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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>