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Agent skill

Google Bigquery

by LeoYeAI in LeoYeAI/openclaw-master-skills

Google BigQuery API integration with managed OAuth. An agent skill from LeoYeAI/openclaw-master-skills.

MITAuto-check passedBackend & APIs

Install Google Bigquery

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill google-bigquery -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills google-bigquery --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/google-bigquery .claude/skills/google-bigquery && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
google-bigquery
GitHub stars
2.2k
Token cost
~4.1k tokens
SKILL.md length
735 words
Files
3
Skills in repo
972
Repo updated
First seen
Licence
MIT

At a glance

Google BigQuery API integration with managed OAuth. An agent skill from LeoYeAI/openclaw-master-skills.

  • Works in 3 steps: Sign in or create an account at maton.ai → Go to maton.ai/settings → Copy your API key
  • Users want to query BigQuery data
  • SKILL.md covers Quick Start, Base URL, Authentication and Connection Management, plus 7 more sections
  • Calls python and curl; reaches gateway.maton.ai and ctrl.maton.ai; needs MATON_API_KEY

What it does

Google Bigquery is an agent skill from LeoYeAI/openclaw-master-skills. 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).

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `_meta.json`). Compatibility notes: Requires network access and valid Maton API key

It sits in Backend & APIs, covering Data warehousing, Microservices and SQL. It works with Google BigQuery and SQL. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Users want to query BigQuery data
  • Manage datasets/tables
  • Run analytics jobs
  • Work with BigQuery resources

Example prompts

  • “/google-bigquery”

Requirements

  • Python 3
  • A credential in MATON_API_KEY
  • A credential in YOUR_API_KEY
  • Compatibility (from SKILL.md): Requires network access and valid Maton API key

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

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

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python
    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • gateway.maton.ai
    • ctrl.maton.ai
    • connect.maton.ai

    Also links to:

    • cloud.google.com
    • maton.ai
    • discord.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • MATON_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires network access and valid Maton API key

    From compatibility in the SKILL.md frontmatter.

Context cost

Google Bigquery loads about 4.1k tokens when it runs. Until then it costs about 95 tokens; SKILL.md has 735 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 735 words, ~4,095 tokens.

Download SKILL.mdSave it as .claude/skills/google-bigquery/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
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
metadata.version
1.0

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

bash
# 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:

bash
export MATON_API_KEY="YOUR_API_KEY"
Getting Your API Key
  1. Sign in or create an account at maton.ai
  2. Go to maton.ai/settings
  3. Copy your API key

Connection Management

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

List Connections
bash
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
bash
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
bash
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:

json
{
  "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
bash
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:

bash
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.

bash
GET /google-bigquery/bigquery/v2/projects

Response:

json
{
  "kind": "bigquery#projectList",
  "projects": [
    {
      "id": "my-project-123",
      "numericId": "822245862053",
      "projectReference": {
        "projectId": "my-project-123"
      },
      "friendlyName": "My Project"
    }
  ],
  "totalItems": 1
}
Datasets
List Datasets
bash
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
bash
GET /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}
Create Dataset
bash
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:

json
{
  "kind": "bigquery#dataset",
  "id": "my-project:my_dataset",
  "datasetReference": {
    "datasetId": "my_dataset",
    "projectId": "my-project"
  },
  "location": "US",
  "creationTime": "1771059780773"
}
Update Dataset (PATCH)
bash
PATCH /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}
Content-Type: application/json

{
  "description": "Updated description"
}
Delete Dataset
bash
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
bash
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
bash
GET /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}/tables/{tableId}
Create Table
bash
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:

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

{
  "description": "Updated table description"
}
Delete Table
bash
DELETE /google-bigquery/bigquery/v2/projects/{projectId}/datasets/{datasetId}/tables/{tableId}
Table Data
List Table Data

Retrieve rows from a table.

bash
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:

json
{
  "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.

bash
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.

bash
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:

json
{
  "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.

bash
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
bash
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:

json
{
  "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
bash
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.

bash
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
bash
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:

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

Response:

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

Use the nextPageToken value as pageToken in subsequent requests.

