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Agent skill

Coreweave Hello World

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Deploy a GPU workload on CoreWeave with kubectl. An agent skill from jeremylongshore/tons-of-skills-marketplace.

MITAuto-check passedAI & LLM Engineering

Install Coreweave Hello World

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill coreweave-hello-world -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace coreweave-hello-world --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/coreweave-hello-world .claude/skills/coreweave-hello-world && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
coreweave-hello-world
GitHub stars
2.8k
Token cost
~1.4k tokens
SKILL.md length
233 words
Files
1
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Deploy a GPU workload on CoreWeave with kubectl. An agent skill from jeremylongshore/tons-of-skills-marketplace.

  • Works in 2 steps: Deploy a vLLM Inference Server → Batch GPU Job
  • Running your first GPU job
  • SKILL.md covers Overview, Prerequisites, Instructions and Error Handling, plus 4 more sections
  • Calls kubectl and curl; needs HUGGING_FACE_HUB_TOKEN and HF_TOKEN

What it does

Coreweave Hello World is an agent skill from jeremylongshore/tons-of-skills-marketplace. Deploy a GPU workload on CoreWeave with kubectl. Use when running your first GPU job, testing inference, or verifying CoreWeave cluster access. Trigger with phrases like "coreweave hello world", "coreweave first deploy", "coreweave gpu test", "run on coreweave".

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Designed for Claude Code

It sits in AI & LLM Engineering, covering Container orchestration. It works with Kubernetes and vLLM. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Running your first GPU job
  • Testing inference
  • Verifying CoreWeave cluster access
  • With phrases like coreweave hello world

Example prompts

  • “coreweave hello world”
  • “coreweave first deploy”
  • “coreweave gpu test”
  • “/coreweave-hello-world”

Requirements

  • A credential in HUGGING_FACE_HUB_TOKEN
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(kubectl:*)

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Deploy a vLLM Inference Server
  2. Batch GPU Job

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(kubectl:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • kubectl
    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.coreweave.com
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • HUGGING_FACE_HUB_TOKEN
    • HF_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Coreweave Hello World loads about 1.4k tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 233 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 233 words, ~1,417 tokens.

Download SKILL.mdSave it as .claude/skills/coreweave-hello-world/SKILL.md (or your agent's skills folder).
name
coreweave-hello-world
description
Deploy a GPU workload on CoreWeave with kubectl. Use when running your first GPU job, testing inference, or verifying CoreWeave cluster access. Trigger with phrases like "coreweave hello world", "coreweave first deploy", "coreweave gpu test", "run on coreweave".
allowed-tools
Read, Write, Edit, Bash(kubectl:*)
compatibility
Designed for Claude Code
version
1.11.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, gpu-cloud, kubernetes, inference, coreweave

CoreWeave Hello World

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.

Overview

Deploy your first GPU workload on CoreWeave: a simple inference service using vLLM or a batch CUDA job. CoreWeave runs Kubernetes on bare-metal GPU nodes with A100, H100, and L40 GPUs.

Prerequisites

  • Completed coreweave-install-auth setup
  • kubectl configured with CoreWeave kubeconfig
  • Namespace with GPU quota

Instructions

Step 1: Deploy a vLLM Inference Server
yaml
# vllm-inference.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-server
spec:
  replicas: 1
  selector:
    matchLabels:
      app: vllm-server
  template:
    metadata:
      labels:
        app: vllm-server
    spec:
      containers:
        - name: vllm
          image: vllm/vllm-openai:latest
          args:
            - "--model"
            - "meta-llama/Llama-3.1-8B-Instruct"
            - "--port"
            - "8000"
          ports:
            - containerPort: 8000
          resources:
            limits:
              nvidia.com/gpu: 1
              memory: 48Gi
              cpu: "8"
            requests:
              nvidia.com/gpu: 1
              memory: 32Gi
              cpu: "4"
          env:
            - name: HUGGING_FACE_HUB_TOKEN
              valueFrom:
                secretKeyRef:
                  name: hf-token
                  key: token
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                  - key: gpu.nvidia.com/class
                    operator: In
                    values: ["A100_PCIE_80GB"]
---
apiVersion: v1
kind: Service
metadata:
  name: vllm-server
spec:
  selector:
    app: vllm-server
  ports:
    - port: 8000
      targetPort: 8000
  type: ClusterIP
bash
# Create HuggingFace token secret
kubectl create secret generic hf-token --from-literal=token="${HF_TOKEN}"

