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 quantum-fermi-hubbard-vqegit 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/quantum-fermi-hubbard-vqe)<a href="https://agentmods.dev/skills/xi-zhao/openquantum/quantum-fermi-hubbard-vqe"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/quantum-fermi-hubbard-vqe/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/quantum-fermi-hubbard-vqe"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/quantum-fermi-hubbard-vqe.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 | $0.00067 | $0.00837 |
| Opus 5.5 | $0.00027 | $0.00335 |
| Sonnet 5.5 | $0.00013 | $0.00167 |
| Haiku 4.5 | $0.00007 | $0.00084 |
Grade A, and why
quantum-fermi-hubbard-vqe 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 13d 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.
What it actually says
fermi_hubbard_vqe
本地开源适配。上游指南 ID：algorithms/quantum-machine-learning/fermi-hubbard-vqe。
适用方法
开放边界 Hubbard 链，Jordan-Wigner 与全 Fock 空间 VQE；粒子数是测量值，未强制固定粒子数扇区。
使用步骤
- 先识别用户是在询问原理、要求运行，还是要求生成/修改代码；仅解释时不自动开始计算。
- 阅读共同运行说明和本地实现。可通过已有
quantum_practicesTool 的get动作、id=algorithms/quantum-machine-learning/fermi-hubbard-vqe读取完整理论、原始参数和推导；其中的外部安装命令及 UnitaryLab 后端要求不适用于本地执行。 - 根据任务准备实际输入，核对下面的参数签名。省略输入只会运行教学示例，不能把它冒充用户数据的结果。需要示例以外的 ansatz、oracle、边界条件或输出时，基于开源 SDK 生成可审查的任务代码。
- 使用 Harness 已有的
bash（Windows 为pwsh）Tool 执行。在 OpenQuantum 仓库根目录，先检查示例 Python 环境；缺少依赖时显式执行npm run capability:algorithms:setup -- --minimal。执行和安装均受现有 Harness 权限、审批、超时及 Job 管理约束。Skill 不启动服务。 - 读取实际结果和错误；保留输入、依赖版本、种子、近似参数与输出。优化未收敛、后选择概率低、码距未计算或样本不足都必须按实际字段报告。通过经典对照或收敛检查支持数值结论；最终科学验收仍为
not_evaluated。
参数：
fermi_hubbard_vqe(sites=2, hopping=1.0, interaction=4.0, field=1.5, layers=3, maxiter=200, seed=7)
最小可运行示例（macOS/Linux；Windows Python 路径见共同说明）：
examples/quantum-algorithms/.venv/bin/python examples/quantum-algorithms/run.py --algorithm fermi_hubbard_vqe
用户参数写入 JSON 文件，追加 --input <path>；需要保留报告时追加 --output <path>。输入规模由用户选择，不能把示例默认值当成算法上限。
来源与边界
上游 MIT 指南：algorithms/quantum-machine-learning/fermi-hubbard-vqe。原文作为参考保存在固定检索库，本文件将执行路线改为开源 SDK。来源摘要和算法模块对应关系见coverage.json，许可证与改动说明见NOTICE。
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.
- 13d ago First seen · 39 lines · 67 tokens per session scan A d1c9be2edaa1
quantum-fermi-hubbard-vqe is a skill published in the GitHub repository xi-zhao/OpenQuantum (74 stars, last pushed 9d ago), licensed MIT. It adds 67 tokens to every session and 837 once invoked, about $0.0003 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 2026-09-25.
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