Skip to content
TrustedSkillsbeta
Back to all skills

Gcp Batch Inference

Checked
viktor-ferenczi·vlatest·cloud·MIT

Provides Google Cloud guidance and assistance for deploying and managing cloud infrastructure.

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 viktor-ferenczi/skills --skill gcp-batch-inference -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

viktor-ferenczi

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 viktor-ferenczi/skills at 36a2adc6e1.

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

Commit
36a2adc6e1
Manifest
skills/gcp-batch-inference/SKILL.md
Scanned
1 file, 79 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 perform batch inference using Google Cloud Platform (GCP). It enables processing large datasets through deployed models, generating predictions at scale. The skill streamlines the process of submitting jobs and managing resources within GCP for efficient model serving.

When to use it

  • Image Classification on a Large Dataset: Process thousands of images stored in Google Cloud Storage to classify them using a trained image recognition model.
  • Sentiment Analysis of Customer Reviews: Analyze a large batch of customer reviews from a CSV file stored in GCP to determine overall sentiment trends.
  • Predictive Maintenance for Industrial Equipment: Run predictions on sensor data collected from numerous industrial machines to identify potential maintenance needs.
  • Generating Leads from Marketing Data: Process a list of leads in Google Cloud Storage through a lead scoring model to prioritize sales efforts.

Key capabilities

  • Submitting batch inference jobs to GCP.
  • Managing resources for efficient model serving.
  • Processing large datasets stored in Google Cloud Storage.
  • Generating predictions at scale using deployed models.

Example prompts

  • "Run a batch prediction job on the 'customer_reviews' dataset in GCS."
  • "Submit an inference request to GCP using my deployed image classification model."
  • "Process all images in the 'images/unprocessed' folder and save predictions to 'predictions/output'."

Tips & gotchas

  • Ensure your Google Cloud project has appropriate permissions configured for the AI agent to access resources.
  • The skill requires a deployed model within GCP that is compatible with batch inference.