hardware-info

hardware-info is a skill for Claude Code, Codex from Katagiri-Hoshino-Lab/VibeCodeHPC. It costs 27 tokens per session (249 once invoked), scanned A, original, MIT.

A method for collecting real hardware specifications and estimating the theoretical peak performance of an HPC machine. It covers CPUs, GPUs, memory bandwidth, and the balance between data movement and calculation.

In plain words
What is it for?
Use it at project start to create hardware documentation, calculate CPU/GPU peak performance, measure memory bandwidth, and verify details through a batch job.
Why use it?
It records the machine's actual capabilities so optimization decisions are based on the compute nodes being used. It also helps identify whether a workload is limited by memory or computation.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it at project start to create hardware documentation, calculate CPU/GPU peak performance, measure memory bandwidth, and verify details through a batch job.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/katagiri-hoshino-lab/vibecodehpc/hardware-info
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.

Any agent
npx skills add Katagiri-Hoshino-Lab/VibeCodeHPC --skill hardware-info
Clone the repo
git clone --depth 1 https://github.com/Katagiri-Hoshino-Lab/VibeCodeHPC

Made for: Claude Code, Codex.

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 hardware-info

README.md
[![agentmods](https://agentmods.dev/badge/skills/katagiri-hoshino-lab/vibecodehpc/hardware-info/github.svg)](https://agentmods.dev/skills/katagiri-hoshino-lab/vibecodehpc/hardware-info)
Your own site
<a href="https://agentmods.dev/skills/katagiri-hoshino-lab/vibecodehpc/hardware-info"><img src="https://agentmods.dev/badge/skills/katagiri-hoshino-lab/vibecodehpc/hardware-info/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.

agentmods 80×15 button for hardware-info

Your own site · 80×15
<a href="https://agentmods.dev/skills/katagiri-hoshino-lab/vibecodehpc/hardware-info"><img src="https://agentmods.dev/badge/skills/katagiri-hoshino-lab/vibecodehpc/hardware-info.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 249 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 5.1 $0.00027 $0.00249
Opus 5 $0.00014 $0.00125
Sonnet 5 $0.00005 $0.00050
Haiku 4.5 $0.00003 $0.00025

Measured 11d ago against content hash 7f3a92cdfdfe, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

hardware-info 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 11d 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.

Agent-shared/skills/hardware-info/SKILL.md · 34 lines

What it actually says

Hardware Info

Theoretical Peak Performance

CPU (FP64)

FLOPS = cores × freq(GHz) × 2(FMA) × SIMD_width
SIMD: SSE=2, AVX/AVX2=4, AVX-512=8 (FP64)

GPU (FP64)

FLOPS = SMs × FP64_cores_per_SM × freq × 2(FMA)
Multi-GPU: multiply by GPU count

Memory Bandwidth

BW = channels × bus_width(bit)/8 × freq(MT/s)

Requirements

  1. SE creates hardware_info.md at project start with actual commands on compute nodes
  2. Theoretical peak must be calculated with formula shown
  3. At least one PG verifies via batch job
  4. B/F ratio (Byte/FLOP) must be noted for memory-bound vs compute-bound classification

Details: references/collection_commands.md, references/hardware_info_template.md

Files

What ships with it

2 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. 11d ago First seen · 34 lines · 27 tokens per session scan A 7f3a92cdfdfe

Subscribe to this mod's changes

hardware-info is a skill published in the GitHub repository Katagiri-Hoshino-Lab/VibeCodeHPC (39 stars, last pushed 5mo ago), licensed MIT. It adds 27 tokens to every session and 249 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

pylabrobot

Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API…

magic3007/dotfiles · 73 tokens

qiskit

IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use…

magic3007/dotfiles · 73 tokens

opentrons-integration

Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.

magic3007/dotfiles · 66 tokens

ruview-applications

Run RuView sensing applications — presence/occupancy, breathing & heart rate, activity & fall detection, 17-keypoint pose estimation (WiFlow), sleep monitoring & apnea screening, environment mapping, Mass Casualty Assessment (MAT), and the 3D point-cloud fusion demo. Use when someone wants to actually do something…

ruvnet/RuView · 79 tokens

lab-hardware-cad

Design custom laboratory hardware as parametric build123d models and export fabrication-ready STEP, STL, and DXF files - microfluidic chips and molds, optomechanical mounts and breadboard adapters, cuvette and microplate holders, tube racks, animal-behavior rigs, and 3D-printed instrument fixtures. Use when a research…

K-Dense-AI/scientific-agent-skills · 106 tokens

pylabrobot

Develop and review PyLabRobot lab-automation resources, liquid-handling plans, offline simulations, and supported-device integrations. Use for PyLabRobot protocols or API questions; keep physical execution behind an explicit operator safety gate.

K-Dense-AI/scientific-agent-skills · 49 tokens