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diagram-reports-generatordiagram reports 生成器

Agent Skill

diagram-reports-generator 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

720

周安装

30

GitHub Stars

65

下载量

240
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:diagram-reports-generator(diagram reports 生成器)
来源仓库:https://github.com/dmccreary/claude-skills
仓库路径:skills/diagram-reports-generator
安装命令:
npx skills add https://github.com/dmccreary/claude-skills --skill diagram-reports-generator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/dmccreary/claude-skills --skill diagram-reports-generator

简介

diagram-reports-generator 自动生成几何课程中所有图表和 MicroSims 的综合报告,支持章节级详细视图与表格概览。

  • 适用于教材项目中的图表审计、实现状态跟踪和复杂度分布分析。
  • 输出按章节组织的表格视图和详细视图,便于教学资源管理与更新。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Diagram Reports Generator

Overview

This skill automatically generates comprehensive reports of all diagrams and MicroSims in the geometry course by analyzing chapter markdown files. It creates two report files: a table view for quick reference and a detailed view organized by chapter.

When to Use This Skill

Use this skill when:

  • Working with the geometry-course intelligent textbook project
  • Needing to audit all diagrams and MicroSims across chapters
  • Tracking implementation status of visual elements
  • Analyzing complexity and Bloom's Taxonomy distribution
  • Updating documentation after adding new diagrams or MicroSims
  • Generating reports for instructors or content creators

Workflow

Step 1: Install the Diagram Report Generator Script

If the project does not already have the diagram report generator script, install it from the skill's bundled resources:

  1. Check if src/diagram-reports/diagram-report.py exists in the project
  2. If it doesn't exist:

- Create the directory: mkdir -p src/diagram-reports - Copy the script from this skill's scripts/diagram-report.py to src/diagram-reports/diagram-report.py

  1. If it already exists, verify it's up to date (optionally show the user a diff if there are differences)

Example installation:

# Create directory if needed
mkdir -p src/diagram-reports

# Copy script from skill (use Read tool to get script content, then Write tool to create file)
# The script is located in this skill's scripts/diagram-report.py

Step 2: Verify Project Structure

Before running the report generator, verify the project structure:

  1. Confirm the current working directory is the geometry-course root (or any intelligent textbook project)
  2. Verify docs/chapters/ contains chapter directories (format: 01-chapter-name, 02-chapter-name, etc.)
  3. Ensure docs/learning-graph/ directory exists for output

Step 3: Run the Report Generator

Execute the Python script to generate the reports:

python src/diagram-reports/diagram-report.py

The script will:

  • Scan all chapter directories in docs/chapters/
  • Parse each chapter's index.md file
  • Extract diagram and MicroSim specifications from <details> blocks with "#### Diagram:" headers
  • Analyze each element for:

- Type (diagram or MicroSim) - Status (implementation status if specified) - Bloom's Taxonomy levels - UI element count - Estimated difficulty (Easy, Medium, Hard, Very Hard) - Learning objectives

Step 4: Verify Generated Reports

After running the script, verify two files were created in docs/learning-graph/:

  1. diagram-table.md - A sortable table view with columns:

- Chapter number - Element title (with links to chapter sections) - Status - Type (Diagram/MicroSim) - Bloom's Taxonomy levels - UI elements count - Difficulty rating

  1. diagram-details.md - A detailed view organized by chapter with:

- Chapter-level summaries - Full element descriptions - Learning objectives - Linked titles to source chapter sections

Step 5: Review Navigation Configuration

The generated reports should already be linked in the MkDocs navigation. Verify the mkdocs.yml file contains these entries under the "Learning Graph" section:

- Learning Graph:
    - Diagrams Table: learning-graph/diagram-table.md
    - Diagrams Details: learning-graph/diagram-details.md

If these entries are missing, add them to the navigation structure.

Step 6: Preview the Reports

To view the generated reports:

  1. Run mkdocs serve to start the local development server
  2. Navigate to the "Learning Graph" section
  3. Click on "Diagrams Table" or "Diagrams Details"
  4. Verify all diagrams and MicroSims are properly listed with accurate information

Understanding Report Output

Difficulty Estimation

The script estimates difficulty based on:

  • Element Type: MicroSims start with higher base difficulty
  • UI Complexity: Number of sliders, buttons, dropdowns, and other controls
  • Features: Animation, rotation, 3D/isometric views, real-time calculations
  • Canvas Size: Larger canvases increase complexity

Difficulty levels:

  • Easy: Static diagrams or simple visualizations
  • Medium: Basic interactivity with 1-3 UI elements
  • Hard: Moderate interactivity with 4-6 UI elements or complex features
  • Very Hard: High interactivity with many UI elements or advanced features

Bloom's Taxonomy Detection

The script automatically detects Bloom's Taxonomy levels mentioned in specifications:

  • Remembering
  • Understanding
  • Applying
  • Analyzing
  • Evaluating
  • Creating

Troubleshooting

No Elements Found

If the report shows zero elements:

  • Verify chapter markdown files contain #### Diagram: headers followed by <details> blocks
  • Check that <details> blocks include specification content
  • Run with verbose flag: python src/diagram-reports/diagram-report.py --verbose

Missing Information

If elements are missing type, status, or other fields:

  • Review the <details> block format in chapter files
  • Ensure specifications include **Type:**, **Status:**, and **Learning Objective:** fields
  • The script will infer type from content if not explicitly specified

Broken Links in Reports

If chapter links don't work:

  • Verify chapter directory naming follows the pattern: ##-descriptive-name
  • Check that MkDocs anchor generation matches the script's anchor creation logic
  • Test links by navigating in the served site

Advanced Usage

Custom Output Location

Specify a different output directory:

python src/diagram-reports/diagram-report.py --output-dir /path/to/output

Generate CSV Format

For spreadsheet analysis:

python src/diagram-reports/diagram-report.py --format csv

Generate HTML Format

For standalone HTML reports:

python src/diagram-reports/diagram-report.py --format html

Verbose Output for Debugging

Enable detailed logging:

python src/diagram-reports/diagram-report.py --verbose

Integration with Intelligent Textbook Workflow

This skill integrates with the broader intelligent textbook creation workflow:

