Claude Skills by tools-only

- **Total Concepts**: 200 - **Foundational Concepts** (no dependencies): 8 - **Concepts with Dependencies**: 192 - **Average Dependencies per Concept**: 1.18

```bash bamCoverage --bam input.bam --outFileName output.bw \ --normalizeUsing RPGC --effectiveGenomeSize 2913022398 \ --binSize 10 --numberOfProcessors 8 ```

Test your understanding of FAQ generation, quiz creation, Bloom's Taxonomy in assessments, command-line interfaces, and taxonomy analysis scripts with these questions. --- <div class="upper-alpha" markdown> 1. To provide supplementary reference material like traditional appendices 2. To anticipate and address predictable student confusion patterns before they arise 3. To list all possible questions students might encounter in the course 4. To replace primary instruction with question-answer p...

Test your understanding of learning graphs, concept nodes, dependency edges, and Claude usage optimization with these questions. --- <div class="upper-alpha" markdown> 1. To create visual diagrams for textbook covers 2. To map conceptual landscape with prerequisite dependencies explicitly 3. To track student progress through course material 4. To organize bibliography references by topic </div> ??? question "Show Answer" The correct answer is **B**. A learning graph is a directed graph data s...

Test your understanding of DAG validation, quality metrics, circular dependency detection, and taxonomy distribution analysis with these questions. --- <div class="upper-alpha" markdown> 1. O(V) where V is the number of vertices 2. O(V + E) where V is vertices and E is edges 3. O(V²) for all possible vertex pairs 4. O(E log E) where E is the number of edges </div> ??? question "Show Answer" The correct answer is **B**. The depth-first search (DFS) algorithm with cycle detection runs in O(V + ...

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...

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...

**Date:** 2026-02-08 **Branch:** feature/universal-infrastructure-strategy **Status:** ✅ READY FOR PRODUCTION ---

**Date:** 2026-02-08 **Branch:** feature/universal-infrastructure-strategy **Status:** ✅ COMPLETED ---

**Date:** February 7, 2026 **Branch:** `feature/universal-infrastructure-strategy` **Status:** ✅ **PRODUCTION READY** ---

**Start Date:** February 2, 2026 **Timeline:** 4 weeks **Strategy:** Hybrid approach combining RAG ecosystem + AI coding tools **Status:** ✅ Ready to Execute ---

**Start Date:** February 2, 2026 **Timeline:** 4 weeks **Strategy:** Hybrid approach combining RAG ecosystem + AI coding tools **Status:** ✅ Ready to Execute ---

Generated: 2025-11-08

This skill installs an interactive learning graph viewer application into an intelligent textbook project. Use this skill when working with a textbook that has a learning-graph.json file and needs a visual, interactive graph exploration tool with search, filtering, and statistics capabilities.

This skill generates interactive multiple-choice quizzes for each chapter of an intelligent textbook, with questions aligned to specific concepts from the learning graph and distributed across Bloom's Taxonomy cognitive levels to assess student understanding effectively.

Runs create_changes.py script to extract commit information and categorize changes

llm = OpenAICompatible.load_from_json_file("provider.json")["your_provider"]["model"] @llm_function(llm_interface=llm) async def classify_sentiment(text: str) -> str: """ 分析文本的情感倾向。 Args: text: 要分析的文本 Returns: 情感分类，可为 'positive', 'negative', 或 'neutral' """ pass # Prompt as Code! async def main(): result = await classify_sentiment("这个产品太棒了！") print(f"情感分类: {result}") asyncio.run(main()) ```

This reference provides guidance on adjusting textbook content for different reading levels based on grade level or educational context.

SimpleLLMFunc 的工具系统为大语言模型提供了调用外部函数和 API 的能力，让 LLM 能够执行计算、查询数据、调用服务等操作。工具系统支持两种创建方式，并能自动将 Python 函数转换为 LLM 可理解的工具描述格式。

The readme-generator skill creates or updates comprehensive README.md files for GitHub repositories following best practices. It generates all essential sections including badges, project overview, site metrics, and getting started instructions.

> Reference for: Common Ground > Load when: Using --graph flag, generating mermaid diagrams ---

Quick reference for using the `scientific_report.sty` style package.

