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Claude Skills by tools-only
github.com/tools-only19,166 skills18 installs6,168 views
- 097 Index 225e3516This chapter provides an in-depth exploration of Claude Skills architecture and best practices for skill development. You'll learn about skill directory structure and how to organize supporting assets including Python scripts, template files, and reference documentation. The chapter covers skill testing and debugging techniques, error analysis, and strategies for improving skill quality over time.Votes: 0GitHub stars: 9
- 097 Index 2e7dc709Metadata hooks provide mechanisms for dynamically injecting and modifying project metadata during the build process. Hatchling includes built-in hooks and supports custom implementations for advanced use cases.Votes: 0GitHub stars: 9
- 097 Index 35cf6f30This final chapter brings together all the tools and techniques needed to complete and deploy your intelligent textbook project. You'll learn to use Visual Studio Code effectively for content development, including working with the integrated terminal. The chapter covers Bash shell scripting, script execution permissions, and essential command-line operations including directory navigation, file creation and editing, and symlink creation for skill installation.Votes: 0GitHub stars: 9
- 097 Index 3847a74cThis chapter teaches you how to enumerate concepts for your learning graph and map their dependencies. You'll learn the process of generating approximately 200 concepts from a course description, following specific requirements for concept labels including Title Case convention and maximum character length. The chapter emphasizes the importance of concept granularity and creating atomic concepts that represent single, clear ideas.Votes: 0GitHub stars: 9
- 097 Index 3a160ccc| Question | File | Mode | |----------|------|------| | What colors/tokens should I use? | `design-system.md`, `overwatch-design-system.md` | Blueprint, Overwatch | | How do I animate elements? | `animation-patterns.md`, `overwatch-interactions.md` | Blueprint, Overwatch | | What components exist? | `component-patterns.md`, `overwatch-interactions.md` | Blueprint, Overwatch | | How do I add WebGPU shaders? | `overwatch-shaders.md` | Overwatch | | What slide templates are available? | `slide-tempVotes: 0GitHub stars: 9
- 097 Index 433e95bbThis chapter provides an in-depth exploration of Claude Skills architecture and best practices for skill development. You'll learn about skill directory structure and how to organize supporting assets including Python scripts, template files, and reference documentation. The chapter covers skill testing and debugging techniques, error analysis, and strategies for improving skill quality over time.Votes: 0GitHub stars: 9
- 097 Index 5de3ab77Cisco Skill Scanner is a layered security analysis tool for AI agent skill packages. It combines deterministic pattern matching, behavioral analysis, and LLM-powered reasoning to catch threats that any single approach would miss. - **[Detect Threats](#multi-engine-threat-detection)** -- 10 analyzers, file intelligence engines, and analyzability scoring work together so no single blind spot dominates. - **[Tune and Control](#policy-driven-control-plane)** -- 3 presets, 14 policy sections, seve...Votes: 0GitHub stars: 9
- 097 Index 720a47adThis 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.Votes: 0GitHub stars: 9
- 097 Index 7f41bf05This 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.Votes: 0GitHub stars: 9
- 097 Index 800f7217This chapter focuses on the practical workflows for generating educational content for your intelligent textbook. You'll learn about chapter and section organization principles, exploring how to structure content in a logical, pedagogically sound manner. The chapter covers the content generation process using Claude Skills, including how to work with chapter index files and chapter concept lists.Votes: 0GitHub stars: 9
- 097 Index 8e680b85This chapter teaches you how to enumerate concepts for your learning graph and map their dependencies. You'll learn the process of generating approximately 200 concepts from a course description, following specific requirements for concept labels including Title Case convention and maximum character length. The chapter emphasizes the importance of concept granularity and creating atomic concepts that represent single, clear ideas.Votes: 0GitHub stars: 9
- 097 Index 974fad2aThis page tracks all significant design decisions made for ContextForge project, using the [ADR](https://adr.github.io/) format. | ID | Title | Status | Section | Date | |-------|----------------------------------------------------|-----------|----------------|-------------| | 0001 | Adopt **FastAPI** + **Pydantic V2** (Rust-core) | Accepted | Framework | 2025-02-01 | | 0002 | Use **Async SQLAlchemy** ORM with Connect...Votes: 0GitHub stars: 9
- 097 Index A4f43470Mermaid workflow for chapter content organization decisions with interactive hover detailsVotes: 0GitHub stars: 9
