apps/v1') ||
        this.hasFilePattern(analysis, '*.yml', 'kind: Deployment')) {
      detected.push({
        name: 'kubernetes',
        manifests: this.getKubernetesManifests(analysis),
        resources: this.analyzeKubernetesResources(analysis),
        namespaces: this.extractNamespaces(analysis)
      });
    }
    
    // OpenShift detection
    if (this.hasDirectory(analysis, '.s2i/') ||
        this.hasReference(analysis, 'openshift') ||
        this.hasFileContent(analysis, 'kind: DeploymentConfig') ||
        this.hasFileContent(analysis, 'kind: Route')) {
      detected.push({
        name: 'openshift',
        templates: this.getOpenShiftTemplates(analysis),
        buildConfigs: this.getBuildConfigs(analysis),
        routes: this.getRoutes(analysis)
      });
    }
    
    return detected;
  }
  
  private detectCICD(analysis: RepositoryAnalysis): CICDTechnology[] {
    const detected: CICDTechnology[] = [];
    
    // Tekton detection
    if (this.hasDirectory(analysis, '.tekton/') ||
        this.hasFileContent(analysis, 'apiVersion: tekton.dev') ||
        this.hasFilePattern(analysis, '*.yaml', 'kind: Pipeline')) {
      detected.push({
        name: 'tekton',
        pipelines: this.getTektonPipelines(analysis),
        tasks: this.getTektonTasks(analysis),
        triggers: this.getTektonTriggers(analysis)
      });
    }
    
    return detected;
  }
  
  private detectConfigManagement(analysis: RepositoryAnalysis): ConfigManagementTechnology[] {
    const detected: ConfigManagementTechnology[] = [];
    
    // Ansible detection
    if (this.hasFile(analysis, 'ansible.cfg') ||
        this.hasDirectory(analysis, 'playbooks/') ||
        this.hasDirectory(analysis, 'roles/') ||
        this.hasFile(analysis, 'inventory') ||
        this.hasFilePattern(analysis, '*.yml', 'hosts:') ||
        this.hasFilePattern(analysis, '*.yaml', 'tasks:')) {
      detected.push({
        name: 'ansible',
        playbooks: this.getAnsiblePlaybooks(analysis),
        roles: this.getAnsibleRoles(analysis),
        inventory: this.getAnsibleInventory(analysis),
        vaultFiles: this.getAnsibleVault(analysis)
      });
    }
    
    return detected;
  }
}

DevOps-Specific Content Templates and Generation

Key DevOps Documentation Patterns:

Container Tutorials: Project-specific Dockerfile optimization, multi-stage builds
Orchestration Guides: Kubernetes/OpenShift deployment strategies
Infrastructure as Code: Ansible playbooks for application deployment
CI/CD Pipelines: Tekton pipeline configuration and best practices

const DEVOPS_CONTENT_TEMPLATES = {
  docker: {
    tutorial: 'Containerizing {projectName} with Docker',
    howto: ['Optimize Docker Images', 'Debug Container Issues'],
    reference: 'Dockerfile Configuration Reference',
    explanation: 'Container Architecture Decisions'
  },
  kubernetes: {
    tutorial: 'Deploying {projectName} to Kubernetes',
    howto: ['Scale Applications', 'Troubleshoot Deployments'],
    reference: 'Kubernetes Manifest Specifications',
    explanation: 'Orchestration Strategy'
  },
  ansible: {
    tutorial: 'Infrastructure as Code with Ansible',
    howto: ['Automate Deployment', 'Manage Multi-Environment'],
    reference: 'Playbook and Role Reference',
    explanation: 'Configuration Management Strategy'
  },
  tekton: {
    tutorial: 'CI/CD Pipeline with Tekton',
    howto: ['Build and Deploy', 'Manage Secrets'],
    reference: 'Pipeline Specifications',
    explanation: 'Cloud Native CI/CD Architecture'
  }
};

function generateDevOpsContent(
  devopsProfile: DevOpsToolProfile,
  projectContext: ProjectContext
): DevOpsContentPlan {
  // Generate project-specific DevOps documentation
  // based on detected tools and project characteristics
}

