Snowflake 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.

Perform business analysis workflows — KPI frameworks, predictive models, real-time dashboards, TAM/SAM/SOM calculations, and multi-year financial modeling for startups — using Python, SQL, and cloud data warehouses like Snowflake and BigQuery.

Build and manage end-to-end data analytics workflows: implement A/B testing with statistical rigor, design reliable analytics tracking, create interactive D3.js visualizations, architect scalable database schemas, and optimize SQL for cloud-native databases.

Develop and manage end-to-end Databricks workflows: data engineering with Spark, Delta, Iceberg, and streaming; build and deploy ML models and GenAI agents; create dashboards, apps, and CI/CD bundles; query Unity Catalog and manage infrastructure via SDK/CLI operations.

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.

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.

Manage AWS data lake and analytics workflows: create and manage S3 Tables (Iceberg), import data from databases and S3, query with Athena, manage Glue connections and catalogs, and store/query vector embeddings with S3 Vectors.

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 cost-safe geospatial SQL queries for BigQuery, Snowflake, Wherobots, and Postgres by discovering database schemas, enforcing mandatory dry-runs, and rendering results on an interactive map.

Profile, analyze, and QA data workflows across multiple SQL dialects — craft optimized queries, apply statistical methods, profile unfamiliar datasets, and catch common analysis pitfalls with a reproducible quality checklist.

Analyze and optimize cloud, AI, and SaaS costs across AWS, Azure, GCP, Databricks, and Snowflake with guidance on commitment management, rightsizing, cost allocation, and AI spend reduction.

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.

Query, model, and govern a Honeydew semantic layer from your code editor: define entities and metrics from Snowflake, Databricks, or BigQuery sources, build relationships and calculated attributes, validate models with execution tests, and manage workspace branches and pull requests.

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.

Generate optimized SQL queries from natural language for BigQuery, PostgreSQL, MySQL, and Snowflake; perform cohort analysis on CSV/Excel user data to compute retention rates, visualize trends, and detect anomalies; evaluate A/B tests with statistical significance, confidence intervals, and launch recommendations.

Analyze data, explore schemas, build insights, and run forecasts using AI agents on the Altertable platform, with support for DuckDB, Snowflake, BigQuery, and more.

Manage Keboola data pipelines via CLI: initialize projects, sync configs bidirectionally with diff previews, and launch 10-agent AI audits for SQL quality, security, performance, financial logic, data architecture, PII detection, lineage mapping, and templatization readiness, generating prioritized reports and fixes.