Magnus HedemarkandGitHub
9917fb455b
* feat(data): validate AI transformation and repair boundaries * docs(data): expose AI transformation and repair triggers * docs(data-engineering): preserve the full reference index
Data Engineering
Data engineering methodology — database operations (vector, relational, graph, time-series), ETL/ELT pipeline design (dbt patterns, incremental loading), SQL analytical patterns, data quality monitoring, schema migration, and storage infrastructure management. Grounded in operational patterns for production data systems.
Why Install This Skill
Your agent gets operational patterns for production data systems — real SQL, dbt models, backup commands, and migration strategies instead of textbook theory.
What You Get
| Directory | Purpose |
|---|---|
SKILL.md |
Core methodology, trigger conditions, reference index |
references/ |
Database operations, analytical SQL, pipelines, quality, migrations, recovery, and AI transformation boundaries |
templates/ai-stage-contract.md |
Pilot, version, retry, budget and publication evidence for an AI stage |
For AI-assisted data work, the boundary workflow helps keep uncertain proposals out of trusted datasets. Use the companion record to retain validation and review evidence.
Triggers
Designing ETL/ELT pipelines, writing analytical SQL, operating vector/graph/time-series databases, planning migrations, setting up data quality monitoring, or defining validated model-assisted transformation boundaries.
Requirements
Platform-agnostic. References cover PostgreSQL, DuckDB, ClickHouse, BigQuery, Snowflake, Neo4j, InfluxDB, TimescaleDB, and dbt.
Quick Start
Start with a concrete pipeline or dataset boundary. For an AI-assisted transformation, fill in templates/ai-stage-contract.md with its input keys, validation rules, retry policy and publication conditions before running a pilot.