feat(examples): add content-generator example demonstrating Prompt Dependency Resolution

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>

examples/content-generator/prepdr/GeneratePrimerTemplate.md

@@ -0,0 +1,23 @@
+GeneratePrimerTemplate
+
+*Program to research infotech topics*
+
+# Generate Primer
+
+Assist me to the best of your ability adressing the $topic>, $guidance and $protoprompt I will be providing. 
+If something is unclear show me the arguments and prompt for refinement, before starting.
+
+<topic>
+{{ETL}}
+</topic>
+
+<guidance>
+{{AuthoringRules}}
+</guidance> 
+
+<protoprompt>
+{{ResearchPrompt}}
+</protoprompt>
+
+
+xxx