squlearn-learning

squlearn-learning is a skill for Claude Code, Codex from xi-zhao/OpenQuantum. It costs 41 tokens per session (501 once invoked), scanned A, original, MIT.

A Python toolkit that connects quantum circuits and measurements with scikit-learn, a common machine-learning library. It supports quantum kernels, which compare data using quantum circuits, and scikit-learn estimators.

In plain words
What is it for?
Use it to create encoded quantum circuits, define observables, run them with an executor, build quantum kernels, and train scikit-learn-style estimators.
Why use it?
It provides a way to experiment with quantum machine-learning models while keeping the circuit encoding, data split, regularization, and local execution settings clear.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to create encoded quantum circuits, define observables, run them with an executor, build quantum kernels, and train scikit-learn-style estimators.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/xi-zhao/openquantum/squlearn-learning
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 xi-zhao/OpenQuantum --skill squlearn-learning
Clone the repo
git clone --depth 1 https://github.com/xi-zhao/OpenQuantum

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 squlearn-learning

README.md
[![agentmods](https://agentmods.dev/badge/skills/xi-zhao/openquantum/squlearn-learning/github.svg)](https://agentmods.dev/skills/xi-zhao/openquantum/squlearn-learning)
Your own site
<a href="https://agentmods.dev/skills/xi-zhao/openquantum/squlearn-learning"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/squlearn-learning/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 squlearn-learning

Your own site · 80×15
<a href="https://agentmods.dev/skills/xi-zhao/openquantum/squlearn-learning"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/squlearn-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 501 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 $0.00041 $0.00501
Opus 5.5 $0.00016 $0.00200
Sonnet 5.5 $0.00008 $0.00100
Haiku 4.5 $0.00004 $0.00050

Measured today against content hash d77d36e2ef25, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

squlearn-learning 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 today.

The scan reads SKILL.md. This mod also ships 1 executable file (references/native-example.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/squlearn-learning/SKILL.md · 32 lines

What it actually says

sQUlearn

原生 SDK 路径

本能力使用 sQUlearn 的原生公开 API。用户需要 编码线路、observable、executor、量子核与 sklearn estimator 时,由 Agent 在固定依赖环境中编写和运行任务代码。

使用 node scripts/sdk-access.mjs inspect squlearn-learning 定位环境,按完整 SDK 工作流执行。模块导航:squlearn;导航不是 API 白名单。

明确本地 executor、编码参数、kernel regularization 和数据划分。账户、真实设备与外部服务保持独立授权;不自动选择云后端。

本入口复用宿主文件与代码工具,没有新增通用执行 Tool。原生 SDK 受上游版本、可选依赖、宿主资源与用户授权约束。已禁用或被拒绝的动作不能借代码路径绕过。执行状态和科研验收分别报告。

明确本地 executor、编码参数、kernel regularization 和数据划分。账户、真实设备与外部服务保持独立授权;不自动选择云后端。

按固定版本的公开文档和签名选择方法;输入、算法与资源预算由任务决定。该 Skill 通过原生 SDK 工作台和宿主代码工具执行自定义程序,没有新增通用执行服务或专用计算 Tool。

保留程序、输入、完整结果、依赖锁摘要和实际检查。原生 API 的执行不推导最终科学验收,scientificValidation=not_evaluated。

上游源码与文档入口。

原生程序示例

固定版本的可编辑程序展示公开 API 的使用方式,参数与模型可按研究任务修改。先显式准备环境,再在独立任务目录运行,保留结果文件;示例检查不构成最终科学验收。

Files

What ships with it

3 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. today First seen · 32 lines · 41 tokens per session scan A d77d36e2ef25

Subscribe to this mod's changes

squlearn-learning is a skill published in the GitHub repository xi-zhao/OpenQuantum (75 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 501 once invoked, about $0.0002 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 2026-10-14.

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