Community Contribution Framework for Language and Tool Support

Language Extension Architecture

interface LanguageExtension {
  name: string;
  ecosystem: string;
  packageManagers: string[];
  detectionPatterns: DetectionPattern[];
  frameworks: FrameworkDefinition[];
  contentTemplates: LanguageContentTemplates;
  validationRules: ValidationRule[];
}

interface DetectionPattern {
  type: 'file' | 'dependency' | 'import' | 'content';
  pattern: string | RegExp;
  weight: number; // 1-10, higher = more confident
  description: string;
}

interface FrameworkDefinition {
  name: string;
  detectionPatterns: DetectionPattern[];
  contentTemplates: FrameworkContentTemplates;
  codeExamples: CodeExampleGenerator;
  bestPractices: BestPractice[];
}

Contribution Guidelines for New Language Support

Step 1: Language Detection Implementation

// Example: Adding Go language support
const GO_LANGUAGE_EXTENSION: LanguageExtension = {
  name: 'go',
  ecosystem: 'go',
  packageManagers: ['go mod', 'dep'],
  detectionPatterns: [
    {
      type: 'file',
      pattern: 'go.mod',
      weight: 10,
      description: 'Go module definition file'
    },
    {
      type: 'file',
      pattern: 'go.sum',
      weight: 8,
      description: 'Go module checksums'
    },
    {
      type: 'file',
      pattern: /.*\.go$/,
      weight: 6,
      description: 'Go source files'
    },
    {
      type: 'content',
      pattern: /^package main$/m,
      weight: 7,
      description: 'Go main package declaration'
    }
  ],
  frameworks: [
    // Framework definitions...
  ],
  contentTemplates: {
    // Content templates...
  }
};

Step 2: Framework-Specific Content Templates

// Example: Adding Gin framework support for Go
const GIN_FRAMEWORK: FrameworkDefinition = {
  name: 'gin',
  detectionPatterns: [
    {
      type: 'dependency',
      pattern: 'github.com/gin-gonic/gin',
      weight: 10,
      description: 'Gin framework dependency'
    },
    {
      type: 'import',
      pattern: 'gin "github.com/gin-gonic/gin"',
      weight: 9,
      description: 'Gin framework import'
    }
  ],
  contentTemplates: {
    tutorials: [
      {
        title: 'Building REST APIs with Gin',
        diataxisType: 'tutorial',
        sections: [
          'Setting up Gin Application',
          'Defining Routes and Handlers',
          'Middleware Configuration',
          'Database Integration',
          'Testing Gin Applications'
        ],
        prerequisites: [
          'Go installed (1.19+)',
          'Basic Go language knowledge',
          'Understanding of HTTP concepts'
        ],
        estimatedTime: '60 minutes',
        difficulty: 'beginner'
      }
    ],
    howToGuides: [
      {
        title: 'Optimize Gin Performance',
        diataxisType: 'how-to',
        tasks: [
          'Configure connection pooling',
          'Implement caching strategies',
          'Set up rate limiting',
          'Profile and benchmark endpoints'
        ]
      }
    ],
    reference: [
      {
        title: 'Gin Router Configuration',
        diataxisType: 'reference',
        sections: [
          'Route definition patterns',
          'Middleware registration',
          'Context object methods',
          'Error handling patterns'
        ]
      }
    ],
    explanation: [
      {
        title: 'Gin Architecture and Design Decisions',
        diataxisType: 'explanation',
        topics: [
          'HTTP router performance characteristics',
          'Middleware pipeline design',
          'Context lifecycle management',
          'Comparison with other Go frameworks'
        ]
      }
    ]
  },
  codeExamples: {
    basicServer: `package main

import (
    "net/http"
    "github.com/gin-gonic/gin"
)

func main() {
    r := gin.Default()
    
    r.GET("/health", func(c *gin.Context) {
        c.JSON(http.StatusOK, gin.H{
            "status": "healthy",
        })
    })
    
    r.Run(":8080")
}`,
    middleware: `func LoggerMiddleware() gin.HandlerFunc {
    return func(c *gin.Context) {
        start := time.Now()
        c.Next()
        duration := time.Since(start)
        log.Printf("%s %s %v", c.Request.Method, c.Request.URL.Path, duration)
    }
}`
  }
};

