G

Google BigQuery

google-bigquery

Access Google BigQuery to run SQL queries, manage datasets and tables, and perform large-scale data analysis with OAuth authentication via the Maton API.

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安装

选择你使用的 Agent

方法一：命令行安装（推荐）

关于 Google BigQuery

--- name: google-bigquery description: | Google BigQuery API integration with managed OAuth. Run SQL queries, manage datasets and tables, and analyze data at scale. Use this skill when users want to query BigQuery data, create or manage datasets/tables, run analytics jobs, or work with BigQuery resources. For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway). compatibility: Requires network access and valid Maton API key metadata: author: maton version: "1.0" clawdbot: emoji: 🧠 homepage: "https://maton.ai" requires: env: - MATON_API_KEY ---

Google BigQuery

Access the Google BigQuery API with managed OAuth authentication. Run SQL queries, manage datasets and tables, and analyze data at scale.

Quick Start

# Run a simple query
python <<'EOF'
import urllib.request, os, json
data = json.dumps({'query': 'SELECT 1 as test_value', 'useLegacySql': False}).encode()
req = urllib.request.Request('https://gateway.maton.ai/google-bigquery/bigquery/v2/projects/{projectId}/queries', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Base URL

https://gateway.maton.ai/google-bigquery/bigquery/v2/{resource-path}

Replace {resource-path} with the actual BigQuery API endpoint path. The gateway proxies requests to bigquery.googleapis.com and automatically injects your OAuth token.

Authentication

All requests require the Maton API key in the Authorization header:

Authorization: Bearer $MATON_API_KEY

Environment Variable: Set your API key as MATON_API_KEY:

export MATON_API_KEY="YOUR_API_KEY"

Getting Your API Key

Sign in or create an account at maton.ai
Go to maton.ai/settings
Copy your API key

Connection Management

Manage your Google BigQuery OAuth connections at https://ctrl.maton.ai.

List Connections

python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections?app=google-bigquery&status=ACTIVE')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Create Connection

python <<'EOF'
import urllib.request, os, json
data = json.dumps({'app': 'google-bigquery'}).encode()
req = urllib.request.Request('https://ctrl.maton.ai/connections', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Get Connection

python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections/{connection_id}')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Response:

{
  "connection": {
    "connection_id": "c8463a31-e5b4-4e52-9a32-e78dcd7ba7b1",
    "status": "ACTIVE",
    "creation_time": "2026-02-14T09:02:02.780520Z",
    "last_updated_time": "2026-02-14T09:02:19.977436Z",
    "url": "https://connect.maton.ai/?session_token=...",
    "app": "google-bigquery",
    "metadata": {}
  }
}

Open the returned url in a browser to complete OAuth authorization.

Delete Connection

python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections/{connection_id}', method='DELETE')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Specifying Connection

If you have multiple Google BigQuery connections, specify which one to use with the Maton-Connection header:

python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://gateway.maton.ai/google-bigquery/bigquery/v2/projects')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Maton-Connection', 'c8463a31-e5b4-4e52-9a32-e78dcd7ba7b1')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

If omitted, the gateway uses the default (oldest) active connection.

API Reference

Projects

List Projects

List all projects accessible to the authenticated user.

GET /google-bigquery/bigquery/v2/projects

Response:

{
  "kind": "bigquery#projectList",
  "projects": [
    {
      "id": "my-project-123",
      "numericId": "822245862053",
      "projectReference": {
        "projectId": "my-project-123"
      },
      "friendlyName": "My Project"
    }
  ],
  "totalItems": 1
}

Datasets

List Datasets

GET /google-bigquery/bigquery/v2/projects/{projectId}/datasets

Query Parameters:

maxResults - Maximum number of results to return
pageToken - Token for pagination
all - Include hidden datasets if true

Get Dataset

GET /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}

Create Dataset

POST /google-bigquery/bigquery/v2/projects/{projectId}/datasets
Content-Type: application/json

{
  "datasetReference": {
    "datasetId": "my_dataset",
    "projectId": "{projectId}"
  },
  "description": "My dataset description",
  "location": "US"
}

Response:

