Take a built RelationalAI model to production — deploy it into a Snowflake schema and version it through the op log (branch, collaborate, merge, and tear down with the `rai models` CLI), or deploy it as a Snowflake CoWork (Cortex) agent. The one path-to-prod skill across deployment targets. Use when deploying a model, managing its deployed lifecycle, or operationalizing it as a Cortex agent — not for first-time install/connect (see rai-setup), building the model (see rai-ontology, rai-pyrel), or interpreting reasoner output.
Graph algorithm selection and execution on PyRel v1 models — construction from ontology patterns, parameter tuning, and result extraction. Use for questions about a network's structure — centrality and importance, community detection, connectivity and components, reachability and dependencies, shortest paths and distance, node similarity, and variable-length path enumeration (where the route itself is the answer).
PyRel v1 language — modeling syntax (concepts, properties, relationships, data loading), business rules as derived properties (validation, classification, tiers, flags), and query construction against `relationalai.semantics.Model` (selects, filters, joins, aggregates, export). Load BEFORE writing any PyRel code, even your first line — prior knowledge of the syntax is likely stale. Use whenever the user asks to model, load, derive, classify, flag, query, count, rank, aggregate, join, or export data from a RAI model, even if they don't say PyRel. Not for ontology design decisions (see rai-ontology), optimization formulation (see rai-prescriptive-problem), graph algorithms (see rai-graph-analysis), or GNN work (see rai-predictive-modeling).
Translation, ideation, and routing layer between an ontology and the RAI reasoners. Surfaces questions the data can answer, classifies them by reasoner family (prescriptive, graph, predictive, rules), and translates user-facing problem framings into the technical implementation hints the downstream reasoner skills need. Use before choosing a reasoner workflow or when scoping what to build next.
Guides diagnosis of RAI engine performance, failed transactions, CDC/data-stream health, and CDC engine management. Use when a reasoner is slow or queuing, a transaction or batch has failed, a CDC stream is suspended or quarantined, or CDC engine sizing/recovery is needed.
Builds and evolves RAI ontologies — greenfield starter builds from Snowflake tables or local data, and all domain-modeling decisions (concepts, relationships, identity, subtypes, data mapping, layering, enrichment). Use when creating a new RAI model, starting a proof of concept, onboarding a dataset, or reviewing and enriching an existing ontology. Authoring the PyRel itself (syntax, data loading, rules, queries) is `rai-pyrel`.
Build graph neural network (GNN) models — concepts, Snowflake data loading, task relationships, graph edges, and PropertyTransformer features. Use for node classification, regression, and link prediction tasks; for training, predictions, and evaluation, see `rai-predictive-training`.
Configure and train graph neural network (GNN) models, generate predictions, evaluate results, and manage trained models. Use when ready to train, generate predictions, evaluate, or manage models; for concepts, data loading, edges, and feature configuration, see `rai-predictive-modeling`.