RelationalAI
34 verified skills136 total stars
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.
documentation4
rai-querying
Covers query construction in PyRel v1 including aggregation, derived concepts, filtering, ordering, multi-concept joins, and data export. Use when building queries or extracting data from RAI models.
development4
dev-skills-review
Reviews RAI agent skills for structure, content quality, prompt engineering, boundaries, examples, and agent usability. Use when creating, reviewing, or auditing skills in rai-agent-skills or rai-agent-skills-private.
development4
rai-cortex-integration
Covers deploying RAI models as Snowflake Cortex Agents for Snowflake Intelligence. Use when deploying a model as a Cortex Agent or configuring Snowflake Intelligence.
devops4
rai-health-skill
Guides diagnosis of RAI engine performance issues and recommends remediation. Use when an engine is slow, unresponsive, or needs scaling.
documentation4
rai-rules-authoring
Converts natural language business rules into PyRel derived properties. Covers validation, classification, derivation, alerting, and reconciliation patterns with rule chaining. Use for business logic, flags, subtypes, segmentation, or compliance rules.
testing4
rai-ontology-design
Covers RAI domain modeling decisions — concepts, relationships, data mapping, model composition, enrichment, and advanced modeling patterns. Use when reviewing, enriching, or evolving an existing ontology — not for greenfield starter builds (see rai-build-starter-ontology).
development4
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 reasoner skills need. Use before choosing a reasoner workflow or when scoping what to build next.
development4
dev-quality-skills-review
Reviews RAI agent skills for structure, content quality, prompt engineering, boundaries, examples, and agent usability. Use when creating, reviewing, or auditing skills in rai-agent-skills or rai-agent-skills-private.
development4
rai-configuration
Covers PyRel v1 configuration including raiconfig.yaml, connection setup, programmatic config, model and reasoner settings, and engine management. Use when setting up or troubleshooting RAI connections and configuration.
data-ai4
rai-prescriptive-results-interpretation
Interprets optimization solver output including solution extraction, status codes, quality assessment, result explanation, and sensitivity analysis. Use when analyzing solve results or communicating optimization outcomes.
development4
rai-onboarding
Guides first-time RelationalAI (RAI) setup end-to-end — install, connect to Snowflake, validate, and run a starter program. Use when starting a new RAI project or environment.
testing4
rai-graph-analysis
Graph algorithm selection and execution on PyRel v1 models. Covers graph construction from ontology patterns, algorithm families (centrality, community, reachability, distance, similarity, components), parameter tuning, result extraction, and downstream use. Use when building or running graph analyses on RAI models.
development4
rai-prescriptive-problem-formulation
Formulates optimization and constraint satisfaction problems from ontology models — decision variables, constraints, objectives, and common patterns. Use for optimization and constraint-satisfaction tasks — building, reviewing, or debugging the formulation; then solve it with rai-prescriptive-solver-management and interpret the solution with rai-prescriptive-results-interpretation.
development4
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`.
development4
rai-querying
PyRel v1 query construction against `relationalai.semantics.Model` — selects, filters, joins, aggregates, grouping, export. Load this BEFORE writing any PyRel query, even your first one — your prior knowledge of the syntax is likely stale. Use whenever the user asks to query, count, list, rank, aggregate, join, or export data from a RAI model, even if they don't say "PyRel". Does not cover deriving new classifications, tiers, flags, segments, or properties — those must be authored with the `rai-rules-authoring` skill first.
data-ai4
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`.
data-ai4
rai-pyrel-coding
Covers PyRel v1 language syntax — imports, type system, concepts, properties, relationships, data loading, references, and code structure. Use when writing or reviewing general PyRel code — not query construction (see rai-querying), business-rule authoring via derived properties (see rai-rules-authoring), or optimization formulation with decision variables, constraints, and objectives (see rai-prescriptive-problem-formulation).
development4
rai-prescriptive-results-interpretation
Interprets optimization solver output including solution extraction, status codes, quality assessment, result explanation, and sensitivity analysis. Use when analyzing solve results or communicating optimization outcomes.
development4
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, shortest paths and distance, and node similarity.
development4
rai-rules-authoring
Converts natural language business rules into PyRel derived properties — validation, classification, derivation, alerting, and reconciliation. Use whenever a task assigns each entity a new tier, segment, score, or flag, or derives a new property; author it here as a derived property, then query it with rai-querying.
testing4
rai-setup
Setup and configuration for RelationalAI — first-time install walkthrough and all raiconfig.yaml tuning. Use when installing RAI, connecting to Snowflake, or editing raiconfig.yaml. Not for writing PyRel model code (see rai-pyrel-coding) or solver usage and diagnostics (see rai-prescriptive-solver-management).
development4
dev-release
Bumps the plugin version across manifest files, commits, and creates a local git tag for rai-agent-skills. Pushing and publishing the GitHub release happen separately, under human review. Use when cutting a release.
tools4
rai-build-starter-ontology
Walks through building a first RAI ontology from Snowflake tables or local data samples. Use when creating a new RAI model, starting a proof of concept, or onboarding a new dataset.
development4
rai-prescriptive-solver-management
Covers solver lifecycle including problem type classification, solver selection and creation, global constraints, pre-solve validation, solve execution, and solver-level diagnostics. Use when configuring or running optimization solvers, not for interpreting post-solve results.
data-ai4
rai-setup
Setup and configuration for RelationalAI — first-time install walkthrough and all raiconfig.yaml tuning. Use when installing RAI, connecting to Snowflake, or editing raiconfig.yaml. Not for writing PyRel model code (see rai-pyrel-coding) or solver usage and diagnostics (see rai-prescriptive-solver-management).
development4
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.
documentation4
rai-discovery
Discover questions to answer or problems to solve. Surfaces what the data can support, classifies by reasoner type, and routes to the right workflow. Use before choosing a reasoner workflow or when scoping what to build next.
development4
rai-build-starter-ontology
Walks through building a first RAI ontology from Snowflake tables or local data samples. Use when creating a new RAI model, starting a proof of concept, or onboarding a new dataset.
development4
rai-prescriptive-solver-management
Covers solver lifecycle including problem type classification, solver selection and creation, global constraints, pre-solve validation, solve execution, and solver-level diagnostics. Use when configuring or running optimization solvers, not for interpreting post-solve results.
data-ai4
rai-pyrel-coding
Covers PyRel v1 language syntax — imports, type system, concepts, properties, relationships, data loading, references, and code structure. Use when writing or reviewing general PyRel code — not query construction (see rai-querying), business-rule authoring via derived properties (see rai-rules-authoring), or optimization formulation with decision variables, constraints, and objectives (see rai-prescriptive-problem-formulation).
development4
rai-ontology-design
Covers RAI domain modeling decisions — concepts, relationships, data mapping, model composition, enrichment, and advanced modeling patterns. Use when reviewing, enriching, or evolving an existing ontology — not for greenfield starter builds (see rai-build-starter-ontology).
development4
rai-cortex-integration
Covers deploying RAI models as Snowflake Cortex Agents for Snowflake Intelligence. Use when deploying a model as a Cortex Agent or configuring Snowflake Intelligence.
devops4
rai-prescriptive-problem-formulation
Formulates optimization problems from ontology models covering decision variables, constraints, objectives, and common patterns. Use when building, reviewing, or debugging a formulation.
development4