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Lambda Labs Gpu Cloud

Listed
davila7·vlatest·cloud·MIT

Leverages Lambda Labs’ GPU cloud for accelerated computing tasks, offering scalable and cost-effective processing power.

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 davila7/claude-code-templates --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

davila7

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

    • cli-tool/components/skills/ai-research/infrastructure-lambda-labs/references/advanced-usage.md:399contacts repo.anaconda.com
      wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
    • cli-tool/components/skills/ai-research/infrastructure-lambda-labs/references/troubleshooting.md:148contacts lambdalabs.com
      wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
    • cli-tool/components/skills/ai-research/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)

    • cli-tool/components/skills/ai-research/infrastructure-lambda-labs/SKILL.md:64SSH private keys or config
      ssh -i ~/.ssh/lambda_key ubuntu@<INSTANCE-IP>
    • cli-tool/components/skills/ai-research/infrastructure-lambda-labs/SKILL.md:295SSH private keys or config
      ssh-keygen -t ed25519 -f ~/.ssh/lambda_key
    • cli-tool/components/skills/ai-research/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

    • cli-tool/components/skills/ai-research/infrastructure-lambda-labs/references/troubleshooting.md:148remote script piped into a shell
      wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
    • cli-tool/components/skills/ai-research/infrastructure-lambda-labs/references/troubleshooting.md:196remote script piped into a shell
      wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
    • cli-tool/components/skills/ai-research/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 davila7/claude-code-templates at 79182c51fb.

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

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/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 enables AI agents to interact with Lambda Labs GPU cloud infrastructure, allowing for the provisioning and management of high-performance computing resources. It streamlines access to specialized hardware needed for intensive workloads like machine learning training and inference.

When to use it

  • Deploying large language model training jobs requiring significant VRAM capacity.
  • Scaling up computational resources for deep learning experimentation on demand.
  • Managing temporary GPU instances for short-term data processing tasks.
  • Integrating cloud GPU access directly into automated AI agent workflows.

Key capabilities

  • Direct interaction with Lambda Labs GPU cloud API endpoints.
  • Automated provisioning of GPU instances based on specific hardware requirements.
  • Resource lifecycle management including start, stop, and termination actions.
  • Configuration of networking and storage for attached GPU environments.

Example prompts

  • "Provision a new A100 instance on Lambda Labs with 40GB VRAM for my current model training run."
  • "Check the status of all active GPU instances in my workspace and terminate any idle ones older than two hours."
  • "Configure a fresh GPU environment with specific Docker dependencies ready for immediate inference testing."

Tips & gotchas

Ensure your AI agent has proper API credentials configured before attempting to provision resources, as unauthorized access will fail. Be mindful of Lambda Labs pricing models; always include termination logic in your prompts to avoid unexpected costs from lingering instances.