Databases Data Orchestrator

Databases & Data Orchestrator

The single entry skill for data-layer work. It locates the task on the engine × concern map and delegates to one of the specialist spokes. The cross-cutting decision every data task starts from — which store fits this workload (OLTP vs OLAP vs cache vs ORM-managed) — plus the conventions every spoke shares (index choice, ID strategy, pooling, migration safety, idempotent writes) lives in databases-data-core; read it before picking a store or planning a schema change.

Routing map (intent → spoke)

Pick & design a relational store (OLTP)

• PostgreSQL schema, indexes, RLS, pooling, query tuning → postgres-patterns
• MySQL / MariaDB schema, replication, deadlocks, connection pools → mysql-patterns
• TypeScript backend going through an ORM → prisma-patterns (traps: updateMany returns count, migrate dev resets the DB, serverless pool exhaustion)

Analytics & time-series (OLAP)

• Column-store schema, MergeTree, materialized views, batch ingest → clickhouse-io

Cache, locks, queues, rate limits (in-memory)

• Caching, distributed locks, rate limiting, pub/sub, streams → redis-patterns

Change the schema safely

• Zero-downtime DDL, backfills, rollbacks, expand/contract across PG/MySQL/ORMs → database-migrations (model in databases-data-core)

Move data fast & correctly

• Large ingestion, backfill, export, ETL, warehouse load, manifest catch-up → data-throughput-accelerator

Routing rules by intent

• "Which database should I use?" → start in databases-data-core §1 (the OLTP/OLAP/cache/ORM decision), then the matching engine spoke.
• "My query is slow." → the engine spoke (postgres-patterns, mysql-patterns, clickhouse-io); if it's an ORM-generated query, prisma-patterns first (N+1, relationJoins row explosion).
• "I need to alter a production table / add a column / index." → database-migrations for the rollout sequence, with the engine spoke for the exact DDL.
• "I need caching / a lock / a rate limiter / a queue." → redis-patterns (and check whether a SKIP LOCKED SQL queue in the engine spoke fits better).
• "This backfill/ETL is too slow." → data-throughput-accelerator; pair with the target engine spoke for native-load specifics.
• Polyglot ask (e.g. "Postgres for app data + ClickHouse for analytics + Redis cache") → fan out to each engine spoke; route the cutover through database-migrations.

Folded spokes

Additional data-layer specialists folded into this cluster. Route to them on demand the same way as the spokes above (load skills/<spoke-name>/SKILL.md). They cover the pipeline/transform/quality/ML/analytics concerns that sit alongside the engine spokes.

Pipelines & orchestration / ETL

• Apache Airflow DAGs — operators, sensors, scheduling, testing, deployment → airflow-dag-patterns (USE WHEN building/orchestrating batch pipelines or scheduling jobs; pair with data-throughput-accelerator for the heavy load step.)
• Apache Spark tuning — partitioning, caching, shuffle, memory → spark-optimization (USE WHEN a Spark job is slow or won't scale.)

Transform & analytics engineering

• dbt models — organization, tests, docs, incremental strategies → dbt-transformation-patterns (USE WHEN building warehouse transformations / analytics models; the SQL transform layer above clickhouse-io/postgres-patterns.)

Data quality & validation

• Data quality frameworks — Great Expectations, dbt tests, data contracts → data-quality-frameworks (USE WHEN adding validation rules, quality gates, or contracts to a pipeline; this is the correctness gate data-throughput-accelerator hands off to.)

Relational schema design (PostgreSQL deep-dive)

• PostgreSQL table design — data types, indexing, constraints, advanced features → postgresql (USE WHEN designing a Postgres schema from scratch; complements postgres-patterns, which owns query tuning/RLS/pooling.)

MLOps

• End-to-end ML pipelines — data prep → training → validation → deployment → ml-pipeline-workflow (USE WHEN building an ML training/deployment pipeline on top of the data layer.)

