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.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill parameter-optimization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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, unifor
- Goal
- Requirements
- Inputs to Gather
- Decision Guidance
- Choosing a DOE Method
- Choosing an Optimizer
- Script Outputs (JSON Fields)
- Workflow
- CLI Examples
- Conversational Workflow Example
- Error Handling
- Limitations
- References
- Version History
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 python3 scripts/doe_generator.py --params 2 --budget 30 --method lhs --json python3 scripts/sensitivity_summary.py --scores 0.7,0.3 --names conductivity,diffusivity --json
What does the parameter-optimization skill do?
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.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill parameter-optimization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.