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Found 54 Skills
Primary entry point for building, managing, and orchestrating data pipelines on Google Cloud. Guides users to the appropriate skill for dbt, Dataflow (Apache Beam), Dataform, Spark (Dataproc Serverless), BigQuery Data Transfer Service (DTS) or orchestration pipeline using Cloud Composer. Clarify requirements and resolve ambiguity for creating, updating and running data pipelines.
Adds schema tests and data quality validation to dbt models. Use when working with dbt tests for: (1) Adding or modifying tests in schema.yml files (2) Task mentions "test", "validate", "data quality", "unique", "not_null", or "accepted_values" (3) Ensuring data integrity - primary keys, foreign keys, relationships (4) Debugging test failures or understanding why dbt test failed Matches existing project test patterns and YAML style before adding new tests.
Debugs and fixes dbt errors systematically. Use when working with dbt errors for: (1) Task mentions "fix", "error", "broken", "failing", "debug", "wrong", or "not working" (2) Compilation Error, Database Error, or test failures occur (3) Model produces incorrect output or unexpected results (4) Need to troubleshoot why a dbt command failed Reads full error, checks upstream first, runs dbt build (not just compile) to verify fix.
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.
Develops and troubleshoots dbt incremental models. Use when working with incremental materialization for: (1) Creating new incremental models (choosing strategy, unique_key, partition) (2) Task mentions "incremental", "append", "merge", "upsert", or "late arriving data" (3) Troubleshooting incremental failures (merge errors, partition pruning, schema drift) (4) Optimizing incremental performance or deciding table vs incremental Guides through strategy selection, handles common incremental gotchas.
dbt (data build tool) patterns for model organization, incremental strategies, and testing.
Use this skill when building data pipelines, ETL/ELT workflows, or data transformation layers. Triggers on Airflow DAG design, dbt model creation, Spark job optimization, streaming vs batch architecture decisions, data ingestion, data quality checks, pipeline orchestration, incremental loads, CDC (change data capture), schema evolution, and data warehouse modeling. Acts as a senior data engineer advisor for building reliable, scalable data infrastructure.
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Expert guidance for creating, modifying, and optimizing dbt pipelines for BigQuery. Use this skill whenever user asks for generating or modifying a dbt model or project. Activate this skill when the user - Creates, modifies, or troubleshoots **dbt models or pipelines** - Needs to **optimize SQL** within a dbt project - Is **setting up a new dbt project** or configuring existing one
Use when running a dbt Fusion project with Astronomer Cosmos. Covers Cosmos 1.11+ configuration for Fusion on Snowflake/Databricks with ExecutionMode.LOCAL. Before implementing, verify dbt engine is Fusion (not Core), warehouse is supported, and local execution is acceptable. Does not cover dbt Core.
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).
Provide a lookup index of dbt models (BigQuery tables) to guide query writing against a data warehouse. Use when you need to query, analyze, or look up data in a dbt-powered data warehouse, or when resolving a vague data question into the right BigQuery tables to query.