Environments

Organization

robium-ai

environments

Set up reproducible robotics environments with uv, Docker, or GPU hosts.

Overview

Publisher	robium-ai
Repository	`robium`
Skill name	`environments`
Stars	104
Forks	2
Bundled files	10
License	MIT
Links	Skill source Repository Homepage

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/robium-ai/robium/tree/main/skills/environments
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.

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/environments .claude/skills/environments

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Environments 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 Environments 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 Environments 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.

Environments

Define the runtime contract before writing around machine-specific accidents. Local and remote runs should differ only where the hardware genuinely differs.

Choose the smallest sufficient boundary

uv: pure-Python robotics, ML, data, and tooling without required system packages. Commit the lockfile and run project commands through uv run.
Docker: ROS 2, native libraries, apt packages, an exact Linux userspace, or deployment as a container.
Docker plus uv: use Docker for the system layer and a project environment for substantial Python dependencies; do not turn the image's system Python into an untracked package set.
Remote GPU host: use when the workload has no viable local hardware path. Record this as a deliberate exception to local/remote symmetry.

Preflight the actual machine before deciding. Confirm architecture, OS, Docker daemon, accelerator, driver, disk, and available Python tooling. Use the Robium doctor if present; fall back to direct probes when it is not.

Keep the contract reproducible

Pin Python and dependency resolution with a lockfile, or pin the container base and build inputs. Avoid latest for reproducible applications.
Keep dependency declarations in one source of truth; do not preserve manual installation steps as hidden prerequisites.
Match container CUDA/runtime requirements to the target host driver rather than the developer laptop.
Design remote work as headless first. Use web visualization instead of making X forwarding part of the normal workflow.
Test the same entry command in the target environment and prove important hardware, file, device, network, and display assumptions.
Before claiming first-run reproducibility, run the complete setup from a clean project copy with the relevant package, model, and asset caches empty. A warm development checkout can hide broken fetch and bootstrap behavior.

Go deeper only when needed

Pure Python and lockfiles: uv patterns.
ROS/system images, build layout, and parity: Docker patterns.
NVIDIA passthrough and headless operation: GPU and remote.
Real-robot LAN, Wi-Fi, DDS/NAT, and Mac host issues: robot networking.
Workloads that can exist only on cloud GPUs: GPU cloud, then the relevant provider skill for provisioning.
macOS, Apple Silicon, arm64, and cold-build evidence: platform notes.
Use the bundled examples only as starting shapes; verify all tags and install steps against current upstream documentation.

Cross into integration when multiple modules, containers, or transports must be wired together. Cross into a deployment skill only after the image and runtime contract work locally or in an equivalent target environment.

Done

A fresh machine can reproduce the environment from committed inputs.
The same documented command starts the workload locally and remotely, except for named hardware flags.
GPU, devices, network, files, and display behavior are verified on the target.
Current details match official uv, Docker, ROS image, and NVIDIA Container Toolkit documentation.

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 Environments AI skill do?

Set up reproducible robotics environments with uv, Docker, or GPU hosts.

Why use Environments on TypingMind?

Because you install it once and use it with any model. Environments 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 Environments in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/robium-ai/robium/tree/main/skills/environments. 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 Environments?

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 Environments?

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

Is the Environments 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Environments

Choose the smallest sufficient boundary

Keep the contract reproducible

Go deeper only when needed

Done

Bundled files

Frequently asked questions

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