dbt plugins

Build production-ready data pipelines with Apache Airflow and dbt, manage scalable data warehouses, and implement vector search and RAG systems using embedding models and vector databases.

Delegate complex data engineering, ML, and AI workflows to specialized sub-agents that design scalable pipelines, build and optimize models, architect LLM systems, tune databases for performance, and deploy production infrastructure across clouds.

Generate and update project documentation via slash commands: create architecture docs with C4/Mermaid, onboarding/migration/troubleshooting guides, dbt model YAML, Keep a Changelog entries; analyze git changes/GitHub issues for explanations and README updates.

Accelerate website creation and optimization with 103 structured skills spanning research, brand strategy, design, content, SEO, analytics, performance, security, and deployment—each providing expert-level prompts for tasks like auditing accessibility, fixing Core Web Vitals, conducting competitive analysis, running experiments, and managing incident response.

Build and test dbt models using SQL transformations, ref/source, and YAML unit tests; configure semantic layers for metrics, dimensions, and KPI queries; troubleshoot Cloud jobs with logs, API, and git; implement Mesh governance for contracts and cross-project refs; access docs; format CLI commands; generate MCP configs for VS Code integration.

Manage the full Airflow data engineering lifecycle: author, test, debug, and deploy DAGs; profile and query data warehouses; trace lineage; migrate between Airflow versions; and manage local and production deployments via the Astro CLI.

Generate Mermaid diagrams visualizing dbt model lineage and dependencies as color-coded DAGs in markdown with legends. Input manifest.json, use MCP tools, or parse code directly to quickly diagram data pipelines and model relationships for documentation and analysis.

Guides data engineering projects through a structured Spec-Driven Development workflow with 58 specialized agents for pipeline design, schema modeling, SQL optimization, data quality, lakehouse architecture, and AI/ML infrastructure. Generates visual diagrams, HTML documentation, code reviews, and git-aware status reports.

Build and orchestrate GCP data pipelines using natural language — architect dbt, Dataform, Spark, BigQuery, and Cloud Composer workflows, profile and transform data with Dataplex, and query BigQuery, Spanner, AlloyDB, and Cloud SQL databases.

Engineer robust ETL pipelines: clean messy CSVs/Parquet, infer schemas, profile datasets, detect anomalies, validate quality with Pydantic/Pandera/Great Expectations, implement incremental patterns, generate dbt models/SQL migrations/tests, and orchestrate autonomous backfills/pipeline testing via agents and CLI commands.

Initialize drt Reverse ETL projects with BigQuery or PostgreSQL, generate YAML sync configs from dbt models or SQL to destinations like Slack, HubSpot, GitHub Actions, or databases, debug auth timeouts and config errors, and migrate pipelines from Census or Hightouch.

Administer Omni Analytics instances, explore and query semantic models, build and optimize YAML data models for AI, embed dashboards in external apps, and evaluate AI query accuracy — all through the Omni CLI.

Build dbt data models with dimensional patterns, staging/marts organization, and tests; deploy and manage Fly.io apps using Docker, fly.toml, volumes, secrets, and multi-region setups for Python/Node/Rails/Django; design services via customer journey maps, blueprints, and touchpoints; apply strategy frameworks like RICE, ICE, and Ansoff for prioritization and growth planning.

Build, configure, and run dbt projects on the duckrun adapter, executing dbt models in DuckDB and materializing results as Delta Lake tables via delta-rs, locally or on cloud object storage (S3/GCS/ADLS/OneLake).

Automate Linux Foundation development workflows: scaffold Snowflake/dbt data sources, enforce post-commit code conventions with peer-review pattern audits, manage Git DCO signoff, PR review resolution, and cross-repo task routing.

Design AWS and GCP infrastructure using Terraform and Ansible patterns, build data pipelines with dbt and SQLMesh, generate and manage RFCs plus technical specs in Markdown, and automate local dev setups including direnv, git worktrees, and port allocations for Docker services.

Streamline end-to-end data science and ML workflows: frame business problems into ML tasks, preprocess and validate data with quality checks, perform EDA on diverse formats, design and execute experiments with hyperparameter tuning via Optuna and interpretability via SHAP, audit reproducibility and leakage, evaluate model performance and readiness for deployment, generate model cards, and extract structured learnings into docs.