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Lambda Labs Gpu Cloud

Listed
orchestra-research·vlatest·cloud·MIT

Leverages Lambda Labs' GPU cloud for accelerated AI model training and inference, boosting performance and accessibility.

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 lambda-labs-gpu-cloud -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 3 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 undeclared network calls: failed

    Contacts 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: lambdalabs.com, repo.anaconda.com.

    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.

    Hosts contacted

    repo.anaconda.comundeclaredlambdalabs.comundeclared

    What it found

    • 09-infrastructure/lambda-labs/references/advanced-usage.md:399contacts repo.anaconda.com
      wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
    • 09-infrastructure/lambda-labs/references/troubleshooting.md:148contacts lambdalabs.com
      wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
    • 09-infrastructure/lambda-labs/references/troubleshooting.md:196contacts lambdalabs.com
      wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -

  • No credential access: failed

    Touches credential material in 11 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 (3 of 11)

    • 09-infrastructure/lambda-labs/SKILL.md:64SSH private keys or config
      ssh -i ~/.ssh/lambda_key ubuntu@<INSTANCE-IP>
    • 09-infrastructure/lambda-labs/SKILL.md:295SSH private keys or config
      ssh-keygen -t ed25519 -f ~/.ssh/lambda_key
    • 09-infrastructure/lambda-labs/SKILL.md:305SSH private keys or config
      echo 'ssh-rsa AAAA...' >> ~/.ssh/authorized_keys

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 3 places.

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

    What it found

    • 09-infrastructure/lambda-labs/references/troubleshooting.md:148remote script piped into a shell
      wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
    • 09-infrastructure/lambda-labs/references/troubleshooting.md:196remote script piped into a shell
      wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
    • 09-infrastructure/lambda-labs/references/troubleshooting.md:200pip 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 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/lambda-labs/SKILL.md
Scanned
3 files, 1,525 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 provides access to Lambda Labs' GPU cloud infrastructure. It allows AI agents to provision and manage virtual machines equipped with powerful GPUs for computationally intensive tasks like model training, inference, and research. The agent can handle resource allocation, monitoring, and potentially scaling of these GPU instances.

When to use it

  • Training large language models: Provision a cluster of GPUs to accelerate the training process.
  • Running complex simulations: Utilize high-performance computing resources for scientific or engineering simulations.
  • Batch inference at scale: Deploy trained models on powerful GPUs to handle a large volume of inference requests efficiently.
  • Research and experimentation: Quickly spin up GPU instances to test new algorithms and architectures.

Key capabilities

  • GPU provisioning
  • Virtual machine management
  • Resource allocation
  • Monitoring (implied)
  • Scaling (potentially)

Example prompts

  • "Provision a Lambda Labs VM with 4 NVIDIA A100 GPUs."
  • "Start a training job on the GPU cluster named 'model-training'."
  • "Monitor the utilization of my GPU instances in the 'research' project."

Tips & gotchas

The skill requires familiarity with Lambda Labs’ platform and potentially an account with them. Be mindful of costs associated with using cloud resources, as GPU time can be expensive.