bigtable-basics

Upgrades Spark workloads on Google Cloud Managed Service for Apache Spark (formerly known as Dataproc) clusters and Serverless to a newer image or runtime version. Supports static compatibility analysis, code upgrade, build checks against the target version, and optional verification runs. Use when: - Upgrading Managed Spark versions (major, minor, or sub-minor) or assessing upgrade readiness. Example prompt: "Upgrade Managed Spark job [job_id] in project [project_id], region [region] to target version [target_version]. Code location: [local path or GCS path]. Target GCS path: [gcs_target_path]" Don't use when: - Writing new Spark code. - Migrating non-Spark workloads (e.g., Hive/Flink to Spark). - Troubleshooting a failed Spark job that is not part of an upgrade. Limitation: This skill does not support data/result validation currently.

Provides expert guidance for troubleshooting Google Cloud Spark and Dataproc workloads (Dataproc Serverless batches and standard Dataproc clusters), and inspecting, streaming, searching, tailing, or summarizing Spark driver outputs and event logs in Cloud Storage. Use when the user asks to debug, troubleshoot, diagnose, or perform Root Cause Analysis (RCA) on failed Spark jobs, PySpark batches, or Spark event logs.

Monitors, inventories, and assesses Google Cloud Managed Service for Apache Spark (Dataproc Serverless and Dataproc on Compute Engine) environments, clusters, batches, jobs, sessions, metrics, logs, costs, and configurations across single and multiple regions. Use when listing active or historical Spark workloads, discovering failed batches or jobs, auditing multi-region fleet inventory, calculating Serverless compute (DCU-Hours) and shuffle storage consumption, detecting idle or zombie clusters, inspecting scheduled pipeline cadence health, investigating failed cluster operations or stockouts, checking quotas and alerting coverage, or querying Cloud Monitoring and Cloud Logging telemetry. Don't use for deep root-cause analysis or performance optimization of a specific job or batch (use gcp-spark-troubleshooting), authoring new PySpark code (use gcp-spark), or upgrading Spark versions (use gcp-managed-spark-upgrades).

Automates declarative resource creation and provisioning for data pipelines, supporting BigQuery, Dataform, Dataproc, BigQuery Data Transfer Service (DTS), and other resources. It manages environment-specific configurations (dev, staging, prod) through a deployment.yaml file. Use when: - Modifying or creating deployment.yaml for deployment settings. - Resolving environment-specific variables (e.g., Project IDs, Regions) for deployment. - Provisioning supported infrastructure like BigQuery datasets/tables, Dataform resources, or DTS resources via deployment.yaml. Do not use when: - Resources already exist. - Managing resources not supported by `gcloud beta orchestration-pipelines resource-types list`. - Managing general cloud infrastructure (VMs, networks, Kubernetes, IAM policies), which are better suited for Terraform. - Infrastructure spans multiple cloud providers (AWS, Azure, etc.). - Already uses Terraform for the target resources.

This skill helps the agent generate or update orchestration pipeline definitions for Google Cloud Composer to initialize orchestration pipeline or update the orchestration definition for orchestration of various data pipelines, like dbt pipelines, notebooks, Spark jobs, Dataform, Python scripts, Vertex AI custom jobs, Machine Learning training, or inline BigQuery SQL queries. This skill also helps deploy and trigger orchestration pipelines.

Provides guidance for migrating Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers migration to Airflow 2.11.1 (MSAA Gen 2 and 3) and Airflow 3 (MSAA Gen 3), including environment inspection, GCS download/upload and scanning patterns for breaking changes.

Guides writing, packaging, executing, and troubleshooting Apache Beam pipelines on Dataflow. Use when creating new pipelines, configuring Flex Templates, or analyzing performance of Dataflow jobs. Capabilities include Java/Python/Go setup, Cloud Build integration, and deep diagnostic analysis of job health and autoscaling. Use when: - Creating an Apache Beam Dataflow pipeline. - Creating a Google Dataflow Flex Template. - Using an existing Google Dataflow Template. - Debugging Dataflow pipeline - Troubleshooting Dataflow pipeline - Analyzing Performance of Dataflow pipeline. Key capabilities: Java/Python/Go project setup, Flex Templates (with Cloud Build), and diagnostics for streaming job health, bottlenecks, and autoscaling. Do NOT use for: - General GCP resource management unrelated to Dataflow. - Issues with other GCP services (e.g., GCE, GCS, BigQuery) unless directly impacting Dataflow pipeline execution. - Pipeline technologies other than Apache Beam on Dataflow.

Guides the authoring and validation of Apache Airflow DAGs for Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.

Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes: Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".

Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.

Set up Claude Code hooks to block dangerous git commands (push, reset --hard, clean, branch -D, etc.) before they execute. Use when user wants to prevent destructive git operations, add git safety hooks, or block git push/reset in Claude Code.

Wire dependency-cruiser into a TypeScript repo so each package is a deep module, with implementation hidden in subfolders and reachable only through its entry-point files. User-invoked.

Conduct a retrospective on a coding session.

Use when completing tasks, implementing major features, or before merging to verify work meets requirements