googlebigquery-automation

Overview of googlebigquery-automation skill

What googlebigquery-automation does

googlebigquery-automation is a Claude skill for working with Google BigQuery data through Rube MCP and Composio’s Metabase toolkit. Instead of asking an agent to “query BigQuery” with no tool discipline, this skill tells the agent to discover the current Rube tool schemas first, verify an active Metabase connection, inspect available datasets or metadata, and then run native SQL or MBQL-style analytical requests through Metabase.

Best-fit users and jobs

This skill is best for analysts, data engineers, BI operators, and product teams who already expose BigQuery data through Metabase and want an AI assistant to help run queries, explore table structure, summarize datasets, or generate repeatable analysis steps. The strongest use case is googlebigquery-automation for Data Analysis: turning a business question into a checked query workflow that respects available schemas instead of guessing table names.

Key differentiators and adoption blockers

The main differentiator is the required Rube MCP flow: RUBE_SEARCH_TOOLS first, then connection management, then Metabase-backed query execution. That reduces brittle tool calls when Composio tool schemas change. The main blocker is architecture: this is not a direct BigQuery API skill. You need Rube MCP available, a Metabase connection, and Metabase configured to access your BigQuery data source.

How to Use googlebigquery-automation skill

googlebigquery-automation install and setup context

Install the skill from the Composio skill collection in a client that supports Claude skills, for example:

npx skills add ComposioHQ/awesome-claude-skills --skill googlebigquery-automation

Then add Rube MCP as an MCP server using https://rube.app/mcp. In practice, the skill depends on these runtime checks more than on local files:

1. Confirm RUBE_SEARCH_TOOLS is available.
2. Use RUBE_MANAGE_CONNECTIONS with toolkit metabase.
3. Complete the returned auth flow if the connection is not ACTIVE.
4. Only run query workflows after the Metabase connection is active.

Read composio-skills/googlebigquery-automation/SKILL.md first; it is the primary source file and contains the workflow assumptions.

Inputs the skill needs before querying

For good googlebigquery-automation usage, give the agent a clear analytical goal, the expected output format, and any known constraints. If you know database, schema, table, date range, metric definitions, or row limits, include them. If you do not know the schema, ask the agent to inspect metadata before writing SQL.

Weak prompt:

“Analyze revenue in BigQuery.”

Stronger prompt:

“Use googlebigquery-automation. First search Rube tools and verify the Metabase connection. Then inspect available BigQuery tables related to orders, payments, and customers. Find monthly gross revenue for 2024, exclude refunded transactions if a refund status field exists, and return SQL plus a short table of results. Limit exploratory queries to safe row counts.”

This improves results because it tells the agent how to discover schemas, what business rule to apply, and how to avoid premature full-table queries.

Practical workflow for analysis

A reliable googlebigquery-automation guide usually follows this sequence:

1. Search tools with RUBE_SEARCH_TOOLS to get current function names and schemas.
2. Confirm the Metabase connection is active.
3. Explore databases, datasets, cards, or metadata exposed by Metabase.
4. Draft native SQL only after table and field names are confirmed.
5. Run a limited query first.
6. Review errors, column names, and sample rows.
7. Expand to the final query and summarize assumptions.

For native SQL, the skill points toward METABASE_POST_API_DATASET with a native query type. For BI-style work, MBQL can be useful when you want Metabase’s structured query model rather than raw SQL.

Tips that materially improve output quality

Ask for both the query and the reasoning trail. Require the agent to state which tables and fields it used, which assumptions remain unresolved, and whether results came from a limited sample or final query. For production-sensitive analysis, request a dry-run style plan before execution: “list intended tables, filters, joins, and limits before running.” This helps catch expensive joins, missing partition filters, and ambiguous metrics.

googlebigquery-automation skill FAQ

Is googlebigquery-automation a direct BigQuery connector?

No. The skill works through Rube MCP and Composio’s Metabase toolkit. BigQuery is reached through a Metabase instance that already has BigQuery configured as a data source. If your environment requires direct Google Cloud credentials, IAM role management, BigQuery jobs API usage, or dataset administration, this skill may not cover that path.

When is this better than an ordinary prompt?

An ordinary prompt can draft SQL, but it often guesses table names or ignores the tool connection state. The googlebigquery-automation skill is better when you need the agent to use live tool discovery, verify Metabase access, inspect metadata, and execute queries through the available MCP tools. It is especially useful when tool schemas may change and the agent must search before calling.

Is it suitable for beginners?

It can help beginners ask better analytical questions, but it assumes some data literacy. You should understand basic SQL concepts, date filtering, joins, aggregation, and the difference between a sample query and a final result. Beginners should start with metadata exploration and small row limits rather than asking for broad analysis across unknown tables.

When should I not use this skill?

Do not use it for BigQuery infrastructure administration, dataset creation, permission changes, data loading jobs, or cost governance unless those capabilities are explicitly exposed through your connected tools. Also avoid it when Metabase does not have access to the required BigQuery project, when the connection is inactive, or when your question requires data that is not modeled or reachable in Metabase.

How to Improve googlebigquery-automation skill

Improve googlebigquery-automation prompts with constraints

The highest-impact improvement is better prompt specificity. Include metric definitions, grain, filters, time zone, date range, and expected output. For example: “daily active users by event date in UTC, excluding internal accounts, for the last 30 complete days” is much safer than “show active users.” Clear constraints help the agent choose correct grouping, avoid accidental partial-day data, and explain assumptions.

Common failure modes to watch for

Typical failures include querying before checking tool schemas, assuming table names, using stale Metabase metadata, omitting partition filters, joining on the wrong key, or treating sample output as final. If a query fails, ask the agent to inspect the error, re-check available fields, and revise only the affected part instead of rewriting the whole analysis from scratch.

Iterate after the first output

After the first result, ask follow-up questions that validate the analysis: “show the SQL,” “list excluded records,” “compare this to the prior period,” “add confidence notes,” or “explain why this table was chosen.” For high-stakes reporting, request a second pass that checks row counts, null rates, duplicate keys, and whether filters match the business definition.

Strengthen the skill for team use

Teams can improve googlebigquery-automation by documenting common datasets, canonical metrics, naming conventions, safe query limits, and approved Metabase databases in their own project notes. The skill itself has a focused SKILL.md, so local context matters: the more your team supplies trusted metric definitions and table guidance, the less the agent must infer during live analysis.