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Architect

Organization
robium-ai
architect

Start robotics apps or demos from compatible examples for interactive simulation, pretrained visual control, mapping/navigation, robot assistants, or manipulation. Choose a stack if none fits; not existing-app maintenance.

Overview

Publisherrobium-ai
Repositoryrobium
Skill namearchitect
Stars
104
Forks
2
Bundled files
8
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.

  • 8 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 Architect 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/architect
  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/architect .claude/skills/architect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architect 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 Architect 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 Architect 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.

Architect

Reach a visible robot result with the least new work, then customize. Inspect existing examples before researching or designing a new stack.

Without an established app, an outcome-only request such as trying interactive simulator examples, mapping an environment, or running a pretrained policy is a first-run request even without "new project", a robot brand, or "architect" in the prompt. Use this skill inline, not the optional heavy-research architect subagent. If a domain skill brought you here, select the baseline once and return to that skill for its mechanics; do not loop through architecture again for the same task.

Start from the outcome

  • Inspect the current repository, the Robium app registry, and the closest working example before asking questions. Start with candidate READMEs, manifests, and launchers; exclude generated environments, caches, and model assets from discovery scans.
  • Define what the robot should visibly do, where it will run, and what evidence would make the first slice credible.
  • Honor the user's stated target platform and restrictions even when the agent runs on a different host. Ask about a compatible alternative, not for them to restate constraints they already gave.

Offer the shortest useful starting path

After a quick registry and candidate check, surface these paths briefly, mark the recommendation, and explain what would run first. Do this before broad research, architecture documents, cloning, or lengthy setup. Do not turn the four paths into a mandatory questionnaire: when intent is clear, explain and proceed within it; ask one short question only for a consequential choice.

PathWhen it fitsFirst step
Try an exampleClose compatible match; user wants to see it workRun it unchanged in the existing checkout; no new project.
Adapt an exampleClose match; user wants their own customized appVerify the baseline, then make a separate derivative and change one behavior.
Reuse selected piecesPartial overlap; useful components fit the new appReuse the smallest compatible pieces and prove one integrated behavior.
Start freshNo useful compatible overlap, or explicitly requestedUse the domain skills to scaffold only the smallest visible slice.

For "just try it", default to the compatible example with the fewest new moving parts, not the most ambitious stack. Describe prerequisites and first-run work, not unmeasured speed promises. Read reuse paths only for adaptation, component reuse, or a fresh scaffold.

Inspect candidates and prove the selected slice

  • Resolve the user's workspace with npx robium-ai workspace --json; its repo and apps paths are authoritative, not a hardcoded ~/robium or the installed plugin cache. For workspace discovery and an occasional quiet freshness check before a new example, read workspace updates. Checking is not updating.
  • Discover candidates in the Robium app registry, using a sibling checkout when available. Match the task, host architecture, runtime requirements, and relevant verification evidence, not just the name or a stable label. Read the selected app's README and launcher; distinguish tested conditions from assumptions. For the homepage's first-run tasks, use first-run examples, then read that candidate's exact README and manifest without scanning the whole apps checkout.
  • For a clear compatible match, explain the choice and proceed within the user's request: "This matches Robium's navigation example. I'll get it running first, then we can adapt it." Ask only when different candidates or an incompatible platform materially change the outcome. Honor an explicit request to build from scratch. Routine maintenance stays in the owning skill.
  • Use the existing checkout without resetting or pulling over user changes; acquire a separate checkout when necessary. Record the source revision and any local changes. For whole-app reuse, run the baseline with its launcher, pinned environment, assets, and viewer; do not create a derivative, upgrade dependencies, or recreate infrastructure just to demonstrate it.
  • Check prerequisites, credentials, and occupied ports before lengthy builds. Explain first-run downloads and reuse caches on later starts. Missing model access is a blocker, not permission to substitute a mock and call it live. Keep secrets out of prompts/logs; explain API charges and obtain authorization before paid calls. Do not provision cloud resources or move physical hardware as an implicit part of onboarding.
  • Check the requested behavior at a meaningful milestone, not after each edit. Use the app's smallest relevant check, not a new suite or repeated UI tours. A low-risk visual check may be handed to the user with exact steps and an explicit unverified status; an open port is not proof the demo works. Stop only processes started for this run. If blocked, report the exact unmet prerequisite without inventing a new stack. Before a port, dependency overhaul, or prolonged troubleshooting, surface the blocker and a simpler compatible path; let the user choose the tradeoff. For try-only requests, offer one small customization after it works. For requested customization, proceed with the agreed change and keep the baseline.

Choose only the stack the slice needs

  • When environment setup is needed, use environments early.
  • For ROS-based mobile navigation, route through ros2, navigation, simulation, and the selected simulator and visualizer.
  • For learned manipulation, route through lerobot, data, and huggingface; add a simulator only when the first slice needs one.
  • Use integration when module boundaries or cross-process communication are themselves a design decision.
  • Use testing when risk, uncertainty, or requested test work needs a verification decision; do not automatically author tests. Use test-assets for needed fixtures.
  • Route provider mechanics to cloud-run or runpod only after deployment is part of the approved slice. A public session layer belongs to live-demo; publishing a finished app belongs to app-publishing.

When the stack is genuinely undecided, read stack-selection.md. Keep upstream project documentation as the source of truth for supported versions and hardware.

Keep the decision record light

  • Keep decisions inline for tryouts and tiny demos. Use docs/architecture-brief.md when a new app or re-architecture benefits from a durable record, never as a gate before an agreed first visible result.
  • Record the outcome, chosen direction, provisional assumptions, risks, next probe, and allowed pivots. It is a living decision record, not a contract.
  • Read brief-template.md only when creating or revising that record. The filled example is illustrative, not a source of current compatibility facts.
  • If no close app exists, read scaffold-patterns.md and prune the starting shape to the first slice.

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 Architect AI skill do?

Start robotics apps or demos from compatible examples for interactive simulation, pretrained visual control, mapping/navigation, robot assistants, or manipulation. Choose a stack if none fits; not existing-app maintenance.

Why use Architect on TypingMind?

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

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

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 Architect?

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

Is the Architect 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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