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Gmx Workflow Packer

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makabaka007x
gmx-workflow-packer

Package GROMACS conventional MD, tREMD, and REST2/HREX workflows into reproducible local or cluster run bundles with blocking preflight checks. Use for prepared GROMACS systems, protein or protein-ligand MD setup, EM/NVT/NPT/production runs, ACPYPE/GAFF ligand preparation, tREMD, REST2 solute-scaling topology generation, PLUMED partial_tempering, GROMACS -hrex capability checks, resume logic, demux, exchange-efficiency analysis, or trajectory reorganization.

Overview

Publishermakabaka007x
Repositorycadd-skill
Skill namegmx-workflow-packer
Stars
54
Forks
6
Bundled files
18
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 18 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by makabaka007x on GitHub. Read the source before you install it.

Installation

Install the Gmx Workflow Packer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/makabaka007x/cadd-skill/tree/main/skills/gmx-workflow-packer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/makabaka007x/cadd-skill.git /tmp/cadd-skill
mkdir -p .claude/skills
cp -r /tmp/cadd-skill/skills/gmx-workflow-packer .claude/skills/gmx-workflow-packer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gmx Workflow Packer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gmx Workflow Packer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gmx Workflow Packer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Gmx Workflow Packer

Overview

把一次性的 GROMACS 常规 MD、tREMD 或 REST2 操作整理成可复用、可交付的目录和脚本。 默认目标不是只解释参数，而是留下一个能在本地准备、上传目标机器并适配提交的运行包；如果当前模块不支持某种增强采样，必须生成阻断式说明，不能给出会失败的提交脚本。

入口判断

先把请求归到下面五类入口之一：

  1. 常规 GROMACS MD
  • 适用于标准 EM -> position-restrained/NVT -> NPT -> production MD。
  • 可从已有 topol.top 和能量最小化坐标开始，也可从蛋白 PDB 生成准备脚本。
  • 使用 assets/md-config-template.yaml 和 scripts/build_md_bundle.py。
  1. 已有 GROMACS 体系直接打包为 tREMD
  • 适用于用户已经有 topol.top、1EM.gro 或等价起始坐标。
  • 直接生成 step1/step2/analysis/run.sh/state.yaml。
  • 这是最稳的主路径。
  1. 标准蛋白本地准备后打包为 tREMD
  • 适用于用户只有蛋白 PDB。
  • 生成本地准备脚本，执行 pdb2gmx -> editconf -> solvate -> genion -> EM。
  • 再生成标准 tREMD 运行目录。
  1. 蛋白配体本地准备后打包
  • 适用于蛋白配体复合物做标准 tREMD。
  • 支持两种配体入口：
  • 独立 ligand.mol2 或 ligand.sdf
  • 从 complex.pdb 中拆出配体
  • 配体默认用 ACPYPE/GAFF，但不自动安装依赖。
  1. REST2/HREX 打包
  • 适用于用户已有 1EM.gro 或等价起始坐标、topol.top，希望只升温溶质或 hot region。
  • 默认使用 PLUMED 官方 partial_tempering 生成每个 replica 的缩放拓扑。
  • REST2 v2 默认按能力检查结果决定是否生成生产脚本。
  • 如果 mdrun -h 能看到 -plumed 但没有 -hrex，只生成拓扑准备与预检包，生产 run.sh 必须阻断并解释“缺少 -hrex，不能直接做 PLUMED 多拓扑 REST2 交换”。
  • 只有在用户明确提供带 -hrex 的 GROMACS/PLUMED 模块，并把 rest2.hrex_available=true 时，才生成真正的 REST2-HREX 生产脚本。
  • REST2 细节读取 references/rest2-workflow.md。
  • 如果用户要求非 PLUMED 的手工拓扑缩放、CHARMM CMAP、或复杂自定义 Hamiltonian，先说明需要人工审查，不要盲目自动化。

默认工作流

默认按下面顺序推进：

  1. 先检查环境
  • 运行 scripts/check_env.py。
  • 如果缺少 gmx、gmx_mpi、perl，先停止。
  • 如果是蛋白配体路径，再额外检查 acpype、antechamber、obabel。
  • 如果是 REST2 路径，运行 scripts/check_env.py --mode rest2 --check-hrex，并在目标模块中确认 gmx_mpi mdrun -h 是否同时支持 -hrex 和 -plumed。
  • 如果检查结果缺少 -hrex，必须阻断 REST2 生产提交，只允许生成准备和预检包。
  1. 读取或生成 YAML 配置
  • 常规 MD 默认使用 assets/md-config-template.yaml 作为起点。
  • tREMD/REST2 默认使用 assets/config-template.yaml 作为起点。
  • sampling.mode=md 使用 scripts/build_md_bundle.py。
  • sampling.mode=tremd 使用 scripts/build_remd_bundle.py。
  • sampling.mode=rest2 使用 scripts/build_rest2_bundle.py。
  1. 确定温度梯度
  • tREMD：如果配置里已经给出 temperature_ladder.temperatures，直接使用；否则运行 scripts/optimize_remd_ladder.py 估算。
  • REST2：使用 rest2.effective_temperatures；如果为空，用 rest2.tmin/tmax/n_replicas 生成几何 effective-temperature 梯度，缩放因子为 scale_i = T0 / T_i。
  • 所有梯度都只是经验初值。默认提醒用户先短跑检查交换率，再决定是否加密或拉宽梯度。
  1. 生成运行包
  • 常规 MD 运行 scripts/build_md_bundle.py。
  • tREMD 运行 scripts/build_remd_bundle.py。
  • REST2 运行 scripts/build_rest2_bundle.py。
  • 常规 MD 输出目录保留 mdp/run/analysis/prep/system/state.yaml/UPLOAD_AND_RUN.md 语义。
  • tREMD 输出目录固定保留 step1/step2/analysis 语义。
  • REST2 输出目录使用 prep/rest2/analysis/run.sh/state.yaml，其中 rest2/replica_00 等目录直接对应 REST2 replica。
  • rest2.hrex_available=false 时，run.sh 是阻断脚本，不能提交生产；rest2.hrex_available=true 时，run.sh 才是生产脚本。
  • 副本目录统一使用 replica_00 风格，不再沿用旧的 equ_0 或 MD_0 命名。
  1. 生成后处理包
  • 运行 scripts/render_postprocess_bundle.py，或让 build_remd_bundle.py 自动调用它。
  • 输出 demux.pl、demux.sh、交换效率分析脚本、按温度轨迹分析骨架。