If a user tells you there is a problem with the project, review the `target/run_results.json` file to identify which resources failed and why.

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

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.

This skill automates the capture of high-quality screenshots for MicroSim visualizations using Chrome headless mode. Use this skill when working with MicroSims that need preview images for social media sharing, documentation, or quality assessment. The skill handles JavaScript-heavy visualizations that require proper rendering time and external CDN resources.

Analyze shell scripts with ShellCheck and present findings.

Series endpoints provide access to economic data series metadata and observations.

**Date:** 2025-11-17 **Task:** Autogenerate all diagrams where Difficulty Level = Medium and MicroSim match score > 90 **Progress:** 18/18 diagrams complete (100%) ✓

Audits C/C++/Rust code for missing zeroization and compiler-removed wipes. Pipeline: source scan -> MCP/LSP semantic context -> IR diff -> assembly checks.

This skill generates a structured chapter outline for intelligent textbooks by analyzing course descriptions, learning graphs, and concept dependencies. Use this skill after the learning graph has been created and before generating chapter content, to design an optimal chapter structure that respects concept dependencies and distributes content evenly across 6-20 chapters.

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.

This skill generates a comprehensive set of Frequently Asked Questions (FAQs) from the course description, course content, learning graphs, concept lists, MicroSims, and glossary terms to help students understand common questions and prepare content for chatbot integration. Use this skill after course description, learning graph, glossary, and at least 30% of chapter content exist.

Add `flowctl epic add-dep` and `flowctl epic rm-dep` commands to manage epic-level dependencies via CLI.

Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.

Generates a comprehensive learning graph from a course description, including 200 concepts with dependencies, taxonomy categorization, and quality validation reports. Use this when the user wants to create a structured knowledge graph for educational content.

This skill generates interactive mathematical function plots using Plotly.js for iframe embedding in intelligent textbooks. Creates visualizations with hover tooltips, interactive sliders to move points along curves, responsive design optimized for narrow layouts, and comprehensive educational documentation. Use this when users request plotting mathematical functions, graphing equations, visualizing f(x), or creating interactive function explorers for calculus, precalculus, physics, or engine...

Creates interactive educational MicroSims using the best-matched JavaScript library (p5.js, Chart.js, Plotly, Mermaid, vis-network, vis-timeline, Leaflet, Venn.js). Analyzes user requirements to route to the appropriate visualization type and generates complete MicroSim packages with HTML, JavaScript, CSS, documentation, and metadata.

This skill analyzes diagram, chart, or simulation specifications and returns a ranked list of the most suitable MicroSim generator skills to use. It compares the specification against capabilities of all available microsim generators (p5.js, ChartJS, Plotly, Mermaid, vis-network, timeline, map, Venn, bubble) and provides match scores (0-100) with detailed reasoning for each recommendation. Use this skill when a user has a diagram specification and needs guidance on which MicroSim generator sk...

This skill automates the capture of high-quality screenshots for MicroSim visualizations using Chrome headless mode. Use this skill when working with MicroSims that need preview images for social media sharing, documentation, or quality assessment. The skill handles JavaScript-heavy visualizations that require proper rendering time and external CDN resources.

This skill generates interactive workflow diagrams using the Mermaid JavaScript library

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.

This skill generates interactive multiple-choice quizzes for each chapter of an intelligent textbook, with questions aligned to specific concepts from the learning graph and distributed across Bloom's Taxonomy cognitive levels to assess student understanding effectively. Use this skill after chapter content has been written and the learning graph exists.

Creates professional TikZ flowcharts with a standardized style (Google Material-like colors, node shapes, and layout).

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.

**Date:** December 10, 2025 **Project:** Claude Code Skills **Goal:** Consolidate 36+ skills to under 30 (Claude Code hard limit) ---

Add infrastructure for epic completion review status tracking.

**Chapter:** 09 - Claude Skills Architecture Development **Generator:** mermaid-generator **Match Score:** 93/100 **Difficulty:** Medium

Update documentation to cover epic-completion review feature.

Create run discovery library to find and manage Ralph runs.