platform/plugin-development/playground/skills/playground/SKILL.md
Creates interactive HTML playgrounds — self-contained single-file explorers that let users configure something visually through controls, see a live preview, and copy out a prompt. Use when the user asks to make a playground, explorer, or interactive tool for a topic.
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library playgroundInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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A playground is a self-contained HTML file with interactive controls on one side, a live preview on the other, and a prompt output at the bottom with a copy button. The user adjusts controls, explores visually, then copies the generated prompt back into Claude.
When the user asks for an interactive playground, explorer, or visual tool for a topic — especially when the input space is large, visual, or structural and hard to express as plain text.
templates/:
templates/design-playground.md — Visual design decisions (components, layouts, spacing, color, typography)templates/data-explorer.md — Data and query building (SQL, APIs, pipelines, regex)templates/concept-map.md — Learning and exploration (concept maps, knowledge gaps, scope mapping)templates/document-critique.md — Document review (suggestions with approve/reject/comment workflow)templates/diff-review.md — Code review (git diffs, commits, PRs with line-by-line commenting)templates/code-map.md — Codebase architecture (component relationships, data flow, layer diagrams)open <filename>.html to launch it in the user's default browser.Keep a single state object. Every control writes to it, every render reads from it.
const state = { /* all configurable values */ };
function updateAll() {
renderPreview(); // update the visual
updatePrompt(); // rebuild the prompt text
}
// Every control calls updateAll() on change
function updatePrompt() {
const parts = [];
// Only mention non-default values
if (state.borderRadius !== DEFAULTS.borderRadius) {
parts.push(`border-radius of ${state.borderRadius}px`);
}
// Use qualitative language alongside numbers
if (state.shadowBlur > 16) parts.push('a pronounced shadow');
else if (state.shadowBlur > 0) parts.push('a subtle shadow');
prompt.textContent = `Update the card to use ${parts.join(', ')}.`;
}
testing
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
tools
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
testing
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
development
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.