architect

architect is a skill for Claude Code, Codex from robium-ai/robium. It costs 167 tokens per session (3,597 once invoked), scanned A, a copy of architect, MIT.

The main planning skill for new robotics applications. It turns a robot idea and its requirements into choices for software, simulation, data, visualisation, training, and the initial build plan.

In plain words
What is it for?
Use it to plan a new robot project, check an uncertain stack choice, create an architecture brief, and direct later work to the relevant robotics skills.
Why use it?
It gives a project a documented technical direction before implementation begins, especially when the software stack is not yet settled.

Skill for Claude CodeCodex

Part of the robium plugin — 72 skills, 1 agent, 6 hooks shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/robium-ai/robium/1.2.0
Any agent
npx skills add robium-ai/robium --skill 1.2.0
Clone the repo
git clone --depth 1 https://github.com/robium-ai/robium

Made for: Claude Code, Codex.

Or install robium, the plugin that ships this one along with the rest of its 72 skills, 1 agent, 6 hooks.

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.

agentmods badge for architect

README.md
[![agentmods](https://agentmods.dev/badge/skills/robium-ai/robium/1.2.0.svg)](https://agentmods.dev/skills/robium-ai/robium/1.2.0)
Your own site
<a href="https://agentmods.dev/skills/robium-ai/robium/1.2.0"><img src="https://agentmods.dev/badge/skills/robium-ai/robium/1.2.0.svg" alt="Measured on agentmods" height="20"></a>
Per session 167 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,597 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 94% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00167 $0.03597
Opus 5 $0.00084 $0.01799
Sonnet 5 $0.00033 $0.00719
Haiku 4.5 $0.00017 $0.00360

Measured 5d ago against content hash 89d32b06a66b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

architect scanned grade A with 0 findings 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 5d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

94% identical to architect — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

archive/architect/1.2.0/SKILL.md · 241 lines

How it starts

The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.

architect

The entry point to robium. Given a robotics application idea, this skill turns requirements into a concrete stack decision — middleware, simulation, data, visualization, and training frameworks — plus a scaffold plan and a written architecture brief. It is the only skill that knows the whole robium catalog; everything else is reached by routing from here. Load it first on any new robotics app, then hand off to the domain skills for the actual build.

When to use this skill

  • Starting a brand-new robotics application from a rough idea ("build a mobile robot that navigates a warehouse", "train a manipulation policy on an arm").
  • The requirements exist but the stack is unchosen, or half-chosen and you want a sanity check before committing.
  • You need a durable, written architecture record the whole project builds from.
  • You want the routing map: "which robium skill do I load for phase X?"
  • Cross-references — go straight to the tool skill, skipping architect, when the stack is already chosen and the question is narrow:
    • Debugging or configuring an existing stack → the matching tool skill (nav2, ros2, gazebo, lerobot, …). "My costmap isn't updating" is a nav2 question, not an architect question.
    • Authoring or improving robium skills themselves → skill-author.
    • Deep-diving one decision (uv vs Docker, which visualizer) → the umbrella that owns it (environments, visualization); architect points you there.

Key directives

  • Delegation posture: route + embed the decision logic. The decisions (which stack, why) live here; the how-to lives in the skill each decision routes to. Never duplicate a tool skill's content — link to it.
  • Always produce or update docs/architecture-brief.md in the app repo. It is the living architecture contract — every later build step reads from it, and refinements edit it in place. No brief, not done. Use references/brief-template.md for its required sections.
  • Virtual-environment-first. Reproducibility is decided before code: route the env question to the environments skill (uv/venv vs Docker) and record the choice in the brief. Do not let a project start with an ad-hoc setup.
  • Never invent syntax or tools. Recommend only real, current tools at versions you have verified — robium ships curation, not a framework. When a version fact matters (ROS 2 distro, Gazebo pairing, GPU floor), confirm it against current docs (e.g. docs.ros.org for ROS 2 distro/EOL status) rather than memory. See references/stack-selection.md for the verified defaults this skill ships with.
  • State open risks explicitly in the brief. Unverified assumptions (GPU availability, hardware you can't see, sim-to-real gaps) go in the brief's open-risks section, not silently into a decision.

Read the full file on GitHub · 241 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 5d ago First seen · 241 lines · 167 tokens per session scan A 89d32b06a66b

Subscribe to this mod's changes

architect is a skill published in the GitHub repository robium-ai/robium (9 stars, last pushed 7d ago), licensed MIT. It adds 167 tokens to every session and 3,597 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to architect, differing in 11 lines, and is treated as a copy.

Related

Other skills, from other repositories

spark-environment-setup

Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.

wshobson/agents · 76 tokens

spark-memory-thermal-ops

Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.

wshobson/agents · 59 tokens

spark-training-gotchas

Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.

wshobson/agents · 63 tokens

terraform-module-library

Build reusable Terraform modules for AWS, Azure, GCP, and OCI infrastructure following infrastructure-as-code best practices. Use when creating infrastructure modules, standardizing cloud provisioning, or implementing reusable IaC components.

wshobson/agents · 44 tokens

telnyx-iot-curl

Manage IoT SIM cards, eSIMs, data plans, and wireless connectivity. Use when building IoT/M2M solutions. This skill provides REST API (curl) examples.

team-telnyx/ai · 45 tokens

telnyx-networking-curl

Configure private networks, WireGuard VPN gateways, internet gateways, and virtual cross connects. This skill provides REST API (curl) examples.

team-telnyx/ai · 35 tokens