Code Examples

JavaScript
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
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']])
Show full SKILL.md (308 more words)Show less

Schema Field Types

Common BigQuery data types for table schemas:

TypeDescription
STRINGVariable-length character data
INTEGER64-bit signed integer
FLOAT64-bit IEEE floating point
BOOLEANTrue or false
TIMESTAMPAbsolute point in time
DATECalendar date
TIMETime of day
DATETIMEDate and time
BYTESVariable-length binary data
NUMERICExact numeric value with 38 digits of precision
BIGNUMERICExact numeric value with 76+ digits of precision
GEOGRAPHYGeographic data
JSONJSON data
RECORDNested 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

StatusMeaning
400Missing Google BigQuery connection or invalid request
401Invalid or missing Maton API key
403Access denied (insufficient permissions or quota exceeded)
404Resource not found (project, dataset, table, or job)
409Resource already exists
429Rate limited
4xx/5xxPassthrough error from BigQuery API
Troubleshooting: API Key Issues
  1. Check that the MATON_API_KEY environment variable is set:
bash
echo $MATON_API_KEY
  1. Verify the API key is valid by listing connections:
bash
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

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/google-bigquery of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • LICENSE.txt
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Google Bigquery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Google Bigquery compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Google Bigquery this skillLeoYeAI/openclaw-master-skills2.2k—~4.1kAutomated safety check: PassMIT
Semantic Analystsidequery/sidemantic129—~982Automated safety check: PassAGPL-3.0
Analysis Artifactswarpdotdev/oz-skills825—~1.1kAutomated safety check: PassMIT
Deploying On GCPancoleman/ai-design-components526—~3.9kAutomated safety check: PassMIT
Analyzing Dataastronomer/agents451—~1.3kAutomated safety check: PassApache-2.0
Bigquery Graphgoogle/adk-python22k—~4.8kAutomated safety check: PassApache-2.0

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    LeoYeAI/openclaw-master-skills

    Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.

    2.2k GitHub stars~4.4k tokensUpdated 2 mo ago
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Questions about Google Bigquery

What does Google Bigquery do?

Google BigQuery API integration with managed OAuth. An agent skill from LeoYeAI/openclaw-master-skills. Google Bigquery is an agent skill from LeoYeAI/openclaw-master-skills. Google BigQuery API integration with managed OAuth.

When should I use Google Bigquery?

Google Bigquery fits situations like: users want to query BigQuery data; manage datasets/tables; run analytics jobs; work with BigQuery resources.

How do I install Google Bigquery in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill google-bigquery -a claude-code`. Or copy the skill folder (skills/google-bigquery in LeoYeAI/openclaw-master-skills) into .claude/skills/google-bigquery in your project. Claude Code loads it when a task matches its description.

How do I install Google Bigquery in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill google-bigquery -a codex`. Or copy the skill folder (skills/google-bigquery in LeoYeAI/openclaw-master-skills) into .agents/skills/google-bigquery in your project. Codex loads it when a task matches its description.

Can I use Google Bigquery in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill google-bigquery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/google-bigquery, .gemini/skills/google-bigquery, .github/skills/google-bigquery and .opencode/skills/google-bigquery in your project.

What does Google Bigquery need to run?

Going by SKILL.md and its folder, Google Bigquery needs the command-line tools its instructions call (python and curl) and credentials named MATON_API_KEY. Our summary lists: Python 3; A credential in MATON_API_KEY; A credential in YOUR_API_KEY. Compatibility (from SKILL.md): Requires network access and valid Maton API key.

Does Google Bigquery access the network?

SKILL.md names 6 domains. In commands or code: gateway.maton.ai, ctrl.maton.ai and connect.maton.ai; the agent is likely to contact these when it follows the instructions. As links in the text: cloud.google.com, maton.ai and discord.com. This is read from the text; nothing was executed.

Is Google Bigquery safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Google Bigquery use?

Google Bigquery is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Google Bigquery use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Google Bigquery?

Skills that share tags, products or a category with Google Bigquery: Semantic Analyst (sidequery/sidemantic, 129 stars), Analysis Artifacts (warpdotdev/oz-skills, 825 stars), Deploying On GCP (ancoleman/ai-design-components, 526 stars) and Analyzing Data (astronomer/agents, 451 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Google Bigquery?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,159 GitHub stars. The repository holds 972 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.