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/artifacts/guidelines/research-prompt.md

@@ -0,0 +1,104 @@
+---
+id: research-protocol-v1
+name: ResearchPrompt
+artifact_type: content
+description: Systematic research protocol for InfoTech topic investigation
+version: 1.0.0
+tags:
+  - research
+  - methodology
+  - guidelines
+---
+
+# InfoTech Research Protocol
+
+Below is a systematic research protocol to thoroughly investigate any InfoTech topic before writing an InfoTechPrimer.
+
+**Purpose:** Produce a factually grounded, scope-aware, source-anchored research brief suitable as direct input for primer authoring.
+
+---
+
+## Research Sections
+
+### 1. Canonical Definition
+- Provide the most widely accepted definition(s) of the topic
+- If multiple definitions exist, explain why and in which contexts they differ
+- Prefer definitions from standards bodies, original designers, or official specifications
+
+### 2. Domain Context and Classification
+- Which technical domain(s) does this topic belong to?
+  (e.g. systems programming, distributed systems, security, AI, quantum computing)
+- What *type* of thing is it?
+  (e.g. protocol, framework, architectural style, API standard, SDK, language, library)
+- At which abstraction level does it primarily operate?
+
+### 3. Historical Origin and Motivation
+- Who introduced it and when?
+- What concrete problem(s) was it created to solve?
+- What existing approaches did it replace, extend, or formalize?
+
+(Only include history that explains intent or constraints.)
+
+### 4. Core Concepts and Invariants
+- List the essential concepts without which the topic would not make sense
+- For each concept, explain its role in one or two sentences
+- Identify any invariants, guarantees, or formal assumptions
+
+### 5. Scope Boundaries
+- Clearly state what the topic explicitly covers
+- Clearly state what it explicitly does NOT cover
+- Identify common misconceptions or misuses
+
+This section should prevent overextension by AI systems.
+
+### 6. Practical Implications (Non-Tutorial)
+- What design or architectural consequences follow from using this?
+- What tradeoffs are inherent?
+- What kinds of systems typically depend on it?
+
+Do NOT include step-by-step usage.
+
+### 7. Relationship to Adjacent Concepts
+- List closely related standards, technologies, or terms
+- For each, explain the relationship (complementary, layered on top, alternative, predecessor)
+
+### 8. Authoritative Sources
+- List primary, authoritative references:
+  - Standards (RFCs, ISO, W3C, IEEE, etc.)
+  - Official specifications or documentation
+  - Foundational papers
+- Include direct links
+- Clearly distinguish primary sources from secondary explanations
+
+### 9. Stability and Maturity Assessment
+- Is this topic considered stable, evolving, or experimental?
+- Are there competing standards or dominant implementations?
+- Is backward compatibility a concern?
+
+### 10. Notes for Primer Authoring
+- Highlight points that MUST be stated clearly in a primer
+- Highlight areas where ambiguity must be avoided
+- Identify terminology that must be used consistently
+
+---
+
+## Research Constraints
+
+- Use precise, declarative language
+- No metaphors or analogies
+- No marketing or opinionated statements
+- Assume a technically literate audience
+- Prefer explicit statements over implied assumptions
+
+---
+
+## Why This Protocol Works
+
+This protocol is intentionally shaped to:
+* **Force scope clarity** (critical for AI agents)
+* **Surface invariants and constraints**
+* **Separate definition from implementation**
+* **Anchor everything in primary sources**
+* **Produce output that maps 1:1 to Primer Authoring Rules**
+
+Think of it as: *A pre-primer that de-risks the primer.*