Documentation

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1246 Decision Guide 0aebde35AUse this flowchart to select the appropriate matching algorithm. Answer each question in order. ---documentationgoexpressVotes: 0GitHub stars: 9
1228 Patterns D3b9d463A**Proven strategies that work** ---documentationpythongitVotes: 0GitHub stars: 9
116 Date 01312cb0A**Date:** 2025-12-17 **Project:** claude-skills/skills/installerdocumentationjavascriptjavaVotes: 0GitHub stars: 9
113 Interactive 22b68c7cAAn interactive infographic showing the five memory layers in Claude Code, how they load, and how higher-priority rules override lower-priority ones.documentationgitdocumentationVotes: 0GitHub stars: 9
113 Consolidated Cfbb172cAA consolidated installation skill that handles all project setup tasks including MkDocs Material template creation, learning graph viewer installation, skill usage tracking, and home page/cover image configuration. Routes to the appropriate installation guide based on your request. [Read full description](./book-installer.md) Create, validate, and score course descriptions following the 2001 Bloom's Taxonomy guidelines with quality assessment (1-100) and improvement suggestions. [Read full de...documentationgogitVotes: 0GitHub stars: 9
113 Chapter 444edbccAThis chapter focuses on validating and assessing the quality of your learning graph. You'll learn techniques for detecting circular dependencies and validating that your graph is a proper Directed Acyclic Graph (DAG). The chapter covers self-dependency checking and introduces comprehensive quality metrics including orphaned nodes, disconnected subgraphs, and linear chain detection.documentationpythongoVotes: 0GitHub stars: 9
113 Chapter 02b79e9bAThis chapter explores how to add taxonomy information to your learning graph and convert it to various formats for visualization and processing. You'll learn about the TaxonomyID field in CSV files and the process of adding taxonomy categorization to existing concept graphs. The chapter provides comprehensive coverage of the vis-network JSON format, including its schema structure with metadata, groups, nodes, and edges sections.documentationjavascriptpythonVotes: 0GitHub stars: 9
1099 Transformation Patterns E383b8f7A1. [Overview](#overview) 2. [Pattern 1: Wide Format → Long Format (Unpivot)](#pattern-1-wide-format--long-format-unpivot) 3. [Pattern 2: Multi-Row Headers → Single Row Headers](#pattern-2-multi-row-headers--single-row-headers) 4. [Pattern 3: Blank Cells → Filled Data](#pattern-3-blank-cells--filled-data) 5. [Pattern 4: Group Headers → Individual Rows](#pattern-4-group-headers--individual-rows) 6. [Pattern 5: Pivot Column](#pattern-5-pivot-column) 7. [Pattern 6: Date Locale Fixes](#pattern-6-datedocumentationgoVotes: 0GitHub stars: 9
1093 Z Image Turbo 261dff90A| Aspect | Detail | |--------|--------| | **Provider** | Tongyi-MAI (Alibaba) | | **Parameters** | 6B | | **Pricing** | $0.005/megapixel (cheapest) | | **Speed** | Sub-second generation | | **License** | Open Source |documentationgoapiVotes: 0GitHub stars: 9
1092 Enhancement Log Ee41afddA**Trigger:** /skill-enhance art-director **Score:** 18/25 → 21/25 **Key changes:** - Added concrete prompt walkthroughs (portrait + product) with user input → thought process → final prompt → WHY IT WORKS - Expanded REVIEW step from 1-line to 6-item self-check checklist - Added ENHANCE step with 3 concrete elevation techniques - Added Iteration Guide table (8 common problems with diagnosis and fixes) - Added Critique Walkthrough example showing GOAL → ANALYZE → PRESCRIBE in action **Research:** documentationgoVotes: 0GitHub stars: 9
108 Plotly 3cb5cbe5AThe `plotly.graph_objects` module provides fine-grained control over figure construction through Python classes representing Plotly components.documentationpythongoVotes: 0GitHub stars: 9
101 Date 7d41dd0dA**Date:** 2025-11-17 **Task:** Autogenerate all diagrams where Difficulty Level = Medium and MicroSim match score > 90documentationpythongoVotes: 0GitHub stars: 9
