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Agent skill

Lammps

by ZimoLiao in ZimoLiao/scholaraio

A skill your agent uses when working on classical materials simulations with LAMMPS, including potential selection, shock or deformation setups, thermodynamic runs, or structure analysis for solids…

MITAuto-check passed

Install Lammps

skills CLI
$ npx skills add ZimoLiao/scholaraio --skill lammps -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ZimoLiao/scholaraio lammps --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ZimoLiao/scholaraio.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/lammps .claude/skills/lammps && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
lammps
GitHub stars
577
Token cost
~1.3k tokens
SKILL.md length
321 words
Files
1
Skills in repo
43
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when working on classical materials simulations with LAMMPS, including potential selection, shock or deformation setups, thermodynamic runs, or structure analysis for solids…

  • Works in 5 steps: 先判断问题属于哪类对象：pair_style、fix、compute、dump、r… → 写输入脚本前，优先查高风险命令页，而不是凭记忆拼装 → 命令名和用户说法不一致时，优先用 search 找主入口，再用 show → …
  • Working on classical materials simulations with LAMMPS
  • SKILL.md covers Agent 默认协议（toolref-first）, 前置条件, 何时使用 and Toolref 优先, plus 5 more sections
  • Calls conda and pip

What it does

Lammps is an agent skill from ZimoLiao/scholaraio. Use when working on classical materials simulations with LAMMPS, including potential selection, shock or deformation setups, thermodynamic runs, or structure analysis for solids and nanomaterials.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Scholar All-In-One: A research infrastructure for AI agents. The licence is MIT.

When your agent uses it

  • Working on classical materials simulations with LAMMPS
  • Including potential selection
  • Deformation setups
  • Thermodynamic runs

Example prompts

  • “/lammps”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. 先判断问题属于哪类对象：pair_style、fix、compute、dump、region、boundary、run 流程
  2. 写输入脚本前，优先查高风险命令页，而不是凭记忆拼装
  3. 命令名和用户说法不一致时，优先用 search 找主入口，再用 show
  4. 如果 toolref 已能回答，就不要在 skill 里重复写手册
  5. 如果 toolref 命中不好或某个 package 页面缺失，agent 应先完成任务，再把它标记为维护层缺口，而不是让用户自己补

What it can do on your machine

Read from SKILL.md and the folder at commit 777628b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • conda
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • ctcms.nist.gov

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Lammps loads about 1.3k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 321 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ZimoLiao/scholaraio at commit 777628b, republished under its MIT licence (© ZimoLiao). 321 words, ~1,274 tokens.

Download SKILL.mdSave it as .claude/skills/lammps/SKILL.md (or your agent's skills folder).
name
lammps
description
Use when working on classical materials simulations with LAMMPS, including potential selection, shock or deformation setups, thermodynamic runs, or structure analysis for solids and nanomaterials.

LAMMPS 材料科学模拟

用 LAMMPS 做材料科学分子动力学模拟：晶体构建、势函数选择、形变/冲击、结构分析、可视化。

本 skill 故意保持轻量：

  • 它负责告诉 agent 什么时候该用 LAMMPS、该遵守什么科学规范、完整工作流长什么样
  • 它不承担完整接口手册的职责
  • 具体命令、参数、语法、package 限制，统一去查 scholaraio toolref

Agent 默认协议（toolref-first）

对 LAMMPS 问题，agent 默认按这个顺序工作：

  1. 先判断问题属于哪类对象：pair_style、fix、compute、dump、region、boundary、run 流程
  2. 写输入脚本前，优先查高风险命令页，而不是凭记忆拼装
  3. 命令名和用户说法不一致时，优先用 search 找主入口，再用 show
  4. 如果 toolref 已能回答，就不要在 skill 里重复写手册
  5. 如果 toolref 命中不好或某个 package 页面缺失，agent 应先完成任务，再把它标记为维护层缺口，而不是让用户自己补

这意味着：

  • 用户不该自己去打磨 fix / pair_style 的映射关系
  • agent 应自己消化 fix npt -> fix_nh、pair style eam -> pair_eam 这类入口差异
  • 只有反复出现的缺口才进入正式 onboarding

