build-artifacts
Regenerate dbt test fixtures after dbt_project changes
debugging-dbt-errors
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.
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Generate SQL validation notebooks for dbt changes. Pass a GitHub PR URL or local dbt repo path.
dbt_ingest
Map dbt `schema.yml` / `properties.yml` models and sources into KTX semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.
answering-natural-language-questions-with-dbt
Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about analytics, metrics, KPIs, or data (e.g., "What were total sales last quarter?", "Show me top customers by revenue"). NOT for validating, testing, or building dbt models during development.
release-dbt-mcp
Release a new version of dbt-mcp to PyPi
dbt-architecture
dbt project structure using medallion architecture (bronze/silver/gold layers). Use this skill when planning project organization, establishing folder structure, defining naming conventions, implementing layer-based configuration, or ensuring proper model dependencies and architectural patterns.
dbt-parser-refresh
Refreshes dbt artifact schemas from dbt-labs/dbt-core and regenerates Pydantic parser classes. Use when the user asks to update parsers, sync with upstream, download dbt schemas, or regenerate parser models.