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boron-nmr-predict硼核磁共振预测

Agent Skill

boron-nmr-predict 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

218

周安装

9

GitHub Stars

44

下载量

71
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:boron-nmr-predict(硼核磁共振预测)
来源仓库:https://github.com/internscience/chemclaw
仓库路径:skills/boron-nmr-predict
安装命令:
npx skills add https://github.com/internscience/chemclaw --skill boron-nmr-predict
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/internscience/chemclaw --skill boron-nmr-predict

简介

使用本地模型预测硼原子 NMR 化学位移值并提供可视化标注图像。

  • 输入要求 SMILES 格式分子结构与溶剂环境参数方可启动推理。
  • 依赖 conda 环境与预下载模型文件完成确定性本地计算流程。
  • 输出包含 ppm 数值与对应原子索引的结构图便于人工核对验证。
  • boron-nmr-predict 属于待分类类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Boron NMR Predict

Use the bundled scripts for deterministic local inference.

Workflow

  1. Create and activate a fresh conda environment for this skill.
  2. Install Python dependencies from requirements-core.txt and requirements-pyg.txt.
  3. Ensure model files exist locally by running scripts/ensure_model.py.
  4. Run scripts/predict_boron_nmr.py with a SMILES string and solvent.
  5. Return the predicted chemical shift for each boron atom in text form.
  6. Generate the labeled PNG image into the user's tmp directory so each ppm value can be matched to B(index) in the structure image.

Input contract

Prefer SMILES input.

Required:

  • Molecule SMILES containing at least one boron atom

Optional:

  • Solvent name. Supported solvents are:

- CDCl3 - C6D6 - d6-DMSO - CD3COCD3 - CD3CN - CD3OD - CD2Cl2 - d8-THF - d8-Toluene - D2O

Commands

Create a fresh conda environment and install deps:

bash scripts/setup_env.sh

The setup script is portable: it does not assume any machine-specific conda path. It first tries the current shell's conda, then common user-local installs such as ~/miniconda3, ~/anaconda3, and ~/conda.

Manual alternative:

conda create -n boron-nmr-predict python=3.11 -y
conda activate boron-nmr-predict
python -m pip install -r requirements-core.txt
python -m pip install -r requirements-pyg.txt

Download model weights:

python scripts/ensure_model.py

Run prediction:

python scripts/predict_boron_nmr.py \
  --smiles "OB(O)c1ccccc1" \
  --solvent CDCl3 \
  --output-image /tmp/boron_nmr_result.png

Run an example:

bash scripts/run_example.sh

Environment variables

  • BORON_NMR_MODEL_REPO: Hugging Face repo id holding the model files
  • BORON_NMR_MODEL_DIR: local cache directory for downloaded model files

Defaults:

  • model repo: SII-AI4Chem/boron-nmr-predict-model
  • model dir: ~/.cache/boron-nmr-predict/models
  • image output: user tmp directory such as /tmp/boron_nmr_<id>.png
  • device: CPU only

Output contract

Return:

  • a text summary for the user
  • canonical SMILES
  • solvent
  • number of boron atoms
  • per-boron predictions with:

- atom_index - element - ppm

  • image path in the user's tmp directory when generated
  • image error message when text prediction succeeds but image rendering fails

When replying to the user:

  • explicitly tell the user where the image file was saved
  • include the concrete image_path in the reply
  • if the runtime/channel supports file sending, send the generated image file to the user as an attachment
  • if file sending is unavailable, still tell the user the exact saved path so they can retrieve it

Notes

  • Keep inference on CPU.
  • Use the bundled source files in src/ instead of the original web app.
  • The labeled image uses B(index) so users can map ppm values to specific boron atoms.
  • By default, model files are downloaded from SII-AI4Chem/boron-nmr-predict-model. Override only when a different repo is explicitly required.
  • Image rendering failure should not block the text prediction result; return the text result and include an image error when needed.
  • After successful image generation, do not only mention that an image exists; tell the user the saved location and send the image when channel capabilities allow it.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.67%
按下载量换算27

Claude

29.78%
按下载量换算21

Cursor

19.6%
按下载量换算14

Gemini CLI

8.89%
按下载量换算6

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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