Step 3: Content Generation Logic

class GoContentGenerator implements FrameworkContentGenerator {
  detectFramework(analysis: RepositoryAnalysis): Framework[] {
    const frameworks: Framework[] = [];
    
    // Check for Gin framework
    if (this.hasGoModule(analysis, 'github.com/gin-gonic/gin')) {
      frameworks.push({
        name: 'gin',
        version: this.extractGoModuleVersion(analysis, 'github.com/gin-gonic/gin'),
        configFiles: this.getGinConfigFiles(analysis),
        routeStructure: this.analyzeGinRoutes(analysis)
      });
    }
    
    // Check for Echo framework
    if (this.hasGoModule(analysis, 'github.com/labstack/echo')) {
      frameworks.push({
        name: 'echo',
        version: this.extractGoModuleVersion(analysis, 'github.com/labstack/echo'),
        configFiles: this.getEchoConfigFiles(analysis),
        routeStructure: this.analyzeEchoRoutes(analysis)
      });
    }
    
    return frameworks;
  }
  
  generateFrameworkContent(framework: Framework, context: ProjectContext): FrameworkContent {
    const templates = GO_LANGUAGE_EXTENSION.frameworks
      .find(f => f.name === framework.name)?.contentTemplates;
    
    if (!templates) return this.generateGenericGoContent(framework, context);
    
    return this.populateTemplatesWithProjectContext(templates, framework, context);
  }
  
  private generateProjectSpecificGoDockerfile(context: ProjectContext): string {
    return `# Multi-stage build for ${context.projectName}
FROM golang:1.21-alpine AS builder

WORKDIR /app
COPY go.mod go.sum ./
RUN go mod download

COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -o main .

# Final stage
FROM alpine:latest
RUN apk --no-cache add ca-certificates
WORKDIR /root/
COPY --from=builder /app/main .
EXPOSE 8080
CMD ["./main"]`;
  }
}

Contribution Process and Standards

Community Contribution Workflow

Language Proposal: Submit GitHub issue with language/framework proposal
Detection Patterns: Define comprehensive detection patterns
Content Templates: Create Diataxis-compliant content templates
Code Examples: Provide working, project-specific code examples
Testing: Include validation tests for detection and generation
Documentation: Document contribution for future maintainers
Review Process: Community and maintainer review
Integration: Merge into main extension registry

Quality Standards for Contributions

interface ContributionStandards {
  detection: {
    minimumPatterns: 3;
    requiredTypes: ['file', 'dependency'];
    weightDistribution: 'balanced'; // No single pattern > 70% weight
    falsePositiveRate: '&lt;5%';
  };
  
  content: {
    diataxisCompliance: 'strict';
    tutorialCount: 'minimum 2';
    howToGuideCount: 'minimum 3';
    referenceCompleteness: '80%';
    explanationDepth: 'architectural decisions covered';
  };
  
  codeExamples: {
    compilationSuccess: '100%';
    projectSpecific: 'true';
    bestPractices: 'current industry standards';
    securityConsiderations: 'included';
  };
  
  testing: {
    detectionAccuracy: '&gt;90%';
    contentGeneration: 'functional tests';
    integrationTests: 'with existing systems';
    performanceImpact: '&lt;10% generation time increase';
  };
}

Template Contribution Format

// Required structure for new language contributions
interface LanguageContributionTemplate {
  metadata: {
    contributorName: string;
    contributorEmail: string;
    languageName: string;
    version: string;
    lastUpdated: string;
    maintenanceCommitment: 'ongoing' | 'initial-only';
  };
  
  detection: DetectionPatternSet;
  frameworks: FrameworkDefinition[];
  contentTemplates: ContentTemplateSet;
  validation: ValidationTestSuite;
  documentation: ContributionDocumentation;
}