097 Index Ba923f8eAThis chapter explores how to add taxonomy information to your learning graph and convert it to various formats for visualization and processing. You'll learn about the TaxonomyID field in CSV files and the process of adding taxonomy categorization to existing concept graphs. The chapter provides comprehensive coverage of the vis-network JSON format, including its schema structure with metadata, groups, nodes, and edges sections.documentationjavascriptpythonVotes: 0GitHub stars: 9
097 Index Ab3c4104AThis chapter explores how to add taxonomy information to your learning graph and convert it to various formats for visualization and processing. You'll learn about the TaxonomyID field in CSV files and the process of adding taxonomy categorization to existing concept graphs. The chapter provides comprehensive coverage of the vis-network JSON format, including its schema structure with metadata, groups, nodes, and edges sections.documentationjavascriptpythonVotes: 0GitHub stars: 9
097 Index 7f41bf05AThis chapter focuses on validating and assessing the quality of your learning graph. You'll learn techniques for detecting circular dependencies and validating that your graph is a proper Directed Acyclic Graph (DAG). The chapter covers self-dependency checking and introduces comprehensive quality metrics including orphaned nodes, disconnected subgraphs, and linear chain detection.documentationpythongoVotes: 0GitHub stars: 9
097 Index 720a47adAThis chapter focuses on validating and assessing the quality of your learning graph. You'll learn techniques for detecting circular dependencies and validating that your graph is a proper Directed Acyclic Graph (DAG). The chapter covers self-dependency checking and introduces comprehensive quality metrics including orphaned nodes, disconnected subgraphs, and linear chain detection.documentationpythongoVotes: 0GitHub stars: 9
089 Skill De1cfbbaAThis skill generates a comprehensive set of Frequently Asked Questions (FAQs) from course content, learning graphs, and glossary terms to help students understand common questions and prepare content for chatbot integration.documentationgonodeVotes: 0GitHub stars: 9
082 Standardized Fb0eb772AStandardized conventions for Salesforce data model diagrams with object type indicators, LDV markers, OWD annotations, and relationship type labels.documentationpythongoVotes: 0GitHub stars: 9
082 Claude 58bdf517A**Only create git tags and GitHub releases when skill or plugin files change.**documentationgitdocumentationVotes: 0GitHub stars: 9
082 Claude 3e6465f4AThis repository contains a Claude Code Marketplace Plugin providing Skills for Claude - modular packages that extend Claude's capabilities with specialized knowledge, workflows, and tools. **Contributing**: When adding, removing, or updating plugins in this repository, follow the procedures documented in [CONTRIBUTING.md](./CONTRIBUTING.md). The model MUST update `.claude-plugin/marketplace.json` when plugins are added or removed. ---documentationjavascriptpythonVotes: 0GitHub stars: 9
082 Claude 3cbfd029AThis repository contains a Claude Code Marketplace Plugin providing Skills for Claude - modular packages that extend Claude's capabilities with specialized knowledge, workflows, and tools. **Contributing**: When adding, removing, or updating plugins in this repository, follow the procedures documented in [CONTRIBUTING.md](./CONTRIBUTING.md). The model MUST update `.claude-plugin/marketplace.json` when plugins are added or removed. ---documentationjavascriptpythonVotes: 0GitHub stars: 9
073 Total 9940b3b3A**Total Visual Elements:** 76 **Diagrams:** 0 **MicroSims:** 76documentationpythongoVotes: 0GitHub stars: 9
061 Architecture Da2f6f3eATailwind v4 requires a specific architecture for CSS variable-based theming. This pattern is **mandatory** - skipping or modifying steps will break your theme.documentationtypescriptdocumentationVotes: 0GitHub stars: 9
056 Chapter 2cb725e9A**Chapter:** 06 - Learning Graph Quality Validation **Generator:** vis-network **Match Score:** 98/100 **Difficulty:** MediumdocumentationgonodeVotes: 0GitHub stars: 9