Wp Performance Review
elvismdev/claude-wordpress-skills
WordPress performance code review and optimization analysis.
Explore and optimize simulation parameters via design of experiments (DOE), sensitivity analysis, and optimizer selection.
Install Parameter Optimization
$ npx skills add majiayu000/claude-skill-registry --skill parameter-optimization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install majiayu000/claude-skill-registry parameter-optimization --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analysis/parameter-optimization .claude/skills/parameter-optimization && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "parameter-optimization" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/analysis/parameter-optimization into .claude/skills/parameter-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "parameter-optimization", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/majiayu000/claude-skill-registry/tree/main/skills/analysis/parameter-optimizationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add majiayu000/claude-skill-registry --skill parameter-optimization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install majiayu000/claude-skill-registry parameter-optimization --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/analysis/parameter-optimization .agents/skills/parameter-optimization && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "parameter-optimization" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/analysis/parameter-optimization into .agents/skills/parameter-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "parameter-optimization", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add majiayu000/claude-skill-registry --skill parameter-optimization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install majiayu000/claude-skill-registry parameter-optimization --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/analysis/parameter-optimization .cursor/skills/parameter-optimization && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "parameter-optimization" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/analysis/parameter-optimization into .cursor/skills/parameter-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "parameter-optimization", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/majiayu000/claude-skill-registry.git --path skills/analysis/parameter-optimization--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add majiayu000/claude-skill-registry --skill parameter-optimization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install majiayu000/claude-skill-registry parameter-optimization --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/analysis/parameter-optimization .gemini/skills/parameter-optimization && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "parameter-optimization" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/analysis/parameter-optimization into .gemini/skills/parameter-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "parameter-optimization", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install majiayu000/claude-skill-registry parameter-optimizationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add majiayu000/claude-skill-registry --skill parameter-optimization -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/analysis/parameter-optimization .github/skills/parameter-optimization && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "parameter-optimization" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/analysis/parameter-optimization into .github/skills/parameter-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "parameter-optimization", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add majiayu000/claude-skill-registry --skill parameter-optimization -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install majiayu000/claude-skill-registry parameter-optimization --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/analysis/parameter-optimization .opencode/skills/parameter-optimization && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "parameter-optimization" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/analysis/parameter-optimization into .opencode/skills/parameter-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "parameter-optimization", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
parameter-optimization- GitHub stars
- 666
- Used in
- 2 other repos
- Token cost
- ~1.6k tokens
- SKILL.md length
- 495 words
- Files
- 2
- Skills in repo
- 971
- Repo updated
- First seen
- Licence
- MIT
At a glance
Explore and optimize simulation parameters via design of experiments (DOE), sensitivity analysis, and optimizer selection.
- Works in 5 steps: Generate DOE with scripts/doe_generator.py → Run simulations at DOE sample points… → Summarize sensitivity with… → …
- Uncertainty studies
- SKILL.md covers Goal, Requirements, Inputs to Gather and Decision Guidance, plus 9 more sections
- Calls python3
What it does
Parameter Optimization is an agent skill from majiayu000/claude-skill-registry. 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.
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).
It sits in Business, Finance & HR, covering Performance reviews. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.
When your agent uses it
- Uncertainty studies
- Parameter sweeps
- Surrogate modeling
- Bayesian optimization setup
Example prompts
- “/parameter-optimization”
Requirements
- Python 3
- Pre-approved tools (allowed-tools): Read, Write, Grep, Glob
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- 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
What it can do on your machine
Read from SKILL.md and the folder at commit 000116a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteGrepGlob
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Parameter Optimization loads about 1.6k tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 495 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from majiayu000/claude-skill-registry at commit 000116a, republished under its MIT licence (© majiayu000). 495 words, ~1,569 tokens.
Download SKILL.mdSave it as
.claude/skills/parameter-optimization/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- parameter-optimization
- description
- 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.
- allowed-tools
- Read, Write, Grep, Glob
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 --jsonConversational 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:
bash python3 scripts/doe_generator.py --params 2 --budget 30 --method lhs --json - After user runs simulations and provides outputs, summarize sensitivity:
bash python3 scripts/sensitivity_summary.py --scores 0.7,0.3 --names conductivity,diffusivity --json - Recommend optimizer:
bash python3 scripts/optimizer_selector.py --dim 2 --budget 30 --noise low --json
Show full SKILL.md (233 more words)Show less
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 |
Security
The parameter-optimization scripts enforce the following safeguards:
- Parameter name validation:
sensitivity_summary.pyvalidates--namesagainst[a-zA-Z_][a-zA-Z0-9_ .-]*with a 200-char limit, preventing shell metacharacter injection via crafted parameter names. - Input length limits: Comma-separated value lists are capped (10,000 for scores, 100,000 for surrogate data) to prevent resource exhaustion.
