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Google BigQuery

Data & APIs

Google BigQuery (cloud.google.com). Use this skill for ANY Google BigQuery request — reading, creating, updating, and deleting data. Whenever a task involves Google BigQuery, use this skill instead of calling the API directly.

Install

openclaw skills install oo-google-bigquery

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.

Category: Data & Analytics, Developer Tools. Exposes 32 action(s).

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:

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

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

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 below links to a reference file with its purpose and exact commands. Read the linked file, then fetch the live schema with oo connector schema before constructing --data.

Available actions

  • cancel_job — Cancel a BigQuery job.
  • create_dataset — Create a BigQuery dataset.
  • create_routine — Create a BigQuery routine such as a user-defined function or stored procedure.
  • create_table — Create a BigQuery table.
  • delete_dataset — Delete a BigQuery dataset, optionally deleting contained tables when explicitly requested.
  • delete_model — Delete a BigQuery ML model from a dataset.
  • delete_routine — Delete a BigQuery routine from a dataset.
  • delete_table — Delete a BigQuery table from a dataset.
  • 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.
  • 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.
  • patch_model — Patch BigQuery ML model metadata such as friendly name, description, or labels.
  • patch_table — Patch BigQuery table metadata.
  • 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.
  • start_load_job_from_gcs — Start an asynchronous BigQuery load job from Cloud Storage objects.
  • start_query_job — Start an asynchronous BigQuery query job.
  • update_dataset — Replace BigQuery dataset metadata with the supplied dataset resource fields.
  • update_routine — Replace BigQuery routine metadata and definition fields.
  • update_table — Replace BigQuery table metadata with the supplied table resource fields.

Safety

  • Read actions (get / list / search) are safe to run directly.
  • Create, update, send, or post actions change Google BigQuery state — confirm the exact payload and effect with the user before running.
  • Delete or remove actions are destructive — 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):

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

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

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

    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:

    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

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