Skill UI

Provides automated guidance for Google Cloud Storage lifecycle policy scenarios, including pattern recommendations, production-ready configurations, and best practices for compute, storage, BigQuery, Vertex AI, and other GCP-specific services.

Automated guidance for GKE cluster configuration within GCP Skills, offering step-by-step instructions, best practices, and production-ready code for compute, storage, BigQuery, Vertex AI, and other GCP services.

Automates Google BigQuery workflows via Rube MCP and Metabase, letting you run native SQL/MBQL queries, fetch metadata, and explore schemas before querying.

This skill provides comprehensive guidelines for monitoring Granola usage, tracking team meeting patterns, and building detailed analytics dashboards. It guides users in defining critical metrics (e.g., adoption rate, efficiency score) and establishing robust data pipelines. Users can stream meeting metadata from Granola to data warehouses like BigQuery, enabling deep, customizable reporting and automated weekly insights via services like Zapier and Slack.

Automates Google Cloud IAM binding tasks by guiding users through best practices, generating production-ready code and configs, and validating outputs across compute, storage, BigQuery, Vertex AI, Cloud Run, Firebase, and other GCP services whenever iam binding creator is mentioned.

Provides comprehensive patterns for data synchronization, ETL processes, and data migration specifically for MaintainX. Use this when needing to keep data consistent between MaintainX and external databases, data warehouses (like BigQuery), or when performing bulk data exports to formats like CSV. It covers incremental syncing, full exports, and data reconciliation checks.

An advanced workflow orchestrator designed to perform multi-phase, deep-dive schema optimization on large datasets (e.g., BigQuery exports). It systematically analyzes schema structure, identifies unused or redundant fields, assesses potential impact, and generates a comprehensive, actionable report with recommended optimization strategies and estimated savings.

Guides GCP service account management requests with automated best practices, step-by-step help, production-ready code/configuration, and validation across compute, storage, BigQuery, Vertex AI, Cloud Run, and Firebase workflows.

Provides automated guidance for designing, validating, and deploying Vertex AI pipelines within Google Cloud, covering compute, storage, BigQuery, and GCP-specific services for production readiness.

Automates VPC network provisioning guidance within the GCP Skills category, offering best practices, ready-to-run code, and validation for compute, storage, BigQuery, Vertex AI, and other Google Cloud services whenever “vpc network setup” is mentioned.

Reviews Python + BigQuery pipelines to flag runaway costs, missing dry-run safeguards, unsafe backfills, write idempotency issues, and monitoring gaps, returning a structured report with exact function/line references for minimal fixes.

Builds a robust, automated workflow to export critical sales data—including forecasts, quota details, adjustments, and CRM totals—from Clari CRM to a designated data warehouse. The pipeline supports multiple targets like Snowflake, BigQuery, and PostgreSQL, ensuring that real-time revenue intelligence is available for advanced analytics and business reporting.