// Example contribution file structure:
// src/languages/
//   ├── go/
//   │   ├── detection.ts
//   │   ├── frameworks/
//   │   │   ├── gin.ts
//   │   │   ├── echo.ts
//   │   │   └── fiber.ts
//   │   ├── templates/
//   │   │   ├── tutorials.ts
//   │   │   ├── howto.ts
//   │   │   ├── reference.ts
//   │   │   └── explanation.ts
//   │   ├── tests/
//   │   │   ├── detection.test.ts
//   │   │   └── generation.test.ts
//   │   └── README.md

Community Validation and Review Process

Automated Validation Pipeline

interface ContributionValidation {
  // Automated checks
  syntaxValidation: 'TypeScript compilation success';
  patternTesting: 'Detection accuracy against test repositories';
  contentValidation: 'Diataxis compliance checking';
  performanceImpact: 'Generation time benchmarking';
  
  // Community review
  peerReview: 'Two community developer approvals';
  maintainerReview: 'Core team architectural review';
  expertValidation: 'Language expert accuracy verification';
  
  // Integration testing
  endToEndTesting: 'Full workflow validation';
  regressionTesting: 'No impact on existing languages';
  documentationReview: 'Contribution documentation completeness';
}

Long-term Maintenance Framework

interface MaintenanceFramework {
  languageUpdates: {
    frameworkVersions: 'automated dependency tracking';
    newFrameworks: 'community contribution process';
    deprecatedPatterns: 'automated detection and flagging';
  };
  
  communityGovernance: {
    languageMaintainers: 'designated community experts';
    updateProcess: 'structured enhancement proposals';
    qualityAssurance: 'continuous validation and testing';
  };
  
  toolingSupport: {
    contributionCLI: 'automated scaffolding for new languages';
    validationTools: 'automated testing and verification';
    documentationGeneration: 'automated API documentation';
  };
}

Quality Assurance

Content Validation Framework

interface ContentValidator {
  validateAccuracy(content: GeneratedContent, analysis: RepositoryAnalysis): ValidationResult;
  checkDiataxisCompliance(content: GeneratedContent): ComplianceResult;
  verifyCodeExamples(examples: CodeExample[], projectContext: ProjectContext): ValidationResult;
  assessContentCompleteness(content: GeneratedContent, plan: ContentPlan): CompletenessResult;
}

interface ValidationResult {
  isValid: boolean;
  issues: ValidationIssue[];
  suggestions: ImprovementSuggestion[];
  confidence: number;
}

Testing Strategy

describe('ContentPopulationEngine', () => {
  describe('Tutorial Generation', () => {
    it('should generate appropriate getting started tutorial for Express.js project');
    it('should include technology-specific setup steps');
    it('should provide working code examples');
    it('should maintain Diataxis tutorial principles');
  });
  
  describe('Technology Detection', () => {
    it('should correctly identify primary framework from package.json');
    it('should detect database dependencies and generate appropriate content');
    it('should handle multi-framework projects appropriately');
  });
  
  describe('Content Quality', () => {
    it('should generate accurate code examples that match project structure');
    it('should maintain consistent tone and style across content types');
    it('should create appropriate cross-references between content sections');
  });
});

Performance Requirements

Content Generation Time: < 30 seconds for comprehensive population
Memory Usage: < 500MB for large repository analysis and content generation
Content Quality: 80%+ accuracy for generated technical content
Coverage: Support for 15+ major JavaScript/TypeScript frameworks initially

Integration Points

Repository Analysis Integration (ADR-002)

Leverage multi-layered analysis results for informed content generation
Use complexity assessment to determine content depth and sophistication
Integrate dependency analysis for framework-specific content selection

Diataxis Framework Integration (ADR-004)

Implement content planning intelligence outlined in ADR-004 lines 153-192
Generate content that strictly adheres to Diataxis category principles
Create appropriate cross-references and user journey flows

MCP Tools API Integration (ADR-006)

Add populate_diataxis_content as seventh core MCP tool
Maintain consistent error handling and response format patterns
Integrate with existing setup_structure tool for seamless workflow

SSG Configuration Integration (ADR-006)

Generate content with appropriate frontmatter for target SSG
Adapt content format and structure to SSG capabilities
Ensure generated content renders correctly across all supported SSGs

Future Enhancements

Advanced AI Integration

Large Language Model Integration: Use specialized models for content refinement
Code Analysis AI: Advanced analysis of project patterns for more accurate content
Natural Language Generation: Improve content quality and readability

Extended Technology Support

Python Ecosystem (Priority Implementation)

Web Frameworks: Django, Flask, FastAPI, Pyramid, Bottle
Data Science: Jupyter, Pandas, NumPy, SciPy documentation patterns
ML/AI: TensorFlow, PyTorch, Scikit-learn integration guides
API Development: Django REST Framework, FastAPI advanced patterns
Testing: pytest, unittest, behave testing documentation
Deployment: Gunicorn, uWSGI, Celery configuration guides

Additional Language Ecosystems

Go Ecosystem: Gin, Echo, Fiber, Buffalo framework support
Rust Ecosystem: Actix-web, Warp, Rocket, Axum content generation
Java Ecosystem: Spring Boot, Quarkus, Micronaut, Play Framework
C# Ecosystem: ASP.NET Core, Entity Framework, Blazor
Ruby Ecosystem: Rails, Sinatra, Hanami framework support
PHP Ecosystem: Laravel, Symfony, CodeIgniter patterns

DevOps and Infrastructure Expansion

Extended Container Support: Buildah, Skopeo, LXC/LXD integration
Advanced Orchestration: Nomad, Docker Swarm, Cloud Foundry support
CI/CD Platforms: Jenkins, GitLab CI, Azure DevOps, CircleCI integration
Infrastructure Tools: Terraform, Pulumi, CloudFormation content generation
Service Mesh: Istio, Linkerd, Consul Connect documentation patterns
Monitoring Stack: Prometheus, Grafana, ELK Stack, Jaeger integration guides

Community and Learning Features

Content Quality Feedback: User ratings and improvement suggestions
Template Sharing: Community-contributed content templates
Usage Analytics: Track which content types provide most value
Personalization: Adapt content style to team preferences and expertise level

Community Ecosystem and Contributions

Language Extension Registry: Centralized repository for community language support
Contribution Tooling: CLI tools for scaffolding new language extensions
Validation Pipeline: Automated testing and quality assurance for contributions
Community Governance: Language maintainer program and review processes
Documentation Portal: Comprehensive guides for extending DocuMCP capabilities
Template Marketplace: Sharing and discovery of specialized content templates

Enterprise Features

Custom Content Standards: Organization-specific content templates and style guides
Multi-language Support: Generate content in multiple languages
Integration APIs: Connect with existing documentation management systems
Approval Workflows: Review and approval processes for generated content

Success Metrics

User Value Metrics

Time to Usable Documentation: Target < 30 minutes (vs. 8-20 hours manually)
Content Completeness: 60-80% populated content out of the box
User Satisfaction: 85%+ positive feedback on generated content quality
Adoption Rate: 90%+ of users use content population vs. structure-only

Technical Metrics

Content Accuracy: 80%+ technical accuracy for generated code examples
Framework Coverage: Support 95% of detected JavaScript/TypeScript frameworks
DevOps Tool Coverage: Support 90% of detected containerization and orchestration tools
Performance: Content generation completes within 30 seconds
Error Rate: < 5% content generation failures

Business Metrics

Competitive Differentiation: Only tool providing intelligent content population
Market Position: Establish DocuMCP as "intelligent documentation assistant"
User Retention: Increase from documentation structure to full workflow adoption
Community Growth: Attract technical writers and documentation specialists

id: 008-intelligent-content-population-engine title: 'ADR-008: Intelligent Content Population Engine' sidebar_label: 'ADR-8: Intelligent Content Population Engine' sidebar_position: 8​

Status​

Context​

Decision​

Architecture Overview:​

1. Content Intelligence Engine​

2. Project-Aware Content Generators​

Tutorial Content Generator​

How-To Guide Generator​

Reference Documentation Generator​

Explanation Content Generator​

3. Repository Analysis Integration Layer​

Implementation Architecture:​

Content Generation Algorithms:​

Tutorial Generation Algorithm​

Reference Generation Algorithm​

Technology-Specific Content Templates:​

JavaScript/TypeScript Ecosystem​

Multi-Language Framework Support​

JavaScript/TypeScript Ecosystem​

Python Ecosystem Support​

Framework-Specific Content Generation​

Alternatives Considered​

Manual Content Creation Only​

AI-Generated Content via External APIs​

Community-Contributed Content Templates​

Generic Template Expansion​

Consequences​

Positive​

Negative​

Risks and Mitigations​

Implementation Details​

MCP Tool Interface​

Content Generation Pipeline​

Technology Detection and Content Mapping​

DevOps and Infrastructure Tooling Support​

DevOps Tool Detection and Content Generation​

DevOps-Specific Content Templates and Generation​

Community Contribution Framework for Language and Tool Support​

Language Extension Architecture​

Contribution Guidelines for New Language Support​

Step 1: Language Detection Implementation​

Step 2: Framework-Specific Content Templates​

Step 3: Content Generation Logic​

Contribution Process and Standards​

Community Contribution Workflow​

Quality Standards for Contributions​

Template Contribution Format​

Community Validation and Review Process​

Automated Validation Pipeline​

Long-term Maintenance Framework​

Quality Assurance​

Content Validation Framework​

Testing Strategy​

Performance Requirements​

Integration Points​

Repository Analysis Integration (ADR-002)​

Diataxis Framework Integration (ADR-004)​

MCP Tools API Integration (ADR-006)​

SSG Configuration Integration (ADR-006)​

Future Enhancements​

Advanced AI Integration​

Extended Technology Support​

Python Ecosystem (Priority Implementation)​

Additional Language Ecosystems​

DevOps and Infrastructure Expansion​

Community and Learning Features​

Community Ecosystem and Contributions​

Enterprise Features​

Success Metrics​

User Value Metrics​

Technical Metrics​

Business Metrics​

References​

id: 008-intelligent-content-population-engine title: 'ADR-008: Intelligent Content Population Engine' sidebar_label: 'ADR-8: Intelligent Content Population Engine' sidebar_position: 8

Status

Context

Decision

Architecture Overview:

1. Content Intelligence Engine

2. Project-Aware Content Generators

Tutorial Content Generator

How-To Guide Generator

Reference Documentation Generator

Explanation Content Generator

3. Repository Analysis Integration Layer

Implementation Architecture:

Content Generation Algorithms:

Tutorial Generation Algorithm

Reference Generation Algorithm

Technology-Specific Content Templates:

JavaScript/TypeScript Ecosystem

Multi-Language Framework Support

JavaScript/TypeScript Ecosystem

Python Ecosystem Support

Framework-Specific Content Generation

Alternatives Considered

Manual Content Creation Only

AI-Generated Content via External APIs

Community-Contributed Content Templates

Generic Template Expansion

Consequences

Positive

Negative

Risks and Mitigations

Implementation Details

MCP Tool Interface

Content Generation Pipeline

Technology Detection and Content Mapping

DevOps and Infrastructure Tooling Support

DevOps Tool Detection and Content Generation

DevOps-Specific Content Templates and Generation

Community Contribution Framework for Language and Tool Support

Language Extension Architecture

Contribution Guidelines for New Language Support

Step 1: Language Detection Implementation

Step 2: Framework-Specific Content Templates

Step 3: Content Generation Logic

Contribution Process and Standards

Community Contribution Workflow

Quality Standards for Contributions

Template Contribution Format

Community Validation and Review Process

Automated Validation Pipeline

Long-term Maintenance Framework

Quality Assurance

Content Validation Framework

Testing Strategy

Performance Requirements

Integration Points

Repository Analysis Integration (ADR-002)

Diataxis Framework Integration (ADR-004)

MCP Tools API Integration (ADR-006)

SSG Configuration Integration (ADR-006)

Future Enhancements

Advanced AI Integration

Extended Technology Support

Python Ecosystem (Priority Implementation)

Additional Language Ecosystems

DevOps and Infrastructure Expansion

Community and Learning Features

Community Ecosystem and Contributions

Enterprise Features

Success Metrics

User Value Metrics

Technical Metrics

Business Metrics

References