bayesian-optimizer
Bayesian Optimizer efficiently explores parameter spaces to maximize target metrics like yield or binding affinity using Gaussian Processes and the Upper Confidence Bound acquisition function. Use this skill when experiments are expensive or time-consuming, for autonomous hyperparameter tuning in machine learning, or to optimize reaction conditions by balancing exploration of new parameters with refinement of known effective results.
git clone --depth 1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills /tmp/bayesian-optimizer && cp -r /tmp/bayesian-optimizer/skills/bayesian-optimizer ~/.claude/skills/bayesian-optimizerSKILL.md
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---
name: 'bayesian-optimizer'
description: 'Bayesian Optimize'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
---
# Bayesian Optimization (Self-Driving Lab)
The **Bayesian Optimizer** allows agents to efficiently explore a parameter space to maximize a target metric (yield, purity, binding affinity) with minimal experiments. It uses Gaussian Processes to model uncertainty and the Upper Confidence Bound (UCB) acquisition function.
## When to Use This Skill
* When experiments are expensive or time-consuming.
* To autonomously tune hyperparameters for a machine learning model.
* To optimize reaction conditions (temperature, pH, concentration).
## Core Capabilities
1. **Next Step Proposal**: Suggests the next best experiment parameters.
2. **Surrogate Modeling**: Predicts outcomes for untested parameters.
3. **Exploration/Exploitation**: Balances trying new things vs. refining known good results.
## Workflow
1. **Input**: History of past experiments (params -> results) and bounds.
2. **Process**: Fits a Gaussian Process to the data.
3. **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**:
```bash
python3 Skills/Mathematics/Probability_Statistics/bayesian_optimization.py \
--history "[[20, 7.0, 0.5], [25, 6.5, 0.6]]" \
--bounds "[[10, 40], [5, 9]]" \
--output next_experiment.json
```
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