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/README.md

@@ -0,0 +1,258 @@
+# Content Generator Example
+
+**Demonstrates: Prompt Dependency Resolution for Systematic Content Generation**
+
+This example shows how to use MarkiTect's Prompt Dependency Resolution infrastructure to generate InfoTech primers with full dependency tracking, provenance tracing, and quality validation.
+
+## Quick Start
+
+```bash
+# Run the example
+python generate_primers.py
+
+# Or if executable:
+./generate_primers.py
+```
+
+## What This Example Demonstrates
+
+1. **Artifact Management**: Store templates, topics, and guidelines as versioned artifacts
+2. **Dependency Resolution**: Automatically resolve `@{macro}` references in templates
+3. **Provenance Tracking**: Trace generated content back to its inputs
+4. **Incremental Updates**: Detect changes and regenerate only affected outputs
+5. **Quality Validation**: Apply gates to ensure output meets standards
+6. **Visualization**: Export dependency graphs to Mermaid or DOT format
+
+## Files
+
+```
+content-generator/
+├── README.md                    # This file
+├── TUTORIAL.md                  # Comprehensive tutorial (START HERE)
+├── generate_primers.py          # Example implementation
+│
+├── templates/
+│   └── generate-primer.md       # PromptTemplate for primer generation
+│
+├── artifacts/
+│   ├── topics/                  # Topic definitions
+│   │   ├── etl.md
+│   │   └── microservices.md
+│   │
+│   └── guidelines/              # Authoring standards
+│       ├── authoring-rules.md
+│       └── research-prompt.md
+│
+└── prepdr/                      # Original manual system (preserved)
+    ├── README.md
+    ├── ETL.md
+    ├── AuthoringRules.md
+    ├── AssistentPrompt.md
+    └── GeneratePrimerTemplate.md
+```
+
+## Before vs After
+
+### Before (prepdr/ - Manual System)
+
+```markdown
+# Manual macro substitution
+{{ETL}}
+{{AuthoringRules}}
+```
+
+Problems:
+- No automation
+- No version tracking
+- No dependency awareness
+- Can't detect changes
+- No traceability
+
+### After (With Infrastructure)
+
+```markdown
+# Automatic resolution
+@{topic}
+@{authoring_rules}
+```
+
+Benefits:
+- ✅ Automatic macro resolution
+- ✅ SHA-256 content digests for change detection
+- ✅ Full dependency graph
+- ✅ Incremental recomputation
+- ✅ Complete provenance traces
+- ✅ CLI tools for inspection
+
+## Tutorial
+
+**[Read the full tutorial →](TUTORIAL.md)**
+
+The tutorial covers:
+- Architecture and core concepts
+- Step-by-step implementation walkthrough
+- Advanced features (incremental execution, quality gates, visualization)
+- CLI usage examples
+- Best practices
+
+## CLI Commands
+
+After running the example, you can use CLI commands to inspect the system:
+
+```bash
+# Trace provenance
+markitect prompt trace <artifact-id> --database primers.db
+
+# Visualize dependencies
+markitect prompt graph <artifact-id> --format mermaid --database primers.db
+
+# List runs
+markitect prompt runs --database primers.db
+
+# Show impact debt (stale artifacts)
+markitect prompt debt --database primers.db
+
+# Graph statistics
+markitect prompt stats --database primers.db
+```
+
+## Architecture
+
+```mermaid
+graph TB
+    A[Topic: ETL] -->|requires| D[Run: generate-etl]
+    B[Authoring Rules] -->|requires| D
+    C[Research Prompt] -->|requires| D
+    D -->|generates| E[Output: ETL Primer]
+
+    style E fill:#90EE90
+    style D fill:#87CEEB
+```
+
+## Key Features
+
+### 1. Content-Based Addressing
+
+Every artifact has a SHA-256 digest that changes when content changes:
+
+```python
+artifact.content_digest  # "9c4d6e8a..."
+artifact.has_changed(new_digest)  # True if modified
+```
+
+### 2. Dependency Graph
+
+Full graph construction enables:
+- **Impact analysis**: "What needs regeneration?"
+- **Provenance**: "How was this produced?"
+- **Build order**: Topological sort for correct execution
+
+### 3. Incremental Execution
+
+Only regenerate what's affected by changes:
+
+```python
+config = RecomputeConfig(
+    max_depth=2,              # Traverse 2 levels
+    impact_threshold=0.1,     # Skip minor changes
+    max_recomputes=10,        # Budget limit
+)
+```
+
+### 4. Quality Validation
+
+Apply automated checks:
+
+```python
+gate = PatternValidationGate(
+    required_patterns=[r"## Definition", r"## Context"],
+    forbidden_patterns=[r"TODO", r"FIXME"],
+)
+```
+
+## Extending the Example
+
+### Add New Topics
+
+```bash
+# 1. Create artifact file
+cat > artifacts/topics/oauth.md << 'EOF'
+---
+id: topic-oauth
+name: OAuth
+artifact_type: content
+---
+
+# OAuth 2.0
+
+An authorization framework that enables...
+EOF
+
+# 2. Run generator
+python generate_primers.py
+# Automatically picks up new topic
+```
+
+### Custom Quality Gates
+
+```python
+from markitect.prompts.quality.gates.schema_gate import SchemaValidationGate
+
+# Validate primer structure
+schema = {
+    "type": "object",
+    "required": ["definition", "context", "scope"],
+}
+
+gate = SchemaValidationGate(schema=schema, gate_id="primer-schema")
+```
+
+### Integrate with LLM
+
+```python
+# Replace mock generation with real LLM call
+from openai import OpenAI
+
+client = OpenAI()
+response = client.chat.completions.create(
+    model="gpt-4",
+    messages=[{"role": "user", "content": compiled.content}]
+)
+
+output_content = response.choices[0].message.content
+```
+
+## Performance
+
+Database operations are optimized:
+- Indexed lookups on artifact ID, space, digest
+- Indexed dependency queries by source, target, run
+- Efficient graph traversal with BFS/DFS
+- Content digest comparison (no full content comparison)
+
+## Troubleshooting
+
+### "Artifact not found"
+- Ensure artifact files exist in correct directories
+- Check file extension is `.md`
+- Verify space IDs match between template and config
+
+### "Resolution failed"
+- Check macro names match artifact names
+- Verify spaces are configured in ResolutionConfig
+- Use `ResolutionStrategy.FIRST_MATCH` for simplicity
+
+### "Circular dependency detected"
+- Review dependency edges in database
+- Use `detect_circular_dependencies()` to find cycles
+- Refactor template structure to break cycles
+
+## Related Documentation
+
+- **[TUTORIAL.md](TUTORIAL.md)** - Comprehensive walkthrough
+- **[Prompt Dependency Resolution Spec](../../roadmap/prompt-dependency-resolution/)** - Design documentation
+- **[Phase 8 Implementation](../../markitect/prompts/)** - Source code
+
+## License
+
+Same as MarkiTect project.