- Add Integration TestCreate a new integration test for dbt-autofix with proper folder structure and golden filesdbt-labs/dbt-autofix88
- Agents Schema SearchUse when answering questions about data in a warehouse (BigQuery, Snowflake, etc.) and you have direct SQL access (CLI, driver, or console). Discover metadata and run knowledge search through the warehouse-native AGENTS schema instead of INFORMATION_SCHEMA.dbt-labs/agents_schema66
- Connect WarehouseConnect an AI coding agent to Snowflake, BigQuery, Databricks, or ClickHouse so it can run SQL. Use when warehouse access is missing, authentication or connection selection is required, or before using a warehouse-data skill such as agents-schema-search.dbt-labs/agents_schema66
- Dbt Ce Ai FunctionsUse the row-level AI functions from the dbt_context_engineering package in a consuming dbt project: generate (free-form/structured text), classify (single label from a taxonomy), extract (typed structured records), and ai_agg (group-level LLM aggregation), plus authoring the versioned prompt and schema macros they require in your own project and reading structured output back with text() and field(). Use whenever a user wants an LLM to run over their rows in dbt: 'classify each ticket by prioritdbt-labs/dbt-context-engineering23
- Dbt Ce PipelineBuild a retrieval / semantic-search pipeline in a dbt project using the dbt_context_engineering package — chunk, attach metadata, embed, vector search, knowledge base — and make it production-grade (cost guards, run log, incremental re-embed, groundedness/eval tests). Use whenever a user wants to turn a text corpus (transcripts, docs, tickets, emails) into searchable context: semantic search, RAG context tables, retrieval over embeddings, a knowledge base, chunking text for an LLM, embedding a cdbt-labs/dbt-context-engineering23
- Dbt Ce SetupInstall and configure the dbt_context_engineering package in a consuming dbt project: packages.yml/dbt deps, the per-adapter var configuration everything depends on (embedding_model, model_generate/classify/extract, the ai_functions_enabled safety gate, cost ceilings, output caps), and per-engine prerequisites for Snowflake (Cortex), Databricks (serverless/DBR), and BigQuery (Vertex connection, Gemini thinking budget). Use whenever a user is setting up or configuring the package: 'how do I instadbt-labs/dbt-context-engineering23
- Adding New Adapters Support For Dbt Query ProfilingUse when adding support for a new data warehouse adapter (e.g., Trino, Postgres, Spark) to dbt-query-profiler. Triggers include creating adapter macros, implementing query history/plan/stats, or extending platform support.dbt-labs/dbt-query-profiler4
- Using Dbt Query Profiler PackageUse when troubleshooting dbt query performance, comparing model implementations, retrieving query plans/stats, or helping users understand how to use the dbt-query-profiler package. Also use when users ask about getting query IDs from dbt runs.dbt-labs/dbt-query-profiler4