SynBO (Synthetic Bayesian Optimization)

SKILL效率
效率 未标注权限 许可证不明确
安装这个技能

Bayesian optimization for chemical reactions using the synbo package. This skill provides Python scripts to set up reaction spaces, build descriptors, run optimization, download recommended conditions, and upload results.

一句话选型结论

按资料字段整理

SynBO (Synthetic Bayesian Optimization)是效率类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类效率 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_4ffb55cd/synbo
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

效率
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_4ffb55cd仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791736597
正文体量5.3 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
整理待办与日程

把零散任务收进清单,排出优先级。

批量处理重复任务

把固定动作做成模板,重复执行。

把流程写成清单

把口头流程整理成可复用的步骤。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在效率场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
SynBO (Synthetic Bayesian Optimization)当前 未标注权限 未标注 未标注 未标注 有
Nano PDF 文档 联网访问 ClawHub 市场 424.9 万 OpenClaw 有
自动更新程序 浏览器控制 ClawHub 市场 296.4 万 OpenClaw 有
Gemini 助手 联网访问 ClawHub 市场 254.7 万 OpenClaw 有
word DOCX 文档 联网访问 ClawHub 市场 239.6 万 OpenClaw 有
Excel XLSX 前端 联网访问 ClawHub 市场 227.0 万 OpenClaw 有

SKILL.md 全文

5.3 KB
模型实际读到的说明书原文,来自技能仓库

synbo

Bayesian optimization for chemical reactions using the synbo package. This skill provides Python scripts to set up reaction spaces, build descriptors, run optimization, download recommended conditions, and upload results.


CRITICAL: BO Optimization Prerequisites

Before executing ANY Bayesian Optimization (synbo) tasks, you MUST sequentially verify the following 5 prerequisites. Do NOT proceed with the optimization process until ALL criteria are met:

1. Find Conda Environment and synbo package
- Verify if conda is installed in the current environment. If not, confirm with the user to install Miniconda (see reference/installation.md).
- Check if the synbo_env conda environment exists. If not, create it; if it exists, activate it.
- Verify if the synbo package is installed in synbo_env. If not, run pip install synbo

2. Working Directory (project_wd) & Project Name (project_name)
* Initial Check: Read config.json located in the skill's directory. If both project_wd and project_name are found, display the project name to the user (e.g., "Found existing project: [Project Name]") and use them.
* If NOT found: Stop and prompt the user to input a Working Directory and a Project Name.
* Validation & Saving (CRITICAL): Upon receiving the user's input:
1. Verify Path: Check if the provided working directory actually exists on the local file system. If it does NOT exist, inform the user "The path is invalid/does not exist" and prompt them to re-enter it.
2. Sanitize & Save: If the path exists, sanitize the project name (replace spaces and special characters with underscores). Then, immediately write/update the config.json file with this format: {"project_wd": "xxx", "project_name": "xxx"}.
3. Use this path and sanitized project name to define the save_dir for all subsequent outputs.

3. Reaction Space
* Check: Verify if standard reaction space data exists specifically within the project_wd/rxn_space directory.
* If NOT found: Stop and prompt the user: "Reaction space data is missing in the project_wd/rxn_space directory. Please provide the standard reaction space data." Do not proceed until provided.

4. Condition Descriptors
* Check: Verify if the corresponding Condition Descriptors exist specifically within the project_wd/descriptors directory.
* If NOT found: Stop and prompt the user: "Condition Descriptors are missing in the project_wd/descriptors directory. Please provide the standard Condition Descriptors, OR let me know if you would like me to automatically generate them for you."

5. Optimization Metrics
* Check: Verify if the optimization settings file (e.g., optimization_settings.json) exists directly within the project_wd directory.
* If NOT found: Stop and prompt the user: "Optimization metrics are not defined. Please specify the target metrics you want to optimize (e.g., yield, ee), along with their optimization direction (max/min), expected numerical ranges, and relative weights (default 1.0)."
* Validation & Saving (CRITICAL): Upon receiving the user's optimization goals, format the data and immediately save it as optimization_settings.json in the project_wd. The JSON file MUST strictly adhere to the following structure:

{
    "reagent_types": ["reagent1", "reagent2", "condition1", "condition2"],
    "opt_metrics": ["target1", "target2"],
    "opt_direct_info": [
        {
            "opt_direct": "max",
            "opt_range": [0, 100],
            "metric_weight": 1.0
        },
        {
            "opt_direct": "min",
            "opt_range": [0, 100],
            "metric_weight": 1.0
        }
    ]
}

Execution Block:
You are strictly forbidden from executing any initialization (initialize), optimization (optimize), or other synbo tasks until Steps 1 through 4 are fully verified and resolved.


Reaction Space

When the user is required to provide the reaction space data, they may submit it via one of two methods:
1. Direct Input: Providing the SMILES strings for the corresponding molecules directly in the chat.
2. File Upload: Providing tabular files containing the SMILES strings.

Naming Conventions:
It is highly recommended that the user assigns a specific name to each molecule. If no names are provided, you must automatically assign names using a sequential format based on the reagent type: {reagent_type}-1, {reagent_type}-2, etc.

Data Storage Rules:
Regardless of the user's submission method, all reaction space data must be formatted and saved strictly into the project_wd/rxn_space directory.
* Each reagent type must be saved as an individual file named {reagent_type}.csv.
* The CSV files must contain exactly two headers: SMILES (for the SMILES strings) and name (for the molecule names).


Condition Descriptors

see [reference/get_desc.md](reference/get_desc.md)


Initialize

see [reference/initialize.md](reference/initialize.md)


Optimize

see [reference/optimize.md](reference/optimize.md)

常见问题

5 问
答案由本站依据条目字段整理
SynBO (Synthetic Bayesian Optimization)怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_4ffb55cd/synbo。安装方式为未标注。装完刷新技能列表或重开客户端。

SynBO (Synthetic Bayesian Optimization)支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

SynBO (Synthetic Bayesian Optimization)权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

SynBO (Synthetic Bayesian Optimization)收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

SynBO (Synthetic Bayesian Optimization)用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。