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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>
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id, name, artifact_type, description, version, tags
| id | name | artifact_type | description | version | tags | |||
|---|---|---|---|---|---|---|---|---|
| topic-etl | ETL | content | Topic definition for ETL (Extract, Transform, Load) | 1.0.0 |
|
ETL (Extract, Transform, Load)
A three-phase computing process where data is extracted from source systems, transformed (including validation, cleaning, enrichment, and aggregation), and loaded into a target data store or data warehouse.
ETL is a fundamental pattern in data integration and analytics pipelines, enabling organizations to consolidate data from heterogeneous sources into a unified format suitable for analysis and reporting.
Key Characteristics:
- Sequential batch-oriented processing
- Data quality enforcement during transformation
- Schema mapping and normalization
- Support for diverse source and target systems
- Typically scheduled and automated
Common Use Cases:
- Data warehouse population
- Business intelligence reporting
- Data migration projects
- Master data management
- Regulatory compliance reporting