- 097 Index A7a1c466This chapter focuses on the practical workflows for generating educational content for your intelligent textbook. You'll learn about chapter and section organization principles, exploring how to structure content in a logical, pedagogically sound manner. The chapter covers the content generation process using Claude Skills, including how to work with chapter index files and chapter concept lists.Votes: 0GitHub stars: 9
- 097 Index Ab3c4104This 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.Votes: 0GitHub stars: 9
- 097 Index B995839dThis chapter explores how to create supplementary educational resources that enhance student learning and assess understanding. You'll learn the FAQ generation process, including how to identify common student questions and generate FAQs from course content. The chapter provides comprehensive coverage of quiz creation, including multiple-choice question design, quiz alignment with learning graph concepts, and Bloom's Taxonomy integration in assessments.Votes: 0GitHub stars: 9
- 097 Index Ba923f8eThis 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.Votes: 0GitHub stars: 9
- 097 Index Be768ee2This page tracks all significant design decisions made for the MCP Gateway project, using the [ADR](https://adr.github.io/) format. | ID | Title | Status | Section | Date | |-------|----------------------------------------------------|-----------|----------------|-------------| | 0001 | Adopt **FastAPI** + **Pydantic V2** (Rust-core) | Accepted | Framework | 2025-02-01 | | 0002 | Use **Async SQLAlchemy** ORM with Conn...Votes: 0GitHub stars: 9
- 097 Index C272f509This final chapter brings together all the tools and techniques needed to complete and deploy your intelligent textbook project. You'll learn to use Visual Studio Code effectively for content development, including working with the integrated terminal. The chapter covers Bash shell scripting, script execution permissions, and essential command-line operations including directory navigation, file creation and editing, and symlink creation for skill installation.Votes: 0GitHub stars: 9
- 097 Index F3cf4b3bThis chapter introduces the Claude Skills system, which is the foundation for automating intelligent textbook creation. You'll learn the structure of skill definition files, including YAML frontmatter, skill names, descriptions, licenses, and allowed tools. The chapter covers how to install skills, list available skills, and invoke them using slash commands. You'll also learn about Claude Commands and understand the important differences between skills and commands.Votes: 0GitHub stars: 9
- 097 Reference 58c292beThis reference covers general-purpose scientific data formats used across multiple disciplines.Votes: 0GitHub stars: 9
- 098 Date Dd14685e**Date:** 2025-01-08 **Chapter:** 12-interactive-elements-microsims **Skill Used:** chapter-content-generator **Status:** ✅ Completed Successfully ---Votes: 0GitHub stars: 9
- 099 Claude 94242796▗ ▗ ▖ ▖ Claude Code v2.0.36 Sonnet 4.5 · Claude Pro ▘▘ ▝▝ $HOME/Documents/ws/claude-skills > run the chapter-content-generator skill on chapters 6 and 7 ⏺ I'll run the chapter-content-generator skill to generate content for chapters 6 and 7. > The "chapter-content-generator" skill is running > The "chapter-content-generator" skill is running ⏺ I'll generate content for chapters 6 and 7. Let me start by finding and verifying these chapter files. ⏺ Bash(ls -la $HOME/Documents/ws/claude-sk...Votes: 0GitHub stars: 9
- 100 Now 1e29f2caI now need to understand more about the content inside all the details tags. I need to build a skill for each type of detail - infographic, network drawing etc. Can you create a new metrics report that categorizes these details components? This will help me prioritize what skills to build next. Ideally I would like a 2D chart that has impact on the vertical axis and effor on the horizontal axis. The effort would be how similar the new details are to my existing microsims. Perfect! Let me ...Votes: 0GitHub stars: 9
- 101 Date 7d41dd0d**Date:** 2025-11-17 **Task:** Autogenerate all diagrams where Difficulty Level = Medium and MicroSim match score > 90Votes: 0GitHub stars: 9
- 102 Claude 496d9513Claude Code v2.0.36 Sonnet 4.5 · Claude Pro !!! prompt Use the skill-generator skill to create a new skill. The skill name is 'timeline-generator'. Place this new skill in skills/timeline-generator. Use the timeline demo at docs/sims/timeline as your example. Prompt the user for the event list if it was not provided. Ask the user if they want category buttons in the viewer like the example. Make sure the sample code you put in skills/timeline-generator/resources is generic. ⏺ I'll help ...Votes: 0GitHub stars: 9
- 103 Total De5134c3**Total Candidates:** 18 **Filter Criteria:** Difficulty = Medium, First Recommendation Score > 90Votes: 0GitHub stars: 9
- 1037 Api Reference 18d13414The main class for converting files to Markdown.Votes: 0GitHub stars: 9
