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Skypilot Multi Cloud Orchestration

Listed
orchestra-research·vlatest·cloud·MIT

Skypilot Multi Cloud Orchestration automates deployments across various clouds, simplifying complex workflows and boosting application portability & efficiency.

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 orchestra-research/ai-research-skills --skill skypilot-multi-cloud-orchestration -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

orchestra-research

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 failed 2 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No credential access: failed

    Touches credential material in 3 places.

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

    What it found

    • 09-infrastructure/skypilot/references/advanced-usage.md:305Kubernetes credentials
      export KUBECONFIG=~/.kube/config
    • 09-infrastructure/skypilot/references/troubleshooting.md:24Kubernetes credentials
      export KUBECONFIG=~/.kube/config
    • 09-infrastructure/skypilot/references/troubleshooting.md:439SSH private keys or config
      ls -la ~/.ssh/sky-key*

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

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

    What it found

    • 09-infrastructure/skypilot/references/troubleshooting.md:181pip install straight from a URL
      pip install torch==2.1.0+cu121 -f https://download.pytorch.org/whl/torch_stable.html

  • 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.

  • Pinned to a commit: passed

    Scanned orchestra-research/ai-research-skills at 773a52944b.

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

Commit
773a52944b
Manifest
09-infrastructure/skypilot/SKILL.md
Scanned
3 files, 1,381 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

Skypilot Multi Cloud Orchestration automates deployments and management of machine learning (ML) workloads across a wide range of cloud providers, including AWS, GCP, Azure, Kubernetes, Lambda, and RunPod. It simplifies complex workflows by providing a unified interface for over 20 different cloud platforms while optimizing costs through automatic selection of the cheapest available region or cloud. The skill also supports long-running jobs on spot instances with automated recovery capabilities.

When to use it

  • You need to run ML workloads across multiple clouds (AWS, GCP, Azure).
  • Cost optimization is a priority and you want automatic cloud/region selection.
  • You're running long jobs that benefit from using spot instances with auto-recovery.
  • You require management of distributed multi-node training environments.
  • You want to avoid vendor lock-in by distributing workloads across various providers.

Key capabilities

  • Multi-cloud support: Works with AWS, GCP, Azure, Kubernetes, Lambda, RunPod and 20+ other cloud providers.
  • Cost optimization: Automatically selects the cheapest available cloud region.
  • Spot instance management: Uses spot instances for cost savings while providing automatic recovery from interruptions.
  • Distributed training: Supports multi-node jobs with gang scheduling.
  • Managed jobs: Provides auto-recovery, checkpointing, and fault tolerance features.
  • Model serving (Sky Serve): Enables model deployment with autoscaling capabilities.

Example prompts

  • "Launch a cluster on the cheapest available cloud region with 1 T4 GPU."
  • "Run my training script (train.py) using spot instances and automatically recover if interrupted."
  • "Deploy my model as a serving endpoint with autoscaling enabled."

Tips & gotchas

  • You'll need to install the SkyPilot package: pip install "skypilot[aws,gcp,azure,kubernetes]".
  • Ensure you have valid cloud credentials configured using sky check before launching any resources.
  • The skill utilizes YAML files (hello.yaml, task YAML) to define resource requirements and execution commands; familiarity with this format is helpful.