Magnus Hedemarkandfactory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
4a5f18e435
Add references/agent-evals.md documenting the official supabase/evals harness: eval/experiment concepts, the tools and local-stack runtimes, run and result-viewing commands, and a mapping of harness scenarios to the skill's operating references. Route to it from the supabase "Choose the path" table and from postgres, agent-evals-and-observability, backend-engineering, and data-engineering. Add two eval cases covering the new reference and keep the generated catalog artifacts current. Closes #271 Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
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/ |
Deep-dive reference files loaded on demand |
Triggers
Designing ETL/ELT pipelines, writing analytical SQL, operating vector/graph/time-series databases, planning migrations, or setting up data quality monitoring.
Requirements
Platform-agnostic. References cover PostgreSQL, DuckDB, ClickHouse, BigQuery, Snowflake, Neo4j, InfluxDB, TimescaleDB, and dbt.
Quick Start
Load SKILL.md for the methodology overview and reference table, then load specific references as needed for the task at hand.