Borrowing it
Nothing to install: this file belongs to ZimoLiao/scholaraio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ZimoLiao/scholaraio/main/.claude/skills/quantum-espresso/SKILL.mdgit clone --depth 1 https://github.com/ZimoLiao/scholaraioWrote 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/zimoliao/scholaraio/quantum-espresso)<a href="https://agentmods.dev/skills/zimoliao/scholaraio/quantum-espresso"><img src="https://agentmods.dev/badge/skills/zimoliao/scholaraio/quantum-espresso/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/zimoliao/scholaraio/quantum-espresso"><img src="https://agentmods.dev/badge/skills/zimoliao/scholaraio/quantum-espresso.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00047 | $0.02345 |
| Opus 5 | $0.00023 | $0.01172 |
| Sonnet 5 | $0.00009 | $0.00469 |
| Haiku 4.5 | $0.00005 | $0.00234 |
Grade A, and why
quantum-espresso 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 10d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quantum ESPRESSO 第一性原理计算
用 Quantum ESPRESSO 做 DFT / DFPT：基态、能带、DOS、声子、电声耦合、费米面和电荷密度分析。
本 skill 故意保持轻量：
- 它负责告诉 agent 什么时候该用 QE、标准计算链路是什么、哪些物理与数值规范不能忽略
- 它不承担输入文件字段和程序参数手册的职责
- 具体输入变量、namelist 字段、程序差异统一去查
scholaraio toolref
Agent 默认协议（toolref-first）
对 QE 问题，agent 默认按这个顺序工作：
- 先判断任务属于哪个程序：
pw.x、ph.x、matdyn.x、q2r.x、dos.x、projwfc.x - 写输入文件或解释参数前，先用
scholaraio toolref show qe ...查关键变量 - 不确定变量归属时，先
search再show - 如果
toolref已能回答，就不要把 skill 当第二份参数手册 - 如果
toolref缺页或命中不好，先继续完成用户任务，再回报这是toolref覆盖缺口，而不是把补文档工作甩给用户
这意味着：
- 普通用户不需要自己打磨 QE
toolref - agent 应优先自己查、自己判断、自己退化处理
- 只有在同一缺口反复出现时，才值得进入正式 onboarding/维护流程
前置条件
# 安装
conda install -c conda-forge qe
# 或编译 GPU 版（推荐 A100）
# 赝势下载
# PseudoDojo NC (DFPT 推荐): http://www.pseudo-dojo.org/
# SSSP Efficiency: https://www.materialscloud.org/discover/sssp
验证：pw.x --version 应显示版本。GPU 版本确认 CUDA 支持。
何时使用
适合：
- 晶体材料的电子结构、能带、DOS、声子、超导、电荷密度
- 需要第一性原理精度、并能接受较高计算成本的任务
不适合：
- 需要大尺度长时间分子动力学时，优先经典 MD
- 只是想快速试错而没有结构、赝势、收敛策略时，不要直接上正式算例
Toolref 优先
当 agent 不确定 QE 输入变量、程序名、namelist 所属、默认值或适用条件时，先查 toolref。
常用查法：
scholaraio toolref search qe "wavefunction cutoff"
scholaraio toolref show qe pw ecutwfc
scholaraio toolref show qe pw occupations
scholaraio toolref show qe ph tr2_ph
scholaraio toolref show qe matdyn asr
推荐习惯：
- 写
.in文件前，先查关键变量 - 不靠旧博客记忆
SYSTEM/ELECTRONS/INPUTPH字段 - 程序切换时先确认变量属于
pw.x、ph.x、matdyn.x还是别的模块
如果遇到覆盖缺口：
- 先继续用官方手册或源码文档完成任务
- 在回答里明确说明“这里使用了
toolref之外的官方文档” - 不要要求用户自己先去补齐文档层
核心工作流
知识库协作模式
- 用
scholaraio usearch "<材料名称> DFT"检索相关论文 - 从论文提取：晶体结构、交换关联泛函、k 网格、截断能、实验基准值
- 在输入文件注释中标注参数来源
- 计算完成后与实验数据（晶格常数、能带间隙、声子频率）对比
计算流程
SCF (pw.x) → 基态电荷密度
├─ NSCF (pw.x) → 能带/DOS 的本征值
│ ├─ bands.x → 能带结构后处理
│ ├─ dos.x → 态密度
│ ├─ projwfc.x → 投影态密度（轨道分辨）
│ └─ fs.x → 费米面 (.bxsf)
├─ Phonons (ph.x) → DFPT 声子计算
│ ├─ q2r.x → 实空间力常数
│ ├─ matdyn.x → 声子色散插值
│ └─ e-ph → 电声耦合 (α²F, λ, Tc)
└─ pp.x → 电荷密度/ELF 后处理
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.
- 10d ago First seen · 198 lines · 47 tokens per session scan A b0e6b22d6473
quantum-espresso is a skill published in the GitHub repository ZimoLiao/scholaraio (570 stars, last pushed 11d ago), licensed MIT. It adds 47 tokens to every session and 2,345 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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