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 flow-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/flow-vqe)<a href="https://agentmods.dev/skills/xi-zhao/openquantum/flow-vqe"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/flow-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/flow-vqe"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/flow-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 | $0.00023 | $0.01041 |
| Opus 5.5 | $0.00009 | $0.00416 |
| Sonnet 5.5 | $0.00005 | $0.00208 |
| Haiku 4.5 | $0.00002 | $0.00104 |
Grade A, and why
flow-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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flow-VQE 参数学习
原生 SDK 路径
本能力同时支持 Flow-VQE source / Torch / Zuko 的原生公开 API。用户需要 自定义 objective、分布、flow、训练数据与优化循环 时，由 Agent 在固定依赖环境中编写和运行任务代码，不限于下文便捷 Tool 的预设模型。
使用 node scripts/sdk-access.mjs inspect flow-vqe 定位环境，按完整 SDK 工作流执行。模块导航：flow_vqe、optimization_baselines、ansatz、torch、zuko；导航不是 API 白名单。
完整 SDK setup 另取固定版本上游源码，论文程序的预置 CLI 不限制用户用公开函数自定义任务。旧 upstream/ 仅供原有便捷 Tool 的稳定实现。
SciPy 固定为 1.17.1，以避开原 1.15.2 PROPACK 扩展在当前 macOS 的加载失败；这是记录在依赖锁中的兼容性调整。
原生程序导入库前，按环境定位器的 bootstrap 执行启动兼容处理：校验完整固定源码后加入模块搜索路径，保留 flow_vqe、ansatz 和 optimization_baselines 的上游模块关系。这不限制公开 API，也不替换数值算法。
下文关于模型、门集、输入与预算的限制描述便捷 Tool 的合同；原生 SDK 另受上游版本、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。
Agent 经 Harness 调用 flow_vqe_local 提供的 train_flow_vqe。本 Skill 负责选择和解释，不启动计算进程。
至少 2 个量子位；Pauli 最左字符是 q0，系数为同一能量单位。主计算以无矩阵 RY/CNOT 实振幅 ansatz 训练 flow 参数，返回重算能量及等评估预算的均匀随机搜索基线。量子位、Pauli 项、层数和训练次数由输入决定。当前处理单个 Hamiltonian，不推导跨分子泛化或全局最优。
独立参考使用 referenceMode=auto|required|skip：auto 按默认阈值选择参考，required 使用调用方资源尝试所请求规模，skip 跳过。未执行时 reference.status=not_run，参考值和差异为 null；尝试后失败会返回错误。
输入示例：
{
"numQubits": 2,
"terms": [
{
"pauli": "ZI",
"coefficient": -1
},
{
"pauli": "IX",
"coefficient": -0.5
}
],
"epochs": 8,
"batchSize": 8
}
调用前确认用户问题落在上述范围内；参数含糊且会改变物理结果时先澄清。接口不接受代码、路径、凭据或真实硬件任务。
计算 worker 默认不设置时间或输出大小上限；部署可配置资源预算，取消会终止本次计算进程组。连接层超时与硬件环境配置见资源配置。
计算前显式准备固定依赖；运行仍可能写 SDK 缓存，因此保留 workspace-write。工具未注册或连接已禁用时，检查设置中心；不要用通用执行工具绕过禁用。
依赖尚未准备时按仓库文档运行 npm run capability:paper-tools:setup；不要自行改版本或扩大输入边界来绕过失败。
所有结果保留完整输入、输入摘要、上游版本和依赖锁摘要。报告实际数值、物理假设、误差与限制；
scientificValidation=not_evaluated，比较数据不构成中央科学验收。失败时说明真实原因，不补造数据。
来源与接入范围见 论文能力说明。
依赖准备
运行前执行 node scripts/setup-paper-tools.mjs flow-vqe。缺失、旧锁和已安装环境的处理见共同准备说明；Tool 不自动安装或升级依赖。
What ships with it
11 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.
- core/statevector.py 2.5 KB runs code
- mcp/bridge.py 4.3 KB runs code
- mcp/contracts.mjs 2.2 KB runs code
- mcp/server.mjs 211 B runs code
- pyproject.toml 508 B
- upstream/flow_training.py 26 KB runs code
- upstream/LICENSE 1.0 KB
- upstream/molecule_configs.py 3.2 KB runs code
- upstream/provenance.json 772 B
- upstream/utils.py 4.8 KB runs code
- uv.lock 121 KB
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.
- 4d ago Changed · +17 lines 181ab3c906f4
- 22d ago Changed · +4 lines 2551d444691c
- 1mo ago Changed · +2 lines · -1 tokens per session 0d8304740b00
- 1mo ago First seen · 41 lines · 24 tokens per session scan A ebce99d2713e
flow-vqe is a skill published in the GitHub repository xi-zhao/OpenQuantum (75 stars, last pushed yesterday), licensed MIT. It adds 23 tokens to every session and 1,041 once invoked, about $0.0001 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 2026-09-13.
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