OpenClaw-Medical-Skills parameter-optimization

Explore and optimize simulation parameters via design of experiments (DOE), sensitivity analysis, and optimizer selection. Use for calibration, uncertainty studies, parameter sweeps, LHS sampling, Sobol analysis, surrogate modeling, or Bayesian optimization setup.

install

source · Clone the upstream repo

git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/parameter-optimization" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-parameter-optimization && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/parameter-optimization" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-parameter-optimization && rm -rf "$T"

manifest: skills/parameter-optimization/SKILL.md

Parameter Optimization

Goal

Provide a workflow to design experiments, rank parameter influence, and select optimization strategies for materials simulation calibration.

Requirements

Python 3.8+
No external dependencies (uses Python standard library only)

Inputs to Gather

Before running any scripts, collect from the user:

Input	Description	Example
Parameter bounds	Min/max for each parameter with units	`kappa: [0.1, 10.0] W/mK`
Evaluation budget	Max number of simulations allowed	`50 runs`
Noise level	Stochasticity of simulation outputs	`low` , `medium` , `high`
Constraints	Feasibility rules or forbidden regions	`kappa + mobility < 5`

Decision Guidance

Choosing a DOE Method

Is dimension <= 3 AND full coverage needed?
├── YES → Use factorial
└── NO → Is sensitivity analysis the goal?
    ├── YES → Use quasi-random (preferred; "sobol" is accepted but deprecated)
    └── NO → Use lhs (Latin Hypercube)

Method	Best For	Avoid When
`lhs`	General exploration, moderate dimensions (3-20)	Need exact grid coverage
`sobol`	Sensitivity analysis, uniform coverage	Very high dimensions (>20)
`factorial`	Low dimension (<4), need all corners	High dimension (exponential growth)

Choosing an Optimizer

Is dimension <= 5 AND budget <= 100?
├── YES → Bayesian Optimization
└── NO → Is dimension <= 20?
    ├── YES → CMA-ES
    └── NO → Random Search with screening

Noise Level	Recommendation
Low	Gradient-based if derivatives available, else Bayesian Optimization
Medium	Bayesian Optimization with noise model
High	Evolutionary algorithms or robust Bayesian Optimization

Script Outputs (JSON Fields)

Script	Output Fields
`scripts/doe_generator.py`	`samples` , `method` , `coverage`
`scripts/optimizer_selector.py`	`recommended` , `expected_evals` , `notes`
`scripts/sensitivity_summary.py`	`ranking` , `notes`
`scripts/surrogate_builder.py`	`model_type` , `metrics` , `notes`

Workflow

Generate DOE with
```
scripts/doe_generator.py
```
Run simulations at DOE sample points (user's responsibility)
Summarize sensitivity with
```
scripts/sensitivity_summary.py
```
Choose optimizer using
```
scripts/optimizer_selector.py
```
(Optional) Fit surrogate with
```
scripts/surrogate_builder.py
```

CLI Examples

# Generate 20 LHS samples for 3 parameters
python3 scripts/doe_generator.py --params 3 --budget 20 --method lhs --json

# Rank parameters by sensitivity scores
python3 scripts/sensitivity_summary.py --scores 0.2,0.5,0.3 --names kappa,mobility,W --json

# Get optimizer recommendation for 3D problem with 50 eval budget
python3 scripts/optimizer_selector.py --dim 3 --budget 50 --noise low --json

# Build surrogate model from simulation data
python3 scripts/surrogate_builder.py --x 0,1,2 --y 10,12,15 --model rbf --json

Conversational Workflow Example

User: I need to calibrate thermal conductivity and diffusivity for my FEM simulation. I can run about 30 simulations.

Agent workflow:

Identify 2 parameters →
```
--params 2
```
Budget is 30 →
```
--budget 30
```

Use LHS for general exploration:

python3 scripts/doe_generator.py --params 2 --budget 30 --method lhs --json

After user runs simulations and provides outputs, summarize sensitivity:

python3 scripts/sensitivity_summary.py --scores 0.7,0.3 --names conductivity,diffusivity --json

Recommend optimizer:

python3 scripts/optimizer_selector.py --dim 2 --budget 30 --noise low --json

Error Handling

Error	Cause	Resolution
`params must be positive`	Zero or negative dimension	Ask user for valid parameter count
`budget must be positive`	Zero or negative budget	Ask user for realistic simulation budget
`method must be lhs, sobol, or factorial`	Invalid method	Use decision guidance to pick valid method
`scores must be comma-separated`	Malformed input	Reformat as `0.1,0.2,0.3`

Limitations

Not for real-time optimization: Scripts provide recommendations, not live optimization loops
Surrogate is a placeholder:
```
surrogate_builder.py
```
computes basic metrics; replace with actual model for production
No automatic simulation execution: User must run simulations externally and provide results

References

```
references/doe_methods.md
```
- Detailed DOE method comparison
```
references/optimizer_selection.md
```
- Optimizer algorithm details
```
references/sensitivity_guidelines.md
```
- Sensitivity analysis interpretation
```
references/surrogate_guidelines.md
```
- Surrogate model selection

Version History

v1.1.0 (2024-12-24): Enhanced documentation, decision guidance, conversational examples
v1.0.0: Initial release with core scripts