{
  "kind": "bigquery#dataset",
  "id": "my-project:my_dataset",
  "datasetReference": {
    "datasetId": "my_dataset",
    "projectId": "my-project"
  },
  "location": "US",
  "creationTime": "1771059780773"
}

Update Dataset (PATCH)

PATCH /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}
Content-Type: application/json

{
  "description": "Updated description"
}

Delete Dataset

DELETE /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}

Query Parameters:

deleteContents - If true, delete all tables in the dataset (default: false)

Tables

List Tables

GET /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}/tables

Query Parameters:

maxResults - Maximum number of results to return
pageToken - Token for pagination

Get Table

GET /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}/tables/{tableId}

Create Table

POST /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}/tables
Content-Type: application/json

{
  "tableReference": {
    "projectId": "{projectId}",
    "datasetId": "{datasetId}",
    "tableId": "my_table"
  },
  "schema": {
    "fields": [
      {"name": "id", "type": "INTEGER", "mode": "REQUIRED"},
      {"name": "name", "type": "STRING", "mode": "NULLABLE"},
      {"name": "created_at", "type": "TIMESTAMP", "mode": "NULLABLE"}
    ]
  }
}

Response:

{
  "kind": "bigquery#table",
  "id": "my-project:my_dataset.my_table",
  "tableReference": {
    "projectId": "my-project",
    "datasetId": "my_dataset",
    "tableId": "my_table"
  },
  "schema": {
    "fields": [
      {"name": "id", "type": "INTEGER", "mode": "REQUIRED"},
      {"name": "name", "type": "STRING", "mode": "NULLABLE"},
      {"name": "created_at", "type": "TIMESTAMP", "mode": "NULLABLE"}
    ]
  },
  "numRows": "0",
  "type": "TABLE"
}

Update Table (PATCH)

PATCH /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}/tables/{tableId}
Content-Type: application/json

{
  "description": "Updated table description"
}

Delete Table

DELETE /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}/tables/{tableId}

Table Data

List Table Data

Retrieve rows from a table.

...

Prompt 示例

安装 Google BigQuery 后，可以对 AI 说这些话来触发它

U

Help me get started with Google BigQuery

A

Explains what Google BigQuery does, walks through the setup, and runs a quick demo based on your current project

U

Use Google BigQuery to access Google BigQuery to run SQL queries, manage datasets and tabl...

A

Invokes Google BigQuery with the right parameters and returns the result directly in the conversation

U

What can I do with Google BigQuery in my data & analytics workflow?

A

Lists the top use cases for Google BigQuery, with example commands for each scenario

常见问题

如何安装 Google BigQuery？▾

将技能文件夹放到 ~/.claude/skills/google-bigquery/ 目录（个人级，所有项目可用），或 .claude/skills/google-bigquery/（项目级）。重启 AI 客户端后，用 /google-bigquery 主动调用，或让 AI 根据上下文自动发现并使用。

Google BigQuery 支持哪些 AI 平台？▾

Google BigQuery 支持 Claude、Cursor、OpenClaw，可与这些 AI 平台无缝集成，扩展其能力。

Google BigQuery 是免费的吗？▾

Google BigQuery 可免费安装使用。请查阅仓库了解许可证信息。

Google BigQuery 有什么功能？▾

Access Google BigQuery to run SQL queries, manage datasets and tables, and perform large-scale data analysis with OAuth authentication via the Maton API.

Google BigQuery 属于哪个分类？▾

Google BigQuery 属于「Data & Analytics」分类，该分类的技能帮助 AI 智能体在此领域执行专业任务。

使用场景

Getting Started with Google BigQuery→Automate Data & Analytics Workflows with Google BigQuery→Team Collaboration with Google BigQuery→

Google BigQuery

安装

关于 Google BigQuery

Google BigQuery

Quick Start

Base URL

Authentication

Getting Your API Key

Connection Management

List Connections

Create Connection

Get Connection

Delete Connection

Specifying Connection

API Reference

Projects

List Projects

Datasets

List Datasets

Get Dataset

Create Dataset

Update Dataset (PATCH)

Delete Dataset

Tables

List Tables

Get Table

Create Table

Update Table (PATCH)

Delete Table

Table Data

List Table Data

Prompt 示例

常见问题

使用场景

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