Data & Analytics

This document defines the **standard JSON format** for learning graphs compatible with the **vis.js Network library**. Use this format when generating learning graphs to ensure proper visualization.

This analysis uses data from the CIA World Factbook. Please note: - Data may be 1-3 years out of date and updated on irregular schedules - Figures may vary from other sources (World Bank, IMF, UN) - Statistics for disputed territories may reflect geopolitical perspectives - Economic and demographic data should be cross-referenced for critical decisions **This data is provided for educational and research purposes only.**

**Document Type**: Architecture & Technical - Architecture (AT-ARCH) **Document ID**: 6767-g-AT-ARCH-skill-scaffold-diagrams **Title**: Skill Scaffold Diagrams (Enforceable) **Version**: 3.0.0 **Status**: CANONICAL (Enterprise-Only) **Date**: 2025-12-20 **Companion To**: 6767-c (Extensions Standard), 6767-d (Schema), 6767-e (Validation), 6767-f (Plugin Diagrams) **Authority**: Intent Solutions (Enterprise Marketplace) **Normative Language**: This document uses MUST / SHOULD / MAY ---

**Document Type**: Architecture & Technical - Architecture (AT-ARCH) **Document ID**: 6767-f-AT-ARCH-plugin-scaffold-diagrams **Title**: Plugin Scaffold Diagrams (Enforceable) **Version**: 3.0.0 **Status**: CANONICAL (Enterprise-Only) **Date**: 2025-12-20 **Companion To**: 6767-c (Extensions Standard), 6767-d (Schema), 6767-e (Validation) **Authority**: Intent Solutions (Enterprise Marketplace) **Normative Language**: This document uses MUST / SHOULD / MAY ---

**Document Type**: Developer Resource - Standard (DR-STND) **Document ID**: 6767-c-DR-STND-claude-code-extensions-standard **Title**: Claude Code Extensions Standard (Unified) **Version**: 3.0.0 **Status**: CANONICAL (Enterprise-Only) **Date**: 2025-12-20 **Supersedes**: 6767-a (plugins), 6767-b (skills) **Superseded By**: 6767-h (master spec) **Authority**: Intent Solutions (Enterprise Marketplace) ---

**Date:** October 11, 2025 **Location:** `/home/jeremy/projects/claude-code-plugins/plugins/ai-ml/` **Status:** 25/25 Plugins Created - MISSION COMPLETE

The vis-network skill creates educational MicroSims using the vis-network JavaScript library for interactive network and graph visualizations. Each MicroSim is a directory in `/docs/sims/` with a main.html file that can be embedded via iframe in educational content.

Generates comprehensive exploratory data analysis reports using ydata-profiling, a battle-tested library with 12.5k+ GitHub stars.

Claude Code v2.0.36 Sonnet 4.5 · Claude Pro !!! prompt Please review all the skills in the skills directory. Then update the docs/skill-descriptions documentation. Make sure that the file docs/skill-descriptions/index.md has a single sentence description and links to a full-page description file in the docs/skill-descriptions directory. ⏺ I'll review all the skills and update the documentation. Let me start by exploring the skills directory and the current documentation structure. ⏺ Bash(ls...

Unsupervised learning discovers patterns in unlabeled data through clustering, dimensionality reduction, and density estimation.

UITree is a flat JSON structure representing a component hierarchy. It's designed for LLM generation—flat structure avoids deep nesting issues with streaming and parsing.

Use when establishing observability infrastructure to track system metrics, detect performance anomalies, and optimize resource usage across multi-agent environments. Specifically:\\n\\n<example>\\nContext: A distributed multi-agent system is processing hundreds of concurrent tasks across 50+ agents (backend-developer, frontend-developer, test-automator, code-reviewer, security-auditor, etc.). There is no current monitoring visibility, making it impossible to identify which components are slo...

Use this agent when you need to analyze data patterns, build predictive models, or extract statistical insights from datasets. Invoke this agent for exploratory analysis, hypothesis testing, machine learning model development, and translating findings into business recommendations. Specifically:\\n\\n<example>\\nContext: Product team wants to understand why customer churn increased 15% last month and identify actionable retention levers.\\nuser: \"We're seeing higher churn recently. Can you a...

Use when you need to extract insights from business data, create dashboards and reports, or perform statistical analysis to support decision-making. Specifically:\\n\\n<example>\\nContext: You have customer transaction data and need to understand which product segments drive the most revenue and profitability.\\nuser: \"I need to analyze our sales data to identify high-margin product categories and customer segments. We have SQL access to our warehouse and want actionable insights.\"\\nassist...

**Cause:** Bot Management didn't run (internal Cloudflare request, Worker routing to zone (Orange-to-Orange), or request handled before BM (Redirect Rules, etc.)) **Solution:** Check request flow and ensure Bot Management runs in request lifecycle

This document provides a comprehensive reference for all deepTools command-line utilities organized by category.

This document outlines the standard workflow for analyzing single-cell RNA-seq data using scanpy.

This skill generates interactive Venn diagram visualizations using the venn.js JavaScript library. Use this skill when users request creating Venn diagrams, set visualizations, overlap diagrams, or comparison charts for educational textbooks. The skill creates complete MicroSim packages with standalone HTML files featuring colorful circles, clear labels, and interactive tooltips, saved to /docs/sims/ following the MicroSim pattern.

Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization.

Installs and configures project infrastructure including MkDocs Material intelligent textbook templates, learning graph viewers, and skill tracking systems. Routes to the appropriate installation guide based on what the user needs to set up.

scEmbed trains Region2Vec models on single-cell ATAC-seq datasets to generate cell embeddings for clustering and analysis. It provides an unsupervised machine learning framework for representing and analyzing scATAC-seq data in low-dimensional space.

```php // routes/web.php use App\Http\Controllers\PostController; use Illuminate\Support\Facades\Route;

Test your understanding of concept enumeration, dependency mapping, CSV file formats, and taxonomy categorization with these questions. --- <div class="upper-alpha" markdown> 1. 50-75 concepts 2. 100-150 concepts 3. 180-220 concepts 4. 300-400 concepts </div> ??? question "Show Answer" The correct answer is **C**. A semester-length course typically targets approximately 200 concepts (range 180-220), which aligns with cognitive load principles, provides adequate assessment coverage, and fits s...

Test your understanding of taxonomy categorization, vis-network JSON format, Dublin Core metadata, and Python processing scripts with these questions. --- <div class="upper-alpha" markdown> 1. 1-2 letters for brevity 2. 3-5 letters for balance 3. 6-10 letters for clarity 4. 15+ letters for full descriptiveness </div> ??? question "Show Answer" The correct answer is **B**. TaxonomyID abbreviations should be 3-5 letters, balancing compactness in CSV files and visualizations with sufficient dist...