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/authoring-rules.md

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+---
+id: primer-authoring-rules-v1
+name: AuthoringRules
+artifact_type: content
+description: Comprehensive guidelines for writing effective InfoTech primers
+version: 1.0.0
+tags:
+  - guidelines
+  - authoring
+  - quality-standards
+---
+
+# Primer Authoring Rules
+
+**Status:** Draft
+**Intended Audience:** Human authors and AI systems generating or validating primers
+**Purpose:** Ensure primers are precise, stable, and suitable as shared context for humans and AI agents
+
+---
+
+## 1. What a Primer Is (Normative)
+
+An **InfoTechPrimer** is a **short, structured reference document** that establishes a **shared understanding** of a specific IT term, standard, method, or concept.
+
+A primer:
+* Defines **what the topic is**
+* Explains **where it fits**
+* Clarifies **scope boundaries**
+* Points to **authoritative sources**
+
+A primer does **not**:
+* Teach step-by-step usage
+* Advocate tools or vendors
+* Explore implementation details beyond what is normatively defined
+
+---
+
+## 2. Target Audience
+
+Primers are written for:
+* Humans with solid general IT knowledge
+* Readers who are *not specialists* in the specific topic
+* AI systems that consume structured context for reasoning and coding
+
+Authors must assume:
+* Conceptual literacy
+* Familiarity with basic IT terminology
+* No prior deep knowledge of the topic
+
+---
+
+## 3. Required Structure (Mandatory)
+
+Every primer **MUST** contain the following sections **in this order**:
+
+1. **Definition**
+2. **Context**
+3. **Core Concepts**
+4. **Scope and Non-Scope**
+5. **Practical Implications**
+6. **Formal Standards and Authoritative Sources**
+7. **Related Concepts**
+
+No section may be omitted. Empty sections are not allowed.
+
+---
+
+## 4. Section Authoring Rules
+
+### 4.1 Definition
+**Purpose:** Establish an unambiguous baseline meaning.
+
+Rules:
+* 2–4 sentences maximum
+* Declarative, precise language
+* No metaphors, examples, or analogies
+* No historical narrative
+
+### 4.2 Context
+**Purpose:** Position the concept within the IT landscape.
+
+Rules:
+* Describe the domain(s) the concept belongs to
+* Explain *why it exists*, not *how to use it*
+* Historical notes allowed only if they clarify intent or constraints
+
+### 4.3 Core Concepts
+**Purpose:** Identify the irreducible ideas that define the topic.
+
+Rules:
+* Bullet points only
+* Each bullet describes one concept
+* No nested lists
+* Avoid redundancy with Definition
+
+### 4.4 Scope and Non-Scope
+**Purpose:** Prevent conceptual drift and misuse.
+
+Rules:
+* Explicitly list inclusions and exclusions
+* Use parallel structure
+* Address common misconceptions
+
+Format:
+```markdown
+**In Scope**
+- ...
+
+**Out of Scope**
+- ...
+```
+
+This section is **critical** for AI agent correctness.
+
+### 4.5 Practical Implications
+**Purpose:** Describe consequences of adopting or interacting with the concept.
+
+Rules:
+* Focus on effects, not instructions
+* No step-by-step guidance
+* Include tradeoffs where relevant
+
+### 4.6 Formal Standards and Authoritative Sources
+**Purpose:** Anchor the primer in canonical truth.
+
+Rules:
+* Prefer primary sources
+* Include direct links
+* Avoid blogs unless widely recognized and necessary
+
+Acceptable sources:
+* RFCs, W3C Recommendations
+* ISO / IEC standards
+* NIST publications
+* Official specifications
+* Foundational academic papers
+
+At least **one** authoritative source is required.
+
+### 4.7 Related Concepts
+**Purpose:** Enable semantic navigation.
+
+Rules:
+* Short descriptions only (one line per concept)
+* No deep explanations
+* Avoid circular definitions
+
+---
+
+## 5. Language and Style Rules
+
+Mandatory:
+* Present tense
+* Declarative sentences
+* Neutral, technical tone
+
+Avoid:
+* First-person language ("we", "you")
+* Rhetorical questions
+* Marketing language
+* Informal phrasing
+* Emojis
+
+---
+
+## 6. Length Constraints
+
+A primer should typically be:
+* **600–1,000 words total**
+* Short enough to be read in one sitting
+* Long enough to define boundaries clearly
+
+Exceeding this range requires justification.
+
+---
+
+## 7. AI Optimization Rules (Explicit)
+
+Authors **SHOULD**:
+* Use consistent terminology
+* Avoid synonyms for core terms once defined
+* Prefer explicit over implicit assumptions
+* State constraints clearly
+
+Authors **MUST NOT**:
+* Rely on context outside the document
+* Assume tool- or framework-specific defaults
+* Leave ambiguity where standards are explicit
+
+---
+
+## 8. Validation Criteria (Checklist)
+
+A primer is valid if:
+* [ ] All required sections are present
+* [ ] Definition is precise and unambiguous
+* [ ] Scope boundaries are explicit
+* [ ] At least one authoritative source is linked
+* [ ] No tutorial or marketing content exists
+* [ ] Language follows declarative style rules

examples/content-generator/artifacts/guidelines/research-prompt.md

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+---
+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.*