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Gcp Bq Data Export

Checked
funnelenvy·vlatest·cloud·MIT

Helps with Google Cloud, data as part of deploying and managing cloud infrastructure workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add funnelenvy/agents_webinar_demos --skill gcp-bq-data-export -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

funnelenvy

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned funnelenvy/agents_webinar_demos at 1804741b81.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
1804741b81
Manifest
.claude/skills/gcp-bq-data-export/SKILL.md
Scanned
1 file, 437 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill allows AI agents to export data from Google BigQuery tables. It can specify the table, destination (e.g., Cloud Storage bucket), and file format for exporting data. The exported data can then be used for analysis, reporting, or integration with other systems.

When to use it

  • You need to extract a subset of data from a large BigQuery table for further processing.
  • You want to create backups of your BigQuery tables in a different format (e.g., CSV, JSON).
  • You're building a data pipeline that requires data from BigQuery as input.
  • You need to share BigQuery data with users or systems that don’t have direct access to BigQuery.

Key capabilities

  • Specifies the source BigQuery table for export.
  • Defines the destination Cloud Storage bucket and path.
  • Supports various file formats (e.g., CSV, JSON).

Example prompts

  • "Export the 'orders' table from my BigQuery dataset to a CSV file in my Cloud Storage bucket."
  • "Can you export all data from the sales_data table into a JSON file?"
  • “Please extract the ‘customer_transactions’ table and save it as a backup in Cloud Storage.”

Tips & gotchas

  • Ensure your AI agent has appropriate permissions to access both BigQuery and Cloud Storage.
  • Large datasets may take considerable time to export, so consider using filtering or partitioning for efficiency.