前置条件

bash
# 安装（含 GPU 支持）
conda install -c conda-forge lammps
# 可视化
pip install ovito

验证：lmp -h 应显示已安装的 packages（需包含 GPU、MANYBODY、EXTRA-COMPUTE）。

GPU 加速：package gpu 4 在输入脚本开头启用，suffix gpu 自动为支持的 pair_style 加 /gpu 后缀。

并行运行约束：

  • 启动 MPI 作业时，优先使用 LAMMPS 所在环境自带的 mpirun/mpiexec
  • 不要默认混用系统里的另一套 MPI launcher；如果 lmp 链接的 libmpi 与 launcher 不一致，可能出现“进程活着但无日志输出”的假启动挂起
  • 需要绑核或做 rank pinning 时，先用小规模 MPI 诊断确认启动和绑定语法，再放大到正式规模

何时使用

适合：

  • 金属、陶瓷、半导体、纳米材料的经典 MD
  • 力学性质、相变、位错/缺陷演化、冲击波、热输运
  • 已有合适经验势函数的体系

不适合：

  • 需要显式电子结构精度时，优先考虑 DFT / Quantum ESPRESSO
  • 势函数没有可靠文献依据时，不要直接开算

Toolref 优先

当 agent 不确定命令、参数、限制、输出字段时，先查 toolref，再写输入脚本。

常用查法：

bash
scholaraio toolref search lammps "nose hoover thermostat"
scholaraio toolref show lammps fix_nh
scholaraio toolref show lammps pair_eam
scholaraio toolref show lammps compute_cna_atom
scholaraio toolref show lammps fix_deform

推荐习惯：

  • 写脚本前先查 pair_style / fix / compute / dump
  • 遇到 package 依赖时先用 toolref show 看 Restrictions
  • 遇到模糊概念先用 toolref search，确定候选命令后再 show

如果遇到覆盖缺口：

  • 先用官方 LAMMPS 文档继续完成任务
  • 明确说明这里是 toolref 覆盖/排序缺口，不是用户操作错误
  • 不要让用户为了当前任务先停下来维护文档层
  • 如果官方手册没有解释清楚实际运行异常、MPI 启动问题、版本兼容或绑定语法，继续上网检索官方文档、社区讨论和已知问题，不要猜

核心流程

知识库协作模式
  1. 用 scholaraio usearch "<材料/现象>" 检索相关论文
  2. 从论文提取：势函数选择、晶格常数、实验基准值（相变压力、弹性常数等）
  3. 在输入脚本注释中标注参数来源
  4. 计算完成后与文献数据定量对比

建议按这个顺序思考：

  1. 体系是否适合经典势函数
  2. 选择哪类势函数
  3. 选择边界条件、加载方式、温压控方式
  4. 选择结构分析与输出
  5. 跑小体系/短步数 smoke test
  6. 正式运行后和文献/实验做定量对比

参数出处规则：

  • 严格复现任务里，先查库内已入库论文和补充材料
  • 如果关键参数仍然缺失，需要别的论文或 supplementary 才能定死，应直接向用户明确索取，不要猜
  • 只有当文献明确允许范围选择时，才可以做工程性取值，并要说明这是“派生设置”而不是“严格复现参数”
势函数选择（最关键决策）
势函数类型适用场景LAMMPS pair_style
EAM/FS金属（Fe, Cu, Al, Ni...）eam/fs, eam/alloy
Tersoff共价半导体（Si, C, SiC）tersoff
ReaxFF反应性体系（燃烧、氧化）reaxff
AIREBO碳纳米材料（CNT, 石墨烯）airebo
SWSi, GaNsw
MEAM多元合金meam

势函数文件来源：

科学规范：势函数选择必须有文献依据，不能随便选一个"能跑"的。

典型任务
  • 冲击波 / 爆轰 / 高应变率：重点看非周期边界、活塞施加方式、空间剖面输出
  • 拉伸压缩：重点看 fix deform、应力应变提取、应变率合理性
  • 温度驱动相变：重点看升温速率、平衡充分性、结构识别
  • 缺陷与位错：重点看结构分析和可视化，不只看总能量

常用结构分析：

  • cna/atom：区分 BCC/FCC/HCP
  • ptm/atom：更稳健的局域结构识别
  • centro/atom：缺陷检测
  • voronoi/atom：局域环境统计

这些命令的准确接口、参数和限制请直接查 toolref。

可视化（OVITO）

OVITO 是 LAMMPS 的标准可视化工具。

python
from ovito.io import import_file
from ovito.modifiers import CommonNeighborAnalysisModifier, SliceModifier
from ovito.vis import Viewport, TachyonRenderer

pipeline = import_file("dump.shock.*", sort_particles=True)
pipeline.modifiers.append(CommonNeighborAnalysisModifier())

