astoryh/nanobot/skills/bigquery-dataset-query

---
name: bigquery-dataset-query
description: Queries a BigQuery dataset and returns the results as a structured data object.
metadata:
  nanobot:
    emoji: 📊
    category: data-analysis
    tags: [bigquery, query, data, analysis]
  dependencies: []
---

## Instructions

This skill allows you to query a BigQuery dataset and retrieve data.  You will provide the project ID, dataset ID, and the SQL query. The nanobot will execute the query and return the results in a structured format (e.g., a list of dictionaries).

**Input:**

The input should be a JSON object with the following keys:

*   `project_id`: (string, required) The Google Cloud project ID where the BigQuery dataset resides.
*   `dataset_id`: (string, required) The ID of the BigQuery dataset to query.
*   `query`: (string, required) The SQL query to execute.  This query *must* be valid BigQuery SQL.

**Example Input:**

```json
{
  "project_id": "my-gcp-project",
  "dataset_id": "my_dataset",
  "query": "SELECT * FROM `my_dataset.my_table` LIMIT 10"
}

Output:

The output will be a JSON object containing the query results. The structure of the results will depend on the query you execute. It will generally be a list of dictionaries, where each dictionary represents a row in the result set and the keys are the column names.

Example Output (based on the example input above):

[
  {
    "column1": "value1",
    "column2": 123,
    "column3": true
  },
  {
    "column1": "value2",
    "column2": 456,
    "column3": false
  },
  // ... more rows
]

Error Handling:

• If the project_id, dataset_id, or query are missing, return an error indicating the missing parameter.
• If the query is invalid or fails to execute in BigQuery, return an error message containing the BigQuery error details.
• If there are any other errors during the process, return a generic error message.

Important Considerations:

• Security: Ensure that the nanobot has the necessary permissions to access the specified BigQuery dataset. This typically involves setting up appropriate IAM roles.
• Query Optimization: Encourage users to write efficient SQL queries to minimize processing time and costs.
• Data Size: Be mindful of the size of the data being returned. Large result sets can consume significant memory and processing resources. Consider using LIMIT and other query optimization techniques to reduce the amount of data retrieved.
• SQL Injection: Sanitize user-provided input to prevent SQL injection vulnerabilities. While this skill doesn't directly take user input into the query, it's a good practice to be aware of this risk.