⚡

// Skill profile

Google BigQuery

Name: Google BigQuery
Author: byungkyu

name: google-bigquery

by byungkyu · published 2026-03-22

日历管理数据处理加密货币

Total installs

Stars

★ 0

Last updated

2026-03

// Install command

$ claw add gh:byungkyu/byungkyu-google-bigquery

View on GitHub

// Full documentation

---

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

1. Sign in or create an account at [maton.ai](https://maton.ai)

2. Go to [maton.ai/settings](https://maton.ai/settings)

3. 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.

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

**Query Parameters:**

`maxResults` - Maximum number of results to return

`pageToken` - Token for pagination

`startIndex` - Zero-based index of the starting row

**Response:**

{
  "kind": "bigquery#tableDataList",
  "totalRows": "100",
  "rows": [
    {
      "f": [
        {"v": "1"},
        {"v": "Alice"},
        {"v": "1.7710597807E9"}
      ]
    }
  ],
  "pageToken": "..."
}

#### Insert Table Data (Streaming)

Insert rows into a table using streaming insert. Note: Requires BigQuery paid tier.

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

{
  "rows": [
    {"json": {"id": 1, "name": "Alice"}},
    {"json": {"id": 2, "name": "Bob"}}
  ]
}

Jobs and Queries

#### Run Query (Synchronous)

Execute a SQL query and return results directly.

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

{
  "query": "SELECT * FROM `my_dataset.my_table` LIMIT 10",
  "useLegacySql": false,
  "maxResults": 100
}

**Response:**

{
  "kind": "bigquery#queryResponse",
  "schema": {
    "fields": [
      {"name": "id", "type": "INTEGER"},
      {"name": "name", "type": "STRING"}
    ]
  },
  "jobReference": {
    "projectId": "my-project",
    "jobId": "job_abc123",
    "location": "US"
  },
  "totalRows": "2",
  "rows": [
    {"f": [{"v": "1"}, {"v": "Alice"}]},
    {"f": [{"v": "2"}, {"v": "Bob"}]}
  ],
  "jobComplete": true,
  "totalBytesProcessed": "1024"
}

**Query Parameters:**

`useLegacySql` - Use legacy SQL syntax (default: false for GoogleSQL)

`maxResults` - Maximum results per page

`timeoutMs` - Query timeout in milliseconds

#### Create Job (Asynchronous)

Submit a job for asynchronous execution.

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

{
  "configuration": {
    "query": {
      "query": "SELECT * FROM `my_dataset.my_table`",
      "useLegacySql": false,
      "destinationTable": {
        "projectId": "{projectId}",
        "datasetId": "{datasetId}",
        "tableId": "results_table"
      },
      "writeDisposition": "WRITE_TRUNCATE"
    }
  }
}

#### List Jobs

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

**Query Parameters:**

`maxResults` - Maximum number of results to return

`pageToken` - Token for pagination

`stateFilter` - Filter by job state: `done`, `pending`, `running`

`projection` - `full` or `minimal`

**Response:**

{
  "kind": "bigquery#jobList",
  "jobs": [
    {
      "id": "my-project:US.job_abc123",
      "jobReference": {
        "projectId": "my-project",
        "jobId": "job_abc123",
        "location": "US"
      },
      "state": "DONE",
      "statistics": {
        "creationTime": "1771059781456",
        "startTime": "1771059782203",
        "endTime": "1771059782324"
      }
    }
  ]
}

#### Get Job

GET /google-bigquery/bigquery/v2/projects/{projectId}/jobs/{jobId}

**Query Parameters:**

`location` - Job location (e.g., "US", "EU")

#### Get Query Results

Retrieve results from a completed query job.

GET /google-bigquery/bigquery/v2/projects/{projectId}/queries/{jobId}

**Query Parameters:**

`location` - Job location

`maxResults` - Maximum results per page

`pageToken` - Token for pagination

`startIndex` - Zero-based starting row

#### Cancel Job

POST /google-bigquery/bigquery/v2/projects/{projectId}/jobs/{jobId}/cancel

**Query Parameters:**

`location` - Job location

Pagination

BigQuery uses token-based pagination. List responses include a `pageToken` when more results exist:

GET /google-bigquery/bigquery/v2/projects/{projectId}/datasets?maxResults=10&pageToken={token}

**Response:**

{
  "datasets": [...],
  "nextPageToken": "eyJvZmZzZXQiOjEwfQ=="
}

Use the `nextPageToken` value as `pageToken` in subsequent requests.