- Finite-value enforcement: All numeric list inputs are validated as finite numbers (
NaN/Infrejected). - Dimension/budget bounds:
doe_generator.pycaps dim at 1,000 and budget at 1,000,000;optimizer_selector.pycaps dim at 100,000 and budget at 10,000,000. - Reduced tool surface: The skill's
allowed-toolsexcludesBashto prevent the agent from executing arbitrary commands when processing user-provided parameter names and constraints.
Limitations
- Not for real-time optimization: Scripts provide recommendations, not live optimization loops
- Surrogate is a placeholder:
surrogate_builder.pycomputes 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 comparisonreferences/optimizer_selection.md- Optimizer algorithm detailsreferences/sensitivity_guidelines.md- Sensitivity analysis interpretationreferences/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
© majiayu000, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in skills/analysis/parameter-optimization of majiayu000/claude-skill-registry.
- SKILL.md
- metadata.json
Open the folder on GitHubat commit 000116a
Used in 2 other repositories
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Parameter Optimization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Parameter Optimization this skillmajiayu000/claude-skill-registry | 666 | 2 repos | ~1.6k | Automated safety check: Pass | MIT | |
| Wp Performance Reviewelvismdev/claude-wordpress-skills | 234 | 1 repos | ~4.5k | Automated safety check: Pass | MIT | |
| Align Humanagentscope-ai/OpenJudge | 867 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Performance ReportAffitor/affiliate-skills | 698 | 1 repos | ~2.5k | Automated safety check: Pass | MIT | |
| Run Mv Hoi Reconstructionnvidia-isaac/video_to_data | 847 | — | ~1.5k | Automated safety check: Pass | Custom licence | |
| Company Analysiszhu1090093659/dsh-trading | 230 | — | ~4.2k | Automated safety check: Pass | Custom licence |
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Related
Categories
Questions about Parameter Optimization
What does Parameter Optimization do?
Explore and optimize simulation parameters via design of experiments (DOE), sensitivity analysis, and optimizer selection. Parameter Optimization is an agent skill from majiayu000/claude-skill-registry. Explore and optimize simulation parameters via design of experiments (DOE), sensitivity analysis, and optimizer selection.
When should I use Parameter Optimization?
Parameter Optimization fits situations like: uncertainty studies; parameter sweeps; surrogate modeling; bayesian optimization setup.
How do I install Parameter Optimization in Claude Code?
Run `npx skills add majiayu000/claude-skill-registry --skill parameter-optimization -a claude-code`. Or copy the skill folder (skills/analysis/parameter-optimization in majiayu000/claude-skill-registry) into .claude/skills/parameter-optimization in your project. Claude Code loads it when a task matches its description.
How do I install Parameter Optimization in Codex?
Run `npx skills add majiayu000/claude-skill-registry --skill parameter-optimization -a codex`. Or copy the skill folder (skills/analysis/parameter-optimization in majiayu000/claude-skill-registry) into .agents/skills/parameter-optimization in your project. Codex loads it when a task matches its description.
Can I use Parameter Optimization in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add majiayu000/claude-skill-registry --skill parameter-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/parameter-optimization, .gemini/skills/parameter-optimization, .github/skills/parameter-optimization and .opencode/skills/parameter-optimization in your project.
What does Parameter Optimization need to run?
Going by SKILL.md and its folder, Parameter Optimization needs the command-line tools its instructions call (python3). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Grep, Glob.
Does Parameter Optimization access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Parameter Optimization safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Parameter Optimization use?
Parameter Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Parameter Optimization use?
About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Parameter Optimization?
Skills that share tags, products or a category with Parameter Optimization: Wp Performance Review (elvismdev/claude-wordpress-skills, 234 stars), Align Human (agentscope-ai/OpenJudge, 867 stars), Performance Report (Affitor/affiliate-skills, 698 stars) and Run Mv Hoi Reconstruction (nvidia-isaac/video_to_data, 847 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Parameter Optimization?
majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 971 skills in this directory. The repository was last updated on October 7, 2026.
Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.