Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/beyarkay/claude-skills/runpod)<a href="https://agentmods.dev/skills/beyarkay/claude-skills/runpod"><img src="https://agentmods.dev/badge/skills/beyarkay/claude-skills/runpod/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/beyarkay/claude-skills/runpod"><img src="https://agentmods.dev/badge/skills/beyarkay/claude-skills/runpod.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.01736 |
| Opus 5 | $0.00022 | $0.00868 |
| Sonnet 5 | $0.00009 | $0.00347 |
| Haiku 4.5 | $0.00004 | $0.00174 |
Grade A, and why
runpod scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://api.wandb.ai/graphql" \ How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RunPod Remote Training
Use this skill to manage RunPod GPU infrastructure and run LLM fine-tuning experiments on remote GPUs from the local machine.
Safety Rules (MANDATORY)
- NEVER touch pods not created by us. Other people's pods exist on this account.
- All resources must have
-boyd-in the name - Confirm with user before any deletion (pods, volumes)
- When listing pods, only act on pods with
-boyd-in the name
Setup
# Load API keys
source /Users/brk/projects/mats/.env
# Load helper functions (rp_status, rp_train, rp_ssh_cmd, etc.)
source /Users/brk/projects/mats/runpod-testing/scripts/runpod-ctl.sh
# SSH wrapper (handles key, host checking, timeouts)
PSS=/Users/brk/projects/mats/runpod-testing/scripts/pod-ssh.sh
Common Operations
Check current status
source /Users/brk/projects/mats/.env
source /Users/brk/projects/mats/runpod-testing/scripts/runpod-ctl.sh
rp_status
Create a pod
# Use existing volume: vol-boyd-test (id=2kve3ufpe3, dc=US-TX-3)
runpodctl create pod \
--name 'pod-boyd-TASKNAME' \
--gpuType 'NVIDIA GeForce RTX 4090' \
--gpuCount 1 \
--imageName 'beyarkay/mats-training:latest' \
--containerDiskSize 20 \
--volumePath /workspace \
--networkVolumeId '2kve3ufpe3' \
--secureCloud --startSSH --ports '22/tcp'
After creation, get SSH info:
POD_ID="<from create output>"
rp_get_ssh_info "$POD_ID"
# Returns: IP PORT
Then write the SSH info to scripts/pod-connection.env:
echo "POD_HOST=<IP>" > /Users/brk/projects/mats/runpod-testing/scripts/pod-connection.env
echo "POD_PORT=<PORT>" >> /Users/brk/projects/mats/runpod-testing/scripts/pod-connection.env
SSH into pod
PSS=/Users/brk/projects/mats/runpod-testing/scripts/pod-ssh.sh
$PSS "hostname && nvidia-smi"
Copy files to pod
$PSS --scp /Users/brk/projects/mats/elicitation /workspace/mats-boyd/elicitation
$PSS --scp /Users/brk/projects/mats/.env /workspace/.env-boyd
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 178 lines · 43 tokens per session scan A 2070694fb6d6
runpod is a skill published in the GitHub repository beyarkay/claude-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 1,736 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
google-cloud-solution-guided-gke-ai-migration
Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to…
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
modal
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.
gke-inference
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).
agent-platform-endpoint-management
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model…