Agent skill
tech-stack
Describes the primary technologies, frameworks, libraries, and language conventions used in this codebase.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/tech-stack
SKILL.md
Tech Stack Overview
Language-Specific Conventions
- Python: See PYTHON.md for detailed conventions and examples
Primary Languages
| Language | Usage | Version |
|---|---|---|
| Python | datagen, notebooks | 3.10+ |
| PySpark | Fabric notebooks | Spark 3.x |
| KQL | Eventhouse queries | N/A |
| JSON/YAML | Fabric item definitions | N/A |
Frameworks & Libraries
Data Generation (datagen)
- DuckDB: Local analytical database for historical data
- Faker: Synthetic data generation
- Pydantic: Data validation and event schemas
- azure-eventhub: Event streaming to Azure
Lakehouse
- Delta Lake: ACID transactions, schema enforcement
- PySpark: Distributed data processing
Real-Time Analytics
- Microsoft Fabric Eventhouse: KQL-based analytics
- Eventstream: Event routing and transformation
Project Architecture
Event Flow:
datagen (Python)
→ Azure Event Hubs
→ Eventstream
→ KQL Tables + Lakehouse Bronze
Data Layers:
Bronze (raw JSON)
→ Silver (typed Delta)
→ Gold (aggregated Delta)
→ Semantic Model (Power BI)
Key Dependencies
See datagen/pyproject.toml for Python dependencies.
Core packages:
pydantic- Schema validationduckdb- Local analyticsfaker- Data generationazure-eventhub- Event streaming
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