AR
数据与复现
arxiv-database
arxiv-database：This skill provides Python tools ，用于 searching 、 retrieving preprints ，面向 arXiv.org ，通过 its public Atom A…
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数据与复现
BA
bayesian-optimizer
维护者 FreedomIntelligence · 最近更新 2026年4月1日
bayesian-optimizer：Bayesian optimization ，用于 experimental design 、 hyperparameter tuning in biomedical research。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bayesian-optimizer
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Bayesian Optimizer allows agents to efficiently explore parameter space to maximize target metric (yield，purity，binding affinity) ，支持 minimal experiments. It uses Gaussian Processes to model uncertainty 、 Upper Confidence Bound (UCB) acquisition function。
- When experiments are expensive 或 time-consuming。
- To autonomously tune hyperparameters ，用于 机器学习 model。
- To optimize reaction conditions (temperature，pH，concentration)。
- When experiments are expensive 或 time-consuming. To autonomously tune hyperparameters ，用于 机器学习 model. * To optimize reaction conditions (temperature，pH，concentration)。
原始文档
SKILL.md 摘录
Core Capabilities
- Next Step Proposal: Suggests the next best experiment parameters.
- Surrogate Modeling: Predicts outcomes for untested parameters.
- Exploration/Exploitation: Balances trying new things vs. refining known good results.
Workflow
- Input: History of past experiments (params -> results) and bounds.
- Process: Fits a Gaussian Process to the data.
- Output: Returns the parameters for the next experiment.
Example Usage
User: "Given these past results, what temperature and pH should I try next?"
Agent Action:
适用场景
- When experiments are expensive 或 time-consuming。
- To autonomously tune hyperparameters ，用于 机器学习 model。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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