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/AssistentPrompt.md

@@ -0,0 +1,154 @@
+ResearchPrompt
+
+*Research a topic...*
+
+# InfoTechPrimer – ResearchPrompt
+
+Below is a **single, reusable, high-precision research prompt** you can use to *systematically get a grip on any InfoTech topic* before writing an **InfoTechPrimer**.
+
+> **Purpose:**
+> Produce a *factually grounded, scope-aware, source-anchored research brief* suitable as the direct input for authoring an InfoTechPrimer.
+
+It is designed to work well with:
+
+* General-purpose LLMs
+* Research-oriented agents
+* Human-in-the-loop review
+
+The prompt is **topic-agnostic**, but forces rigor, boundaries, and source grounding.
+
+---
+
+## ResearchPrompt
+
+```
+You are conducting foundational research for an InfoTechPrimer.
+
+Topic: <$topic>
+
+The goal is NOT to teach or promote, but to establish a precise, shared understanding
+of the topic for experienced IT professionals and AI systems.
+
+Produce a structured research brief that answers the following sections.
+Be concise, factual, and source-driven.
+Avoid tutorials, opinions, and vendor marketing language.
+
+---
+
+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.
+
+---
+
+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 Prompt Works Well
+
+This prompt 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 your Primer Authoring Rules**
+
+You can think of it as:
+
+> *A pre-primer that de-risks the primer.*
+
+---
+
+## How You’ll Likely Use It in Practice
+
+Typical flow:
+
+1. Run this prompt on a topic (e.g. CUDA, OAuth 2.0, OpenQASM)
+2. Review and correct factual issues
+3. Collapse the research brief into the Primer schema
+4. Apply brand tone + structural rules
+5. Publish as an InfoTechPrimer
+
+xxx