atlas: Skill for Claude Code

.claude/skills/oracle_pre_commit_cross_hardware_check/SKILL.md

oracle_pre_commit_cross_hardware_check is a skill for Claude Code from Avarok-Cybersecurity/atlas. It costs 330 tokens per session (1,737 once invoked), scanned A, original, AGPL-3.0.

A pre-commit check for kernel changes that may behave differently across hardware or GPU toolchains. It examines selected kernel, configuration, model, hardware, and symlink changes before a commit is pushed.

In plain words
What is it for?
Use it when changing kernels, hardware-specific dispatch, configuration files, or kernel symlinks for different GPU platforms.
Why use it?
It helps catch cross-hardware compatibility problems before changes leave the repository.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

This is Avarok-Cybersecurity/atlas's own configuration. It tells Claude Code how to work on atlas itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything atlas configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Avarok-Cybersecurity/atlas. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Avarok-Cybersecurity/atlas/main/.claude/skills/oracle_pre_commit_cross_hardware_check/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Avarok-Cybersecurity/atlas

Made for: Claude Code.

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 oracle_pre_commit_cross_hardware_check

README.md
[![agentmods](https://agentmods.dev/badge/skills/avarok-cybersecurity/atlas/oracle_pre_commit_cross_hardware_check/github.svg)](https://agentmods.dev/skills/avarok-cybersecurity/atlas/oracle_pre_commit_cross_hardware_check)
Your own site
<a href="https://agentmods.dev/skills/avarok-cybersecurity/atlas/oracle_pre_commit_cross_hardware_check"><img src="https://agentmods.dev/badge/skills/avarok-cybersecurity/atlas/oracle_pre_commit_cross_hardware_check/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 oracle_pre_commit_cross_hardware_check

Your own site · 80×15
<a href="https://agentmods.dev/skills/avarok-cybersecurity/atlas/oracle_pre_commit_cross_hardware_check"><img src="https://agentmods.dev/badge/skills/avarok-cybersecurity/atlas/oracle_pre_commit_cross_hardware_check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 330 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,737 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 unknown 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 $0.00330 $0.01737
Opus 5.5 $0.00132 $0.00695
Sonnet 5.5 $0.00066 $0.00347
Haiku 4.5 $0.00033 $0.00174

Measured 1mo ago against content hash 29169ba9f7bc, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

oracle_pre_commit_cross_hardware_check 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 1mo 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.

.claude/skills/oracle_pre_commit_cross_hardware_check/SKILL.md · 105 lines

The source is not reproduced here

Licensed AGPL-3.0

The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

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. 1mo ago Changed · +2 tokens per session 29169ba9f7bc
  2. 1mo ago First seen · 105 lines · 328 tokens per session scan A 10d7f72fc88e

Subscribe to this mod's changes

oracle_pre_commit_cross_hardware_check is a skill published in the GitHub repository Avarok-Cybersecurity/atlas (712 stars, last pushed 3d ago), licensed AGPL-3.0. It adds 330 tokens to every session and 1,737 once invoked, about $0.0013 per session on Opus 5.5. 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 2026-09-12.

Related

Other skills, from other repositories

gpu-server-setup

Prepare a Linux server with NVIDIA or Intel Arc GPUs for neural-network and LLM workloads, or diagnose one that misbehaves. Use when the user asks to set up or prepare a GPU server, install NVIDIA drivers or CUDA on Debian/Ubuntu, bring up Intel Arc (A770, A750, B580, Arc Pro B60/B70) with the i915/xe driver and…

EvilFreelancer/gpu-server-setup · 223 tokens

neuron-evaluation

Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…

neuron-core/neuron-ai · 102 tokens

jetson-validate-image

Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.

NVIDIA/skills · 50 tokens

holohub-app-lifecycle

Use for non-failing HoloHub app work with ./holohub: scaffold, build, run, test, visual evidence, lint, and flow benchmarking.

NVIDIA/skills · 40 tokens

SmartHome Video Anomaly Benchmark

VLM evaluation suite for video anomaly detection in smart home camera footage.

SharpAI/DeepCamera · 20 tokens

passport-build

Validate and package FoloToy AI Passport firmware, including a verified merged image and matching debugging artifacts. Use for build, test, and firmware-package requests; does not flash, commit, or publish.

FoloToy/ai-passport · 43 tokens