Agent skill · Frontend

matlab-optimize-pcb-design

Optimize RF PCB dimensions for bandwidth, return loss, or area via patternsearch and surrogateopt with constraints. TRIGGER: user asks to optimize an RF PCB component for performance (bandwidth, return loss, insertion loss, area) or apply constraints to a design. Invoke BEFORE writing optimization code — RF PCB Toolbox has a built-in optimize() function that differs from generic fmincon/ga approaches. SKIP: designing a component from scratch without an optimization objective (use the specific matlab-design-pcb-* skill), EM analysis without optimization (use matlab-analyze-em), material/stackup

matlabgithub.com/matlabGitHub ↗
claude-codecodexcopilotNOASSERTION
Install
npx skills add matlab/matlab-agentic-toolkit --skill matlab-optimize-pcb-design --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 17 KB
Bundled scripts: none
Version: 1.0
Declared author: MathWorks
Path: skills-catalog/rf-and-mixed-signal/matlab-optimize-pcb-design/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 868
Language: MATLAB

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Optimizing RF PCB Designs ## Critical: Always Use optimize() First **The RF PCB Toolbox provides a built-in `optimize()` function that handles all standard optimization scenarios.** Always use it as the first approach — do NOT write manual optimization loops with `patternsearch`, `surrogateopt`, `fmincon`, or `ga` called directly unless `optimize()` is demonstrably insufficient for the specific problem. ## When to Use - Tuning filter, coupler, or splitter dimensions for return loss, bandwidth, or area - Running `optimize()` on catalog objects with bounds and constraints - Using SADEA/TR-SADEA, patternsearch, or surrogateopt via the `optimize()` interface - Defining custom objective functions over S-parameter responses ## When NOT to Use - Designing components from scratch — use `matlab-design-pcb-filter`, `matlab-design-pcb-coupler`, etc. first, then optimize - Running EM analysis without optimization — use `matlab-analyze-em` - Parameter sweeps without an objective — just loop over `sparameters` calls directly - Antenna-only optimization without PCB context — use Antenna Toolbox docs ## Typical Workflow 1. **Before:** A design skill (`matlab-design-pcb-filter`, `matlab-design-pc

What's inside
Steps it walks through
  1. Critical: Always Use optimize() First
  2. When to Use
  3. When NOT to Use
  4. Typical Workflow
  5. Quick Reference
  6. Algorithm Overview
  7. Timing and Practical Considerations
  8. design() — Initial Sizing
  9. optimize() Syntax
  10. Built-in Objectives
  11. Example: Minimize Area with Constraints
  12. Querying Area After Optimization
  13. Example: Maximize Return Loss for Hairpin Filter
  14. S-Parameter Constraints
Ships with 1 file
  • manifest.yaml
More from matlab-agentic-toolkit
All skills →
About this skill
What does the matlab-optimize-pcb-design skill do?

Optimize RF PCB dimensions for bandwidth, return loss, or area via patternsearch and surrogateopt with constraints. TRIGGER: user asks to optimize an RF PCB component for performance (bandwidth, return loss, insertion loss, area) or apply constraints to a design. Invoke BEFORE writing optimization code — RF PCB Toolbox has a built-in optimize() function that differs from generic fmincon/ga approaches. SKIP: designing a component from scratch without an optimization objective (use the specific matlab-design-pcb-* skill), EM analysis without optimization (use matlab-analyze-em), material/stackup

How do I install it?

Run `npx skills add matlab/matlab-agentic-toolkit --skill matlab-optimize-pcb-design --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 matlab/matlab-agentic-toolkit, a repository with 868 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.

Keep going