Skip to content
TrustedSkillsbeta
Back to all skills

Push To Registry

Official
hashicorp·vlatest·cloud·MPL-2.0

This skill pushes Terraform configuration files directly to a specified cloud registry (e.g., AWS S3, Azure Blob Storage), streamlining deployments.

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 hashicorp/agent-skills --skill push-to-registry -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

hashicorp

Matched to the vendor's own GitHub organisation.

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 hashicorp/agent-skills at f706481af9.

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

Commit
f706481af9
Manifest
plugins/packer/skills/push-to-registry/SKILL.md
Scanned
1 file, 192 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

The push-to-registry skill enables AI agents to package and deploy their trained models or artifacts into a centralized registry. This allows for version control, sharing, and reuse of AI components across different projects or teams.

When to use it

  • After training an AI model, you want to store it in a shared repository for future access.
  • You need to automate the deployment pipeline for AI agents by pushing artifacts directly into a registry.
  • Your team uses a centralized model registry and needs consistent versioning of deployed models.

Key capabilities

  • Package trained models or artifacts into standardized formats.
  • Push packages to a centralized registry with metadata support.
  • Enable version control and tracking of deployed AI components.

Example prompts

  • "Package the latest model and push it to the central registry."
  • "Deploy this AI agent's output as a new version in the registry."
  • "Push the trained classifier to the registry with version 1.2."

Tips & gotchas

  • Ensure your registry is properly configured before using this skill to avoid deployment errors.
  • Metadata such as model name, version, and description should be provided for better organization and traceability.