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Gcp Bq Data Loading

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-loading -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-loading/SKILL.md
Scanned
1 file, 522 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 enables AI agents to load data into Google BigQuery tables. It handles the complexities of connecting to GCP, authenticating, and formatting data for efficient loading. The agent can specify the source file (e.g., CSV, JSON), target dataset and table within BigQuery, and any necessary transformation options.

When to use it

  • Automated Data Pipelines: Schedule regular data imports from external sources into your BigQuery warehouse.
  • Data Migration: Quickly move datasets from local files or cloud storage (like Google Cloud Storage) into BigQuery for analysis.
  • Real-time Analytics: Ingest streaming data directly into BigQuery to power dashboards and reports.
  • ETL Processes: Integrate this skill as part of a larger Extract, Transform, Load (ETL) workflow.

Key capabilities

  • Connects to Google Cloud Platform (GCP).
  • Loads data from files into BigQuery tables.
  • Supports various file formats (CSV, JSON).
  • Handles authentication with GCP.

Example prompts

  • "Load the CSV file 'customer_data.csv' from my local machine into the 'marketing_dataset.customers' table in BigQuery."
  • "Import the JSON data from Google Cloud Storage bucket 'my-bucket' and file 'sales_data.json' into the 'analytics.orders' BigQuery table."
  • “Load the data, skipping the first row of headers.”

Tips & gotchas

  • Ensure your AI agent has appropriate permissions to access GCP resources and write to BigQuery datasets.
  • Large files may require configuring partitioning or clustering for optimal loading performance in BigQuery.