Business analytics & presentation

• KPI dashboard design — metric selection, viz best practices, real-time monitoring → kpi-dashboard-design (USE WHEN designing a business dashboard or choosing metrics.)
• Data storytelling — turning analytics into stakeholder narratives → data-storytelling (USE WHEN presenting analytics / building an exec report from data.)

Quantitative finance data

• Backtesting frameworks — look-ahead/survivorship bias, transaction costs → backtesting-frameworks (USE WHEN building/validating a trading strategy backtest.)
• Risk metrics — VaR, CVaR, Sharpe, Sortino, drawdown → risk-metrics-calculation (USE WHEN computing portfolio risk metrics or wiring risk limits.)

Picked-up spokes

Standalone data-layer skills imported from external libraries (origin noted in each spoke's frontmatter). Route to them on demand the same way (load skills/<spoke-name>/SKILL.md).

Durable workflows / reliable data ops (DBOS)

• Go application with DBOS durable workflows, steps, queues → dbos-golang (USE WHEN adding fault-tolerant, exactly-once workflows to a Go service backed by Postgres.)
• Python application with DBOS durable workflows, steps, queues → dbos-python (USE WHEN adding fault-tolerant, exactly-once workflows to a Python service backed by Postgres.)
• TypeScript application with DBOS durable workflows, steps, queues → dbos-typescript (USE WHEN adding fault-tolerant, exactly-once workflows to a TypeScript service backed by Postgres.)

Serverless Postgres

• Neon serverless Postgres — branching, connection pooling, Prisma/Drizzle integration → neon-postgres (USE WHEN running Postgres on Neon, setting up DB branching per preview/PR, or wiring the serverless driver/pooler; complements postgres-patterns query tuning.)

Graph & scientific data (Python)

• NetworkX graph modeling and algorithms — shortest path, centrality, community detection → networkx (USE WHEN the data is a graph/network and you need graph algorithms or connectivity analysis in Python.)
• Biopython bioinformatics — sequence/structure parsing, NCBI/Entrez, phylogenetics → biopython (USE WHEN parsing biological file formats or running sequence/structure analysis in Python.)

Codebase structural memory

• Data Structure Protocol — persistent dependency/public-API map for agents over a codebase → data-structure-protocol (USE WHEN an agent must reason over a large codebase's structure without re-reading the whole repo.)

Standard flow

1. Locate the task: which store (relational / analytical / in-memory / ORM-managed) and which concern (model → query → optimize → migrate → move).
2. If it touches store selection, schema/index design, or any schema change, pull the model from databases-data-core first — these decisions interlock and a wrong store choice is expensive to undo.
3. Delegate to the spoke(s). Multi-step asks fan out in lifecycle order (e.g. "ship a new column" → database-migrations plan → engine spoke DDL → backfill via data-throughput-accelerator).
4. Return: chosen spoke(s), the store/schema decision implied, the migration or correctness gate required, and the next action.

Sibling clusters (hand off, don't absorb)

This cluster owns engine/query/migration internals. Hand off when the task is about the layer above the database:

• App-layer repository pattern, transaction/unit-of-work boundaries, service-layer data access → backend-architecture.
• ORM work lives with its framework: Eloquent → php-laravel; JPA/Hibernate → jvm. Prisma stays here (prisma-patterns) as the data-layer ORM this cluster owns.

Guardrails

See databases-data-core. In short: match the store to the access pattern — don't force OLAP scans onto an OLTP row store or run analytics off your primary. Treat every production schema change as a migration (forward-only, expand/contract, no NOT NULL without a default, build indexes concurrently). Validate inputs at the application boundary; never trust client-supplied SQL fragments. Size connection pools deliberately (serverless = external pooler + low per-instance limit). Make bulk writes idempotent and prove correctness with manifests/counts before calling a data job done.

Loading spokes on demand

To keep CLI startup context lean, this cluster's spokes are not separately registered as skills — only this orchestrator and its *-core are enumerated. When you route to a spoke named above, load it on demand by reading its file:

~/.agents/skill-clusters/skills/<spoke-name>/SKILL.md (or skills/<spoke-name>/SKILL.md inside the skill-clusters repo).