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reinvent4reinvent4 效率

Agent Skill

reinvent4 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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

3,127

周安装

129

GitHub Stars

公开资料未说明

下载量

1,022
OpenClaw

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

MIT-0

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:reinvent4(reinvent4 效率)
来源仓库:https://github.com/zoeprior/reinvent4
安装命令:
openclaw skills install reinvent4
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install reinvent4

简介

reinvent4 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。

  • 适用于分子设计生成场景,支持 REINVENT4 模型的运行与扩展。
  • 通过 clawhub 安装,命令为 openclaw skills install reinvent4。
  • 建议确认权限范围、维护状态及是否触发联网或文件读写操作。
  • reinvent4 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
reinvent4
description
Help install, configure, run, troubleshoot, and extend MolecularAI REINVENT4 for generative molecular design. Use when the user mentions REINVENT4 or Reinvent 4, asks about reinvent or reinvent_datapre, needs to choose an install backend like mac, cpu, cu126, rocm6.4, or xpu, wants help editing configs/*.toml, working with sampling, scoring, transfer learning, staged learning, notebooks, or scoring plugins, or needs guidance grounded in the upstream REINVENT4 repository.

REINVENT4

Prefer a local REINVENT4 checkout over web summaries. Treat a directory as the repo root when it contains install.py, pyproject.toml, reinvent/, and configs/.

Workflow

  1. Classify the request: install, run configuration, data preprocessing,

notebook conversion, scoring plugin, or test/troubleshooting.

  1. Read only the relevant reference file:

- installation or CLI usage: references/install-and-run.md - TOML mode selection or parameter mapping: references/config-modes.md - plugins, notebooks, or tests: references/plugins-and-tests.md

  1. Verify commands against the local checkout before proposing them. Prefer

python install.py --help, python install.py <backend> --dry-run, reinvent --help, direct file inspection, and existing files under configs/.

  1. Reuse the example configs in configs/ instead of inventing schemas from

scratch.

  1. Keep file paths explicit. Upstream example configs are templates and must be

adjusted to local model, SMILES, output, and log paths before execution.

Installation Rules

  • Use an isolated Python environment with Python 3.10 or newer.
  • Map the processor/backend carefully:

- macOS CPU: mac - Linux CPU: cpu - Linux NVIDIA CUDA: upstream examples use values like cu126 - Linux AMD ROCm: upstream examples use values like rocm6.4 - Intel XPU: xpu - Windows: CPU, CUDA, and XPU are supported, but upstream says Windows is only partially tested

  • Remember that install.py defaults to optional dependency set all, which

includes extra packages such as openeye and isim.

  • Prefer -d none for minimal or smoke-test installs unless the user

explicitly needs OpenEye ROCS or iSIM-related functionality.

  • Use --dry-run before a real install whenever backend choice or dependency

resolution is uncertain.

  • Verify a finished install with reinvent --help.

Running REINVENT

  • Main CLI entry point: reinvent [-l logfile] <config.toml>.
  • Data pipeline entry point from pyproject.toml: reinvent_datapre.
  • Prefer TOML because upstream ships maintained examples in configs/.
  • When editing configs, update at least device selection, model/prior paths,

SMILES inputs, output files, and TensorBoard/log paths.

Troubleshooting Rules

  • On macOS, remind the user that upstream documents CPU-only support and says

macOS is only partially tested.

  • If a macOS clone reports path collisions under

contrib/tutorials/maize/adgpu_prepare, avoid relying on those collided files unless the user specifically needs that tutorial.

  • For tests, warn that they require a JSON config with a non-existent

MAIN_TEST_PATH; some tests also require OE_LICENSE.

  • Do not promise a full RL/TL run unless models, datasets, and optional

licensed tools are present locally.

Source Files

Use these upstream files as the primary source of truth when they are present in the local checkout:

  • README.md
  • install.py
  • pyproject.toml
  • configs/README.md
  • configs/PARAMS.md
  • configs/SCORING.md
  • notebooks/README.md
  • tests/example_config.json

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

71.81%
按下载量换算734

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。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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