Runpod logo

Runpod

Organization
robium-ai
runpod

Provision, diagnose, and clean up paid RunPod compute for robotics workloads.

Overview

Publisherrobium-ai
Repositoryrobium
Skill namerunpod
Stars
104
Forks
2
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by robium-ai on GitHub. Read the source before you install it.

Installation

Install the Runpod AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/robium-ai/robium/tree/main/skills/runpod
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/robium-ai/robium.git /tmp/robium
mkdir -p .claude/skills
cp -r /tmp/robium/skills/runpod .claude/skills/runpod
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Runpod in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Runpod on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Runpod is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

RunPod

Treat the allocated Pod, not the submitted request, as the resource contract.

Before allocating

  • Define the workload acceptance test, minimum compute, immutable image, storage, placement, ports, budget, deadline, and cleanup responsibility.
  • Inspect balance, existing Pods, volumes, live inventory, prices, and volume locality without creating anything.
  • Verify current CLI or API fields against official RunPod documentation; provider interfaces, stock, and prices change.
  • Present one exact candidate and obtain explicit approval for that paid resource and lifetime. Failed or mismatched creation does not authorize a series of retries.
  • Read provisioning.md when selecting an interface, attaching a network volume, or creating a Pod.

Verify what was created

  • Re-read safe fields for the image digest, GPU ID and count, cloud tier, datacenter, volume ID and mount, ports, registry identity, operating mode, price, and termination control.
  • Never print full environment maps or authenticated resource objects; they may contain credentials.
  • Delete a mismatched Pod after preserving safe evidence. Do not diagnose it as though its requested contract were realized.

Robium observed runpodctl 2.8 accepting volume flags while the resulting Pod had no network volume, and REST rejecting a live inventory GPU identifier, on 2026-08-24. GraphQL expressed the required contract in that run. This is a reason to verify provider state, not a universal preference for GraphQL.

For a public service with a shared daily cap, reserve a conservative maximum session cost in an external atomic ledger before Pod creation. Use generation-conditional writes, fail closed on conflicts, and reconcile only controller-owned Pod IDs against final billing. A process-local counter or unconditional object overwrite cannot enforce a multi-instance budget.

Diagnose without guessing

  • Combine control-plane state, system logs, container logs, storage identity, durable progress markers, health endpoints, and artifact timestamps.
  • A missing runtime, silent container, or repeated start event does not alone distinguish a cold pull, restart loop, provider lag, or application failure.
  • Use at most one explicitly approved, time-bounded interactive Pod to isolate application defects. A clean rerun from the rebuilt image digest is the deployment proof; an overlay is not.
  • Read diagnostics-and-lifecycle.md for startup classification, proxy validation, evidence, billing, and cleanup.

Finish the allocation

  • Validate localhost before the provider proxy, then test authentication and scope boundaries through the public route.
  • Download and independently check the evidence before termination.
  • Record cost as provider billing, a price-by-lifetime bound, or an observed balance delta; do not blur those measures.
  • Delete temporary Pods and verify the authoritative Pod list. Preserve a network volume unless its deletion was explicitly authorized; storage cost continues independently.
  • Production enablement is a separate decision from feasibility or cleanup.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Runpod AI skill do?

Provision, diagnose, and clean up paid RunPod compute for robotics workloads.

Why use Runpod on TypingMind?

Because you install it once and use it with any model. Runpod is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Runpod in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/robium-ai/robium/tree/main/skills/runpod. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Runpod?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Runpod?

As many as you like. As long as a model supports skills, you can use Runpod with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Runpod AI skill free?

Yes. It is published on GitHub by robium-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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