- 1037 Api Reference 1f9b3f94This document provides a comprehensive API reference for the Phase 3 Intelligent Learning System, including all classes, methods, and data structures introduced in the synergistic integration.Votes: 0GitHub stars: 9
- 104 Three 077e11c0Three.js geometry creation - built-in shapes, BufferGeometry, custom geometry, instancing. Use when creating 3D shapes, working with vertices, building custom meshes, or optimizing with instanced rendering.Votes: 0GitHub stars: 9
- 105 Core Bd572f13Core concepts for building your first 3D scene.Votes: 0GitHub stars: 9
- 106 Skill Fd7cf62eThis skill automatically generates a comprehensive glossary of terms from a learning graph's concept list, ensuring each definition follows ISO 11179 metadata registry standards (precise, concise, distinct, non-circular, and free of business rules).Votes: 0GitHub stars: 9
- 107 Networkx E0b719bbNetworkX supports four main graph classes:Votes: 0GitHub stars: 9
- 1078 Getting Started 02e04cc9**Data-Driven Construction: From Isolated Data to Automation** > "Predictions and forecasts based on historical data allow companies to make more accurate decisions about project costs and timelines." — Data-Driven Construction, Chapter 4.5 ---Votes: 0GitHub stars: 9
- 1078 Getting Started C7386cf9> **What is EvalView?** An open-source, pytest-style testing framework for AI agents that detects regressions when you change prompts, swap models, or update tools. Works with LangGraph, CrewAI, OpenAI Assistants, Anthropic Claude, HuggingFace, Ollama, and any HTTP API. EvalView is lightweight, YAML-first, and gets you testing AI agents in under 5 minutes. No database required, no vendor lock-in, works offline with Ollama.Votes: 0GitHub stars: 9
- 1078 Getting Started F07a70f1EvalView is a lightweight, YAML-first testing framework for AI agents. Get started in under 5 minutes.Votes: 0GitHub stars: 9
- 108 Plotly 3cb5cbe5The `plotly.graph_objects` module provides fine-grained control over figure construction through Python classes representing Plotly components.Votes: 0GitHub stars: 9
- 109 Plotting E47219851. [2D Plotting](#2d-plotting) 2. [3D Plotting](#3d-plotting) 3. [Specialized Plots](#specialized-plots) 4. [Figure Management](#figure-management) 5. [Customization](#customization) 6. [Exporting and Saving](#exporting-and-saving)Votes: 0GitHub stars: 9
- 1091 Security Audit 40f90ca4**Last Audited:** 2026-01-04 **Audited By:** Claude (Opus 4.5) + Human Review **Status:** Passed - No security issues found ---Votes: 0GitHub stars: 9
- 1092 Enhancement Log Ee41afdd**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:** Votes: 0GitHub stars: 9
- 1093 Z Image Turbo 261dff90| Aspect | Detail | |--------|--------| | **Provider** | Tongyi-MAI (Alibaba) | | **Parameters** | 6B | | **Pricing** | $0.005/megapixel (cheapest) | | **Speed** | Sub-second generation | | **License** | Open Source |Votes: 0GitHub stars: 9
- 1094 Skill Structure Guide B4996cbfThis guide helps organize extracted expertise into well-structured, usable Claude skills. It covers architecture patterns, file organization, and quality guidelines.Votes: 0GitHub stars: 9
- 1095 Pixie Api 8720bc2bQuick reference for creating custom geometric elements.Votes: 0GitHub stars: 9
- 1096 Presentation Design 33da5791**Slides support the speaker — they don't replace the speaker.**Votes: 0GitHub stars: 9
- 1097 Strategic Analysis F1a108e5**Use when:** Evaluating current position, strategic planning, decision making | Internal | | |----------|----------| | **Strengths** | **Weaknesses** | | What you do well | Where you can improve | | Resources you have | Resources you lack | | Competitive advantages | Competitive disadvantages | | External | | |----------|----------| | **Opportunities** | **Threats** | | Trends to leverage | Obstacles you face | | Market gaps | Competitor actions | | Emerging needs | RegulatoVotes: 0GitHub stars: 9
- 1098 Multi Row Headers 5eb7537a1. [When to Use](#when-to-use) 2. [Important Decision Framework](#important-decision-framework) 3. [Method 1: Separate Header + Append (Recommended)](#method-1-separate-header--append-recommended) 4. [Method 2: Transpose Method (Auto but Complex)](#method-2-transpose-method-auto-but-complex) 5. [Method Comparison](#method-comparison) 6. [Quick Decision Tree](#quick-decision-tree)Votes: 0GitHub stars: 9
- 1099 Transformation Patterns 037a152eDetailed dasel v3 patterns organized by use case. Each pattern follows the structure: problem, dasel command, expected result, caveats.Votes: 0GitHub stars: 9
- 1099 Transformation Patterns E383b8f71. [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-dateVotes: 0GitHub stars: 9
- 110 You Fec9c86bDo you already have an existing UI component to visualize some backend data? Or do you need some functionality currently not supported in *UI Agent* generic UI components. Or does LLM generated view not suits your needs and you want to have well-tuned view for that piece of data?Votes: 0GitHub stars: 9
- 1100 Advanced 8b673c79Advanced patterns and features for sophisticated skill systems. **View the complete example:** [advanced_usage.py](../examples/advanced_usage.py)Votes: 0GitHub stars: 9