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* feat(data): validate AI transformation and repair boundaries * docs(data): expose AI transformation and repair triggers * docs(data-engineering): preserve the full reference index
32 lines
1.8 KiB
Markdown
32 lines
1.8 KiB
Markdown
# Data Engineering
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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.
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## Why Install This Skill
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Your agent gets operational patterns for production data systems — real SQL, dbt models, backup commands, and migration strategies instead of textbook theory.
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## What You Get
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| Directory | Purpose |
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|-----------|---------|
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| `SKILL.md` | Core methodology, trigger conditions, reference index |
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| `references/` | Database operations, analytical SQL, pipelines, quality, migrations, recovery, and AI transformation boundaries |
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| `templates/ai-stage-contract.md` | Pilot, version, retry, budget and publication evidence for an AI stage |
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For AI-assisted data work, the [boundary workflow](references/ai-transformation-boundaries.md) helps keep uncertain
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proposals out of trusted datasets. Use the [companion record](templates/ai-stage-contract.md) to retain
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validation and review evidence.
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## Triggers
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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.
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## Requirements
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Platform-agnostic. References cover PostgreSQL, DuckDB, ClickHouse, BigQuery, Snowflake, Neo4j, InfluxDB, TimescaleDB, and dbt.
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## Quick Start
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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.
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