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.
npx skills add xi-zhao/OpenQuantum --skill qiskit-machine-learninggit clone --depth 1 https://github.com/xi-zhao/OpenQuantumWrote 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.
[](https://agentmods.dev/skills/xi-zhao/openquantum/qiskit-machine-learning)<a href="https://agentmods.dev/skills/xi-zhao/openquantum/qiskit-machine-learning"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/qiskit-machine-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.
<a href="https://agentmods.dev/skills/xi-zhao/openquantum/qiskit-machine-learning"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/qiskit-machine-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.00528 |
| Opus 5.5 | $0.00020 | $0.00211 |
| Sonnet 5.5 | $0.00010 | $0.00106 |
| Haiku 4.5 | $0.00005 | $0.00053 |
Grade A, and why
qiskit-machine-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.
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.
What it actually says
Qiskit Machine Learning
原生 SDK 路径
本能力使用 Qiskit Machine Learning 的原生公开 API。用户需要 量子核、QSVC/QSVR、QNN、梯度、训练与推断 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 qiskit-machine-learning 定位环境，按完整 SDK 工作流执行。模块导航：qiskit_machine_learning；导航不是 API 白名单。
使用该版本的 primitives API。固定数据划分和 seed，保留训练与测试指标、shots 与优化器；训练成功不等于泛化有效。
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。
使用该版本的 primitives API。固定数据划分和 seed，保留训练与测试指标、shots 与优化器；训练成功不等于泛化有效。
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。
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。
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.
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.
- today First seen · 32 lines · 51 tokens per session scan A 475809c9c26b
qiskit-machine-learning is a skill published in the GitHub repository xi-zhao/OpenQuantum (75 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 528 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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