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Add gcp_job_monitoring skill for Data Cloud Admin and Workload Monitoring. - #467
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…toring. https://docs.google.com/document/d/1GzdDp1uaHisdxDJnfOViVvd9vzRoQ5VuQvQfe7m2eo0/edit?resourcekey=0-xEguGjH3LcF64huvf-07Zg&tab=t.0 Adds a platform-independent skill (`gcp-job-monitoring`) and zero-dependency Python helper CLI (`scripts/gcp_job_monitor.py`) that dynamically answers Data Cloud Admin & Practitioner job-monitoring queries across BigQuery and Dataproc Spark (Serverless Batches, Interactive Sessions, and Cluster Jobs): - Recent & active jobs (`bq_list`, `spark_list`) with `--mine`, `--user`, and `--state` filtering, plus automatic `allUsers=true` -> `allUsers=false` permission fallback when the caller lacks `bigquery.jobs.listAll`. - Most expensive BigQuery jobs (`bq_top_cost`) ranked by bytes billed (with estimated on-demand USD cost) and slot time. - Project-wide spend, slot & failure attribution (`bq_summary`) breaking down usage by user, by Edition / Slot Reservation (`ON_DEMAND` vs. `ENTERPRISE`), and top failure error reasons. - Hot tables analysis (`bq_hot_tables`) ranking referenced BigQuery tables by total bytes billed, slot time, query count, and distinct users. - Capacity & fleet monitoring (`bq_reservations`, `spark_clusters`) inspecting BigQuery slot reservations, autoscaling limits, capacity commitments, assignments, active Dataproc clusters, and Serverless interactive sessions. - Slowest jobs & single-job stage inspection (`bq_slowest`, `spark_slowest`, `bq_describe`, `spark_describe`) surfacing SQL text, per-stage `queryPlan` wait/compute/spill metrics, referenced tables, errors, and Serverless DCU-hours. - Supports `--format=json`, reusable CAA-safe `RestClient` for arbitrary GCP REST endpoints, and `references/information_schema_queries.md` for SQL/MCP environments. PiperOrigin-RevId: 999612801
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Add
gcp_job_monitoringskill for Data Cloud Admin and Workload Monitoring.https://docs.google.com/document/d/1GzdDp1uaHisdxDJnfOViVvd9vzRoQ5VuQvQfe7m2eo0/edit?resourcekey=0-xEguGjH3LcF64huvf-07Zg&tab=t.0
Adds a platform-independent skill (
gcp-job-monitoring) and zero-dependency Python helper CLI (scripts/gcp_job_monitor.py) that dynamically answers Data Cloud Admin & Practitioner job-monitoring queries across BigQuery and Dataproc Spark (Serverless Batches, Interactive Sessions, and Cluster Jobs):bq_list,spark_list) with--mine,--user, and--statefiltering, plus automaticallUsers=true->allUsers=falsepermission fallback when the caller lacksbigquery.jobs.listAll.bq_top_cost) ranked by bytes billed (with estimated on-demand USD cost) and slot time.bq_summary) breaking down usage by user, by Edition / Slot Reservation (ON_DEMANDvs.ENTERPRISE), and top failure error reasons.bq_hot_tables) ranking referenced BigQuery tables by total bytes billed, slot time, query count, and distinct users.bq_reservations,spark_clusters) inspecting BigQuery slot reservations, autoscaling limits, capacity commitments, assignments, active Dataproc clusters, and Serverless interactive sessions.bq_slowest,spark_slowest,bq_describe,spark_describe) surfacing SQL text, per-stagequeryPlanwait/compute/spill metrics, referenced tables, errors, and Serverless DCU-hours.--format=json, reusable CAA-safeRestClientfor arbitrary GCP REST endpoints, andreferences/information_schema_queries.mdfor SQL/MCP environments.