rai

Plugin rai from marketplace RelationalAI in RelationalAI/rai-agent-skills. Commands read from the repo's manifest — 8 days ago.

Skills (12)

• rai-deployment

  Take a built RelationalAI model to production — deploy it into a Snowflake schema, iterate on it through the dev-schema redeploy loop, monitor its refreshes, 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 (C

• rai-discovery

  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

• rai-graph-analysis

  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, short

• rai-health

  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.

• rai-ontology

  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

• rai-predictive-modeling

  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`. Not for creatin

• rai-predictive-task-generation

  Build prediction task tables for predictive-modeling frameworks (e.g. GNN) that ingest Snowflake tables. Use this skill whenever the user wants to create, scaffold, or modify a task table for any of the six supported tasks — binary classification, multilabel classification, multiclass classification

• rai-predictive-training

  Generate PyRel code (it does not run it) to configure and train graph neural network (GNN) models, generate predictions, evaluate results, and manage trained models. Also covers `RelFM`/`RelFMTabular` -- zero-shot, no-training-step alternatives to GNN (flat/tabular single-table and relational multi-

• rai-prescriptive-problem

  Formulates optimization and constraint-satisfaction problems from ontology models — decision variables, constraints, objectives, problem-type classification, solver selection, global constraints, and pre-solve validation. Use when building, reviewing, debugging, or relaxing a formulation, through th

• rai-prescriptive-results

  Runs optimization solves and interprets the output — solve execution and parameters, diagnostics requests (sensitivity, conflict / IIS), status codes, solution extraction, quality assessment, sensitivity analysis, infeasibility diagnosis, and stakeholder explanation. Use when executing a formulated

• rai-pyrel

  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 a

• rai-setup

  Setup and configuration for RelationalAI — first-time install walkthrough and all raiconfig.yaml tuning. Use when installing RAI, connecting to Snowflake, running RAI locally on DuckDB for development, or editing raiconfig.yaml. Not for writing PyRel model code (see rai-pyrel) or solver usage and di

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