Code Examples

JavaScript

// Run a query
const response = await fetch(
  'https://gateway.maton.ai/google-bigquery/bigquery/v2/projects/my-project/queries',
  {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.MATON_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      query: 'SELECT * FROM `my_dataset.my_table` LIMIT 10',
      useLegacySql: false
    })
  }
);
const data = await response.json();
console.log(data.rows);

Python

import os
import requests

# Run a query
response = requests.post(
    'https://gateway.maton.ai/google-bigquery/bigquery/v2/projects/my-project/queries',
    headers={'Authorization': f'Bearer {os.environ["MATON_API_KEY"]}'},
    json={
        'query': 'SELECT * FROM `my_dataset.my_table` LIMIT 10',
        'useLegacySql': False
    }
)
data = response.json()
for row in data.get('rows', []):
    print([field['v'] for field in row['f']])

Schema Field Types

Common BigQuery data types for table schemas:

| Type | Description |

|------|-------------|

| `STRING` | Variable-length character data |

| `INTEGER` | 64-bit signed integer |

| `FLOAT` | 64-bit IEEE floating point |

| `BOOLEAN` | True or false |

| `TIMESTAMP` | Absolute point in time |

| `DATE` | Calendar date |

| `TIME` | Time of day |

| `DATETIME` | Date and time |

| `BYTES` | Variable-length binary data |

| `NUMERIC` | Exact numeric value with 38 digits of precision |

| `BIGNUMERIC` | Exact numeric value with 76+ digits of precision |

| `GEOGRAPHY` | Geographic data |

| `JSON` | JSON data |

| `RECORD` | Nested fields (also called STRUCT) |

**Field Modes:**

`NULLABLE` - Field can be null (default)

`REQUIRED` - Field cannot be null

`REPEATED` - Field is an array

Notes

Project IDs are typically in the format `project-name` or `project-name-12345`

Dataset IDs follow naming rules: letters, numbers, underscores (max 1024 characters)

Table IDs follow same naming rules as datasets

Job IDs are generated by BigQuery and include location prefix

Query results use `f` (fields) and `v` (value) structure

Streaming inserts require BigQuery paid tier (not available in free tier)

Use `useLegacySql: false` for GoogleSQL (standard SQL) syntax

IMPORTANT: When using curl commands, use `curl -g` when URLs contain brackets to disable glob parsing

IMPORTANT: When piping curl output to `jq` or other commands, environment variables like `$MATON_API_KEY` may not expand correctly in some shell environments

Error Handling

| Status | Meaning |

|--------|---------|

| 400 | Missing Google BigQuery connection or invalid request |

| 401 | Invalid or missing Maton API key |

| 403 | Access denied (insufficient permissions or quota exceeded) |

| 404 | Resource not found (project, dataset, table, or job) |

| 409 | Resource already exists |

| 429 | Rate limited |

| 4xx/5xx | Passthrough error from BigQuery API |

Troubleshooting: API Key Issues

1. Check that the `MATON_API_KEY` environment variable is set:

echo $MATON_API_KEY

2. Verify the API key is valid by listing connections:

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

Troubleshooting: Invalid App Name

1. Ensure your URL path starts with `google-bigquery`. For example:

Correct: `https://gateway.maton.ai/google-bigquery/bigquery/v2/projects`

Incorrect: `https://gateway.maton.ai/bigquery/v2/projects`

Resources

[BigQuery API Overview](https://cloud.google.com/bigquery/docs/reference/rest)

[Datasets](https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets)

[Tables](https://cloud.google.com/bigquery/docs/reference/rest/v2/tables)

[Jobs](https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs)

[Tabledata](https://cloud.google.com/bigquery/docs/reference/rest/v2/tabledata)

[Standard SQL Reference](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax)

[Maton Community](https://discord.com/invite/dBfFAcefs2)

[Maton Support](mailto:support@maton.ai)

// Comments

// Related skills

More tools from the same signal band

Order food/drinks (点餐) on an Android device paired as an OpenClaw node. Uses in-app menu and cart; add goods, view cart, submit order (demo, no real payment).

Sign plugins, rotate agent credentials without losing identity, and publicly attest to plugin behavior with verifiable claims and authenticated transfers.

The philosophical layer for AI agents. Maps behavior to Spinoza's 48 affects, calculates persistence scores, and generates geometric self-reports. Give your...

日历管理数据处理

1 installs★ 0