# 按结构类型着色
def color_by_phase(frame, data):
    import numpy as np
    colors = np.zeros((data.particles.count, 3))
    cna = data.particles["Structure Type"]
    colors[cna == 3] = [0.3, 0.5, 0.8]   # BCC → 蓝
    colors[cna == 2] = [0.85, 0.15, 0.15] # HCP → 红
    colors[cna == 1] = [0.2, 0.8, 0.2]    # FCC → 绿
    colors[cna == 0] = [0.7, 0.7, 0.7]    # Other → 灰
    data.particles_.create_property("Color", data=colors)

pipeline.modifiers.append(color_by_phase)

vp = Viewport(type=Viewport.Type.ORTHO, camera_dir=(0, -1, 0))
vp.zoom_all(size=(1920, 1080))
renderer = TachyonRenderer(shadows=False, ambient_occlusion=True)
vp.render_image(filename="snapshot.png", size=(1920, 1080), renderer=renderer)

推荐输出：

  • 结构类型着色快照或动画
  • 应力/温度/速度的空间剖面图
  • 与文献基准的对比图，而不是只给一张原子图

性能参考

体系大小势函数GPU 配置预期性能
~500k 原子EAM4×A100~50 ns/day
~2M 原子EAM4×A100~15-20 ns/day
~100k 原子ReaxFF4×A100~1-2 ns/day

科学规范

检查项正确做法常见错误
势函数有文献验证的 EAM/Tersoff随便选一个 LJ
体系大小足够消除有限尺寸效应太小导致伪周期
平衡先 NPT 平衡再施加载荷直接拉伸未平衡体系
时间步长metal 单位下 0.001 ps (1 fs)步长太大导致能量不守恒
边界条件冲击方向用 s（非周期）全周期导致冲击波自干涉
截断半径根据势函数要求设置用默认值不检查

Agent 行为准则

  • 不要凭记忆瞎写 fix / compute / pair_style 细节，先查 toolref
  • 不要因为“能跑”就默认模型合理，必须说明势函数和参数依据
  • 不要只汇报温度/能量曲线，要给结构、相分数、应力或波前等材料学指标
  • 不要把 LAMMPS 当黑箱；结果解释必须回到材料机制

© ZimoLiao, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/lammps of ZimoLiao/scholaraio.

Open the folder on GitHubat commit 777628b

Compare with similar skills

Lammps next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Lammps

What does Lammps do?

A skill your agent uses when working on classical materials simulations with LAMMPS, including potential selection, shock or deformation setups, thermodynamic runs, or structure analysis for solids…. Lammps is an agent skill from ZimoLiao/scholaraio. Use when working on classical materials simulations with LAMMPS, including potential selection, shock or deformation setups, thermodynamic runs, or structure analysis for solids and nanomaterials.

When should I use Lammps?

Lammps fits situations like: working on classical materials simulations with LAMMPS; including potential selection; deformation setups; thermodynamic runs.

How do I install Lammps in Claude Code?

Run `npx skills add ZimoLiao/scholaraio --skill lammps -a claude-code`. Or copy the skill folder (.claude/skills/lammps in ZimoLiao/scholaraio) into .claude/skills/lammps in your project. Claude Code loads it when a task matches its description.

How do I install Lammps in Codex?

Run `npx skills add ZimoLiao/scholaraio --skill lammps -a codex`. Or copy the skill folder (.claude/skills/lammps in ZimoLiao/scholaraio) into .agents/skills/lammps in your project. Codex loads it when a task matches its description.

Can I use Lammps in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ZimoLiao/scholaraio --skill lammps -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lammps, .gemini/skills/lammps, .github/skills/lammps and .opencode/skills/lammps in your project.

What does Lammps need to run?

Going by SKILL.md and its folder, Lammps needs the command-line tools its instructions call (conda and pip). Our summary lists: Python 3.

Does Lammps access the network?

SKILL.md names 1 domain. As links in the text: ctcms.nist.gov. This is read from the text; nothing was executed.

Is Lammps safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Lammps use?

Lammps is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Lammps use?

About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Lammps?

Skills that share tags, products or a category with Lammps: Investor Materials (affaan-m/ECC, 276k stars), Eas Simulator (sickn33/agentic-awesome-skills, 47k stars), Material Design (sickn33/agentic-awesome-skills, 47k stars) and Material (bergside/awesome-design-skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Lammps?

ZimoLiao (a GitHub user) maintains it in ZimoLiao/scholaraio, which has 577 GitHub stars. The repository holds 43 skills in this directory. The repository was last updated on September 25, 2026.

Source: ZimoLiao/scholaraio on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.