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