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Route methodology skills to real catalog skills instead of dead role names: - platform-engineering: docker-management -> docker-compose - backend-engineering: technical-architect -> api-design-and-evolution, reviewer -> qa-methodology - frontend-engineering: ux-designer -> product-design-and-ux, technical-architect -> api-design-and-evolution, writer -> neutral, reviewer -> qa-methodology - security-audit-methodology: reviewer -> qa-methodology, debugger -> systematic-debugging - data-engineering: UX designer -> product-design-and-ux - technical-documentation: writer -> neutral, technical architect -> software-architecture-analysis, debugger -> systematic-debugging, reviewer -> qa-methodology - operational-design: executive-methodology -> strategy-frameworks - technical-documentation/references/agent-facing-docs.md: drop dead deployment-methodology skill_view example Also bring modified skills' descriptions up to the quality validator's imperative-verb + negative-boundary requirement and regenerate catalogs. agent-council's external hermes-profiles list is retained unchanged. Closes #234 Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
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name, description, license, metadata
| name | description | license | metadata | ||||
|---|---|---|---|---|---|---|---|
| data-engineering | Design and operate data infrastructure — 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. Do not use for statistical analysis or ML model development. | MIT |
|
Data Engineering Methodology
Data engineering is the operational backbone of data-driven systems. This methodology covers running, maintaining, and evolving data infrastructure — from relational databases and vector stores to graph databases, time-series stores, and the transformation pipelines that move data between them.
The Data Engineer's Domain
| You own | You don't own |
|---|---|
| Database operations — schema management, indexing, backup/recovery, migration across relational, vector, graph, and time-series stores | Data modeling and schema design — that's the data architect |
| Data transformation pipelines — dbt models, ETL/ELT patterns, incremental loading, incremental strategies | Statistical analysis and experiments — that's the data scientist |
| Analytical SQL — window functions, CTEs, query optimization, execution plan analysis, star schema queries | Training infrastructure and model deployment — that's the ML engineer |
| Graph database operations — Neo4j data modeling, Cypher queries, graph algorithms, import/export | Application-level data access patterns — that's the developer |
| Time-series database operations — InfluxDB schema design, downsampling, retention policies, Telegraf | Infrastructure provisioning — that's the platform engineer |
| Data quality monitoring — integrity checks, deduplication, anomaly detection, freshness validation | Visual dashboard design — that's the analyst / product-design-and-ux |
| Storage infrastructure — capacity planning, performance tuning, archival strategies |
Reference Files
| Reference | When to load |
|---|---|
references/sql-analytical-patterns.md |
Writing analytical SQL — window functions, CTEs, execution plan reading, star schema queries, engine-specific optimization (PostgreSQL, DuckDB, ClickHouse, BigQuery, Snowflake) |
references/dbt-patterns.md |
Designing data transformation pipelines with dbt — project structure, modeling layers (staging/intermediate/facts/dimensions), materializations, tests, snapshots, Jinja macros, CI/CD, dbt Mesh |
references/etl-pipeline-design.md |
Building reliable data pipelines — extraction strategies (full, incremental, CDC), transformation layers, validation gates, error handling, idempotency |
references/data-quality.md |
Monitoring data integrity — quality dimensions, validation rule types, anomaly detection, deduplication strategies, pipeline health signals |
references/graph-databases.md |
Working with graph databases — Neo4j data modeling, Cypher query patterns (traversal, aggregation, pathfinding), import strategies, graph algorithms, pipeline integration |
references/time-series-databases.md |
Working with time-series databases — InfluxDB data model (measurements, tags, fields), schema design (cardinality), downsampling, retention, Telegraf ingest, comparison with TimescaleDB/QuestDB/Prometheus |
references/vector-db-operations.md |
Managing vector databases — Milvus, Qdrant, Chroma — index types, collection lifecycle, dimension migrations, backup strategies |
references/database-migrations.md |
Schema evolution — zero-downtime migration patterns, rollback planning, versioned schemas, test-first migrations |
references/backup-and-recovery.md |
Backup strategies per data store type, RPO/RTO planning, WAL archiving, snapshot management, recovery plan template |
Core Principles
Data without integrity is noise — No pipeline, model, or dashboard is worth more than the quality of the data feeding it. Validate at every boundary.
Design for operability — Every database, pipeline, and store needs monitoring, backup, and recovery procedures defined before it goes to production. If you can't detect failure, you can't recover from it.
Idempotency is a requirement — Every pipeline should produce the same result whether it runs once or twice. Duplicate handling is not optional.
Schema changes are code changes — Every migration needs review, testing, and a rollback plan. Schema drift is technical debt with compounding interest.
Know your storage characteristics — Access patterns, retention requirements, growth rates, and consistency guarantees determine the right storage architecture. Choose based on data, not familiarity.