集群配置原则

  • step1 和 step2 可以使用 GPU 队列，正式多副本 run.sh 常用 CPU 或 MPI 队列，具体以目标集群为准。
  • GPU/CPU partition、QOS、module、account、wall-time、MPI launcher 和最大任务数必须由用户或集群文档确认。
  • cluster.cpu.max_total_tasks 必须大于等于 replica 数；否则停止并要求用户减少 replica 数或提供更高资源限制。
  • GROMACS 可执行入口通常是 gmx 或 gmx_mpi，不要把某个集群路径写成公开默认。

关键约束

始终遵循这些约束：

  • run.sh 使用单脚本分段续跑。
  • 续跑必须依赖 -cpi -append 和 state.yaml。
  • 默认不要在任何提交脚本里写 #SBATCH --time。
  • 默认不要在生产 run.sh 中使用 -maxh。
  • 不要复用旧版 step2.sh 的逗号字符串温度循环。
  • 副本目录必须零填充，避免 replica_1 replica_10 replica_2 这种字典序问题。
  • 正式 CPU 多副本任务默认 --ntasks-per-node 等于副本数；如果超过 cluster.cpu.max_total_tasks，必须停下来让用户调整配置。
  • REST2 的 effective temperature 不是 thermostat 真实温度；REST2 各 replica 的 md.mdp 默认使用同一个参考温度，采样增强来自缩放 Hamiltonian。
  • REST2 拓扑必须从 grompp -pp 生成的 processed topology 开始标记 hot atoms，不要直接改带 #include 的原始 topol.top。
  • PLUMED partial_tempering 多拓扑 REST2 生产脚本必须包含 -hrex -plumed plumed.dat -dlb no，并要求 nstlist 能整除 replex。
  • 如果 mdrun -h 只有 -plumed 没有 -hrex，不要用 -replex 伪装 REST2；这只会变成错误的普通多模拟或在运行时报错。
  • GROMACS 官方原生 HREX 是 free-energy lambda 路线，不等价于 PLUMED partial_tempering 多拓扑 REST2；除非用户专门要求并提供 lambda 拓扑设计，不要自动切换。

主要资源

优先使用这些资源，不要现场重写：

  • 环境检查：
  • scripts/check_env.py
  • 温度梯度估算：
  • scripts/optimize_remd_ladder.py
  • 运行包生成：
  • scripts/build_md_bundle.py
  • scripts/build_remd_bundle.py
  • REST2 运行包生成：
  • scripts/build_rest2_bundle.py
  • 后处理生成：
  • scripts/render_postprocess_bundle.py
  • 标准 tREMD 工作流：
  • references/core-workflow.md
  • REST2 教程：
  • references/rest2-workflow.md
  • HREMD/复杂 REST2 注意事项：
  • references/future-rest2.md
  • 默认配置模板：
  • assets/md-config-template.yaml
  • assets/config-template.yaml
  • 默认 mdp 模板：
  • assets/templates/
  • demux 资源：
  • assets/demux.pl

输出要求

把目录视为“已完成可交付”之前，至少要满足：

  • 有完整 YAML 配置副本。
  • 常规 MD 包有 mdp/run/analysis/system/run.sh/state.yaml/UPLOAD_AND_RUN.md。
  • tREMD 包有 step1/step2/analysis/run.sh/state.yaml/UPLOAD_AND_RUN.md。
  • REST2 包有 prep/rest2/run.sh/state.yaml/UPLOAD_AND_RUN.md；只有 rest2.hrex_available=true 时才要求 analysis/ 后处理脚本。
  • 有温度表 YAML 和 CSV。
  • 有中文说明，写清首跑、续跑、demux 和效率分析命令。
  • REST2 包还必须有 effective-temperature/scale 表、prep/prepare_rest2_topologies.sh、prep/mark_rest2_hot_atoms.py，以及每个 replica 的 plumed.dat。
  • 如果只是 dry-run，要明确说明哪些输入文件还未准备好。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gmx Workflow Packer AI skill do?

Package GROMACS conventional MD, tREMD, and REST2/HREX workflows into reproducible local or cluster run bundles with blocking preflight checks. Use for prepared GROMACS systems, protein or protein-ligand MD setup, EM/NVT/NPT/production runs, ACPYPE/GAFF ligand preparation, tREMD, REST2 solute-scaling topology generation, PLUMED partial_tempering, GROMACS -hrex capability checks, resume logic, demux, exchange-efficiency analysis, or trajectory reorganization.

Why use Gmx Workflow Packer on TypingMind?

Because you install it once and use it with any model. Gmx Workflow Packer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gmx Workflow Packer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/makabaka007x/cadd-skill/tree/main/skills/gmx-workflow-packer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gmx Workflow Packer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gmx Workflow Packer?

As many as you like. As long as a model supports skills, you can use Gmx Workflow Packer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gmx Workflow Packer AI skill free?

It is published on GitHub by makabaka007x. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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