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>
3.6 KiB
3.6 KiB
id, name, artifact_type, description, version, tags
| id | name | artifact_type | description | version | tags | |||
|---|---|---|---|---|---|---|---|---|
| research-protocol-v1 | ResearchPrompt | content | Systematic research protocol for InfoTech topic investigation | 1.0.0 |
|
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.