Google BigQuery#

Operate Google BigQuery through your OOMOL-connected account. This skill calls the google_bigquery connector with the oo CLI; OOMOL injects credentials server-side, so you never handle raw tokens.

Running an action#

Assume the user has already installed the oo CLI, signed in, and connected Google BigQuery. Do not run oo auth login or open the connection URL proactively — just run the action. Fall back to First-time setup only when a command actually fails with an auth or connection error.

1. Inspect the contract to get the authoritative input/output schema before building a payload:

bash

oo connector schema "google_bigquery" --action "<action_name>"

2. Run the action with a JSON payload that matches the input schema:

bash

oo connector run "google_bigquery" --action "<action_name>" --data '<json>' --json

• --data takes a JSON object string or @path/to/file.json; omit it to send {}.
• The response is { "data": ..., "meta": { "executionId": "..." } }; the execution id lives under meta.executionId.

Each action is listed below with a one-line description; actions that change state carry a [write] or [destructive] tag. Before constructing --data, fetch the action's live schema with oo connector schema to get its authoritative input fields.

Available actions#

• cancel_job — Cancel a BigQuery job. [destructive]
• create_dataset — Create a BigQuery dataset. [write]
• create_routine — Create a BigQuery routine such as a user-defined function or stored procedure. [write]
• create_table — Create a BigQuery table. [write]
• delete_dataset — Delete a BigQuery dataset, optionally deleting contained tables when explicitly requested. [destructive]
• delete_model — Delete a BigQuery ML model from a dataset. [destructive]
• delete_routine — Delete a BigQuery routine from a dataset. [destructive]
• delete_table — Delete a BigQuery table from a dataset. [destructive]
• get_dataset — Retrieve BigQuery dataset metadata.
• get_job — Retrieve BigQuery job metadata.
• get_model — Retrieve BigQuery ML model metadata.
• get_query_results — Poll a BigQuery query job and return a page of results.
• get_routine — Retrieve BigQuery routine metadata.
• get_table — Retrieve BigQuery table metadata, including schema when available.
• insert_all — Insert a small batch of rows into a BigQuery table. [write]
• list_datasets — List BigQuery datasets in a Google Cloud project.
• list_jobs — List BigQuery jobs in a Google Cloud project.
• list_models — List BigQuery ML models in a dataset.
• list_projects — List Google Cloud projects accessible to BigQuery.
• list_routines — List BigQuery routines in a dataset.
• list_table_data — List rows from a BigQuery table.
• list_tables — List BigQuery tables in a dataset.
• patch_dataset — Patch BigQuery dataset metadata. [write]
• patch_model — Patch BigQuery ML model metadata such as friendly name, description, or labels. [write]
• patch_table — Patch BigQuery table metadata. [write]
• query — Run a BigQuery SQL query and return the first page of results.
• start_extract_job_to_gcs — Start an asynchronous BigQuery extract job to Cloud Storage objects. [write]
• start_load_job_from_gcs — Start an asynchronous BigQuery load job from Cloud Storage objects. [write]
• start_query_job — Start an asynchronous BigQuery query job. [write]
• update_dataset — Replace BigQuery dataset metadata with the supplied dataset resource fields. [destructive]
• update_routine — Replace BigQuery routine metadata and definition fields. [destructive]
• update_table — Replace BigQuery table metadata with the supplied table resource fields. [destructive]

Safety#

• Untagged actions are reads (get / list / search) — safe to run directly.
• Actions tagged [write] change Google BigQuery state — confirm the exact payload and effect with the user before running.
• Actions tagged [destructive] remove or overwrite data — always confirm the target and get explicit approval first.

First-time setup#

These are one-time steps — do not repeat them on every call. Run a step only when a command fails for the matching reason.

• oo: command not found — install the oo CLI (other platforms: https://cli.oomol.com/install-guide.md): bash

  curl -fsSL https://cli.oomol.com/install.sh | bash    # macOS / Linux
  
  

  powershell

  irm https://cli.oomol.com/install.ps1 | iex           # Windows PowerShell
  
  

• Not signed in / authentication error — sign in to your OOMOL account once: bash

  oo auth login
  
  

• scope_missing / credential_expired / app_not_ready / app_not_found — Google BigQuery is not connected, or the connection expired or lacks a scope. Connect once (auth type: OAuth2) at: text

  https://console.oomol.com/app-connections?provider=google_bigquery
  
  

• HTTP 402 / OOMOL_INSUFFICIENT_CREDIT — billing stop. Recharge at https://console.oomol.com/billing/token-recharge before retrying.

Resources#

• Google BigQuery homepage: https://cloud.google.com/bigquery