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This example demonstrates the full workflow of generating InfoTech primers
using MarkiTect's Prompt Dependency Resolution infrastructure.
Features demonstrated:
- Artifact creation and storage with content-based addressing
- PromptTemplate with @{macro} resolution across multiple spaces
- Automatic dependency tracking and graph construction
- Provenance tracing from outputs back to inputs
- Visualization export (Mermaid format)
- Incremental execution with change detection
Files added:
- generate_primers.py: Complete working example
- README.md: Quick start guide and architecture overview
- TUTORIAL.md: Comprehensive 500+ line tutorial
- templates/generate-primer.md: Template with macros
- artifacts/topics/: ETL and Microservices topic definitions
- artifacts/guidelines/: Authoring rules and research protocol
- prepdr/: Original manual system (preserved for reference)
Example output:
- Generates 2 primers (ETL, Microservices)
- Creates 8 artifacts across 4 information spaces
- Records 8 dependency edges in SQLite database
- Exports dependency graph visualization
Run with: cd examples/content-generator && python generate_primers.py
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
MarkiTect Documentation
Welcome to the MarkiTect documentation. This directory contains comprehensive documentation for developers, users, and contributors.
Documentation Structure
📐 Architecture Documentation (architecture/)
Deep technical documentation about system design, performance, and implementation details.
- Capabilities Architecture - Critical: How capabilities work as independent git submodules and separation of concerns
- Caching System - Why and how MarkiTect's AST caching delivers 60-85% performance improvements
- Coming soon: Database Schema, CLI Architecture
👥 User Guides (user-guides/)
End-user documentation for working with MarkiTect CLI and features.
- Coming soon: Getting Started, Command Reference, Best Practices
🔧 Development Documentation (development/)
Documentation for contributors and developers extending MarkiTect.
- Coming soon: Contributing Guide, Testing Strategy, Release Process
Quick Links
For Users
- Installation & Setup
- Command Reference (coming soon)
- Performance Guide (coming soon)
For Developers
- Architecture Overview - System design and component relationships
- Development Setup - Local development environment
- API Documentation (coming soon)
Project Management
- Project Status - Current development status
- Roadmap - Strategic development plan
- Current Tasks - Task management using Keep a Todofile format
Key Concepts
Core Architecture Principles
- Parse Once, Use Many Times - AST caching for 60-85% performance improvement
- Convention Over Configuration - Sensible defaults with minimal setup
- Schema-Driven Processing - Structured markdown with validation
- Relational Metadata - Database-powered document relationships
Performance Philosophy
MarkiTect treats markdown documents as structured, queryable data rather than plain text. This approach enables:
- Lightning-fast document processing through intelligent caching
- Complex querying and relationship management
- Schema validation and consistency enforcement
- Scalable performance that grows with your content
Contributing to Documentation
Documentation follows the same quality standards as code:
- Clear Structure - Logical organization and navigation
- Practical Examples - Real-world usage patterns
- Performance Context - Why architectural decisions matter
- User-Focused - Written for the intended audience
Documentation Standards
- Use clear, concise language
- Include practical examples
- Explain the "why" behind design decisions
- Keep technical accuracy as the highest priority
- Update docs when changing functionality
This documentation is maintained alongside the codebase. For the most current information, always refer to the latest version in the repository.