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Found 23 Skills
Use when creating or modifying dbt Semantic Layer components including semantic models, metrics, and dimensions leveraging MetricFlow.
Wire a semantic layer into a nao agent so that metric queries are routed through a single source of truth. Supports dbt MetricFlow (dbt Cloud with Semantic Layer), Snowflake (views or semantic views via MCP), an in-house nao YAML semantic layer, or other tools (via MCP discovery). Installs the right MCP server, updates RULES.md to route metric queries through the semantic layer, and (for the nao YAML option) generates starter metric files. Use after a first round of tests has shown the agent struggling with metric reliability. Do not use for raw rule writing (write-context-rules) or first-time setup (setup-context).
Use when building, creating, or editing data apps that should query Metabase tables and metrics through generated schema files like metabase.data.ts or *.metabase.data.ts.
Use when fetching dbt documentation, looking up dbt features, or answering questions about dbt Cloud, dbt Core, or the dbt Semantic Layer
Use ktx to build a self-improving context layer that teaches AI agents how to query data warehouses accurately with approved metrics, semantic layers, and business knowledge
Context layer for data and analytics AI agents with semantic layer, skills, and memory via MCP
Build and maintain an executable context layer for data and analytics agents using ktx's semantic layer, wiki knowledge, and MCP integration
Create, alter, and validate Snowflake semantic views using Snowflake CLI (snow). Use when asked to build or troubleshoot semantic views/semantic layer definitions with CREATE/ALTER SEMANTIC VIEW, to validate semantic-view DDL against Snowflake via CLI, or to guide Snowflake CLI installation and connection setup.
Use when working with Lightdash YAML files, dbt models with Lightdash metadata, the lightdash CLI (deploy, upload, download, preview, lint, warehouse-catalog, sql, set-warehouse), or managing charts, dashboards, spaces and access, AI agents, scheduled content, users, groups, custom roles, metrics, and dimensions as code
Use this skill when building dbt models, designing semantic layers, defining metrics, creating self-serve analytics, or structuring a data warehouse for analyst consumption. Triggers on dbt project setup, model layering (staging, intermediate, marts), ref() and source() usage, YAML schema definitions, metrics definitions, semantic layer configuration, dimensional modeling, slowly changing dimensions, data testing, and any task requiring analytics engineering best practices.
Configure and use ktx to build an executable context layer for AI agents querying data warehouses with semantic layers, wiki knowledge, and approved metrics
Expert in using ktx, the executable context layer for data and analytics agents that enables accurate querying through MCP with skills, memory and a semantic layer