# Deploy
kubectl apply -f vllm-inference.yaml
kubectl get pods -w  # Wait for Running state

# Port-forward and test
kubectl port-forward svc/vllm-server 8000:8000 &
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "meta-llama/Llama-3.1-8B-Instruct", "messages": [{"role": "user", "content": "Hello!"}]}'
Step 2: Batch GPU Job
yaml
# gpu-batch-job.yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: gpu-benchmark
spec:
  template:
    spec:
      restartPolicy: Never
      containers:
        - name: benchmark
          image: pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime
          command: ["python3", "-c"]
          args:
            - |
              import torch
              print(f"CUDA available: {torch.cuda.is_available()}")
              print(f"GPU: {torch.cuda.get_device_name(0)}")
              x = torch.randn(10000, 10000, device="cuda")
              y = torch.matmul(x, x)
              print(f"Matrix multiply result shape: {y.shape}")
              print("CoreWeave GPU test passed!")
          resources:
            limits:
              nvidia.com/gpu: 1
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                  - key: gpu.nvidia.com/class
                    operator: In
                    values: ["A100_PCIE_80GB"]
bash
kubectl apply -f gpu-batch-job.yaml
kubectl logs job/gpu-benchmark --follow

Error Handling

ErrorCauseSolution
Pod stuck PendingNo GPU capacityTry different GPU type or check quota
nvidia-smi not foundWrong base imageUse NVIDIA CUDA images
OOMKilledInsufficient GPU memoryUse larger GPU (80GB A100)
Image pull errorRegistry authCreate imagePullSecret

Output

  • A minimal, namespace-scoped GPU workload or inference endpoint with one declared GPU.
  • A visible readiness signal and a bounded smoke-test response that confirms the GPU runtime path without placing a model token or customer prompt in source control.

Examples

Validate the batch path first because it is cheaper and easier to roll back than a public endpoint:

bash
kubectl -n sandbox apply -f gpu-batch-job.yaml
kubectl -n sandbox wait --for=condition=complete job/gpu-benchmark --timeout=15m
kubectl -n sandbox logs job/gpu-benchmark

Delete the smoke job after recording its redacted result. If it remains Pending, inspect its events and namespace quota—do not broaden cluster permissions or embed registry credentials in the manifest to force it through.

Resources

Next Steps

Proceed to coreweave-local-dev-loop for development workflow setup.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/.curated/coreweave-hello-world of jeremylongshore/tons-of-skills-marketplace.

Open the folder on GitHubat commit cfae287

Compare with similar skills

Coreweave Hello World next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Coreweave Hello World compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Coreweave Hello World this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.4kAutomated safety check: PassMIT
Lego Rl ConfigLegoX/Lego-RL113—~2.1kAutomated safety check: NotesApache-2.0
Vllm Deploy K8svllm-project/vllm-skills102—~2kAutomated safety check: PassApache-2.0
LLM Inference Scalingsickn33/agentic-awesome-skills47k1 repos~2.1kAutomated safety check: PassMIT
LLM Inference ScalingBagelHole/DevOps-Security-Agent-Skills1.2k—~2kAutomated safety check: PassMIT
Dstack Prototypingdstackai/dstack2.3k—~1.6kAutomated safety check: PassMPL-2.0

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Works with

Categories

Questions about Coreweave Hello World

What does Coreweave Hello World do?

Deploy a GPU workload on CoreWeave with kubectl. An agent skill from jeremylongshore/tons-of-skills-marketplace. Coreweave Hello World is an agent skill from jeremylongshore/tons-of-skills-marketplace. Deploy a GPU workload on CoreWeave with kubectl.

When should I use Coreweave Hello World?

Coreweave Hello World fits situations like: running your first GPU job; testing inference; verifying CoreWeave cluster access; with phrases like coreweave hello world.

How do I install Coreweave Hello World in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill coreweave-hello-world -a claude-code`. Or copy the skill folder (skills/.curated/coreweave-hello-world in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/coreweave-hello-world in your project. Claude Code loads it when a task matches its description.

How do I install Coreweave Hello World in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill coreweave-hello-world -a codex`. Or copy the skill folder (skills/.curated/coreweave-hello-world in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/coreweave-hello-world in your project. Codex loads it when a task matches its description.

Can I use Coreweave Hello World in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill coreweave-hello-world -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/coreweave-hello-world, .gemini/skills/coreweave-hello-world, .github/skills/coreweave-hello-world and .opencode/skills/coreweave-hello-world in your project.

What does Coreweave Hello World need to run?

Going by SKILL.md and its folder, Coreweave Hello World needs the command-line tools its instructions call (kubectl and curl) and credentials named HUGGING_FACE_HUB_TOKEN and HF_TOKEN. Our summary lists: A credential in HUGGING_FACE_HUB_TOKEN. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(kubectl:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Coreweave Hello World access the network?

SKILL.md names 2 domains. As links in the text: docs.coreweave.com and github.com. This is read from the text; nothing was executed.

Is Coreweave Hello World safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Coreweave Hello World use?

Coreweave Hello World is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Coreweave Hello World use?

About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Coreweave Hello World?

Skills that share tags, products or a category with Coreweave Hello World: Lego Rl Config (LegoX/Lego-RL, 113 stars), Vllm Deploy K8s (vllm-project/vllm-skills, 102 stars), LLM Inference Scaling (sickn33/agentic-awesome-skills, 47k stars) and LLM Inference Scaling (BagelHole/DevOps-Security-Agent-Skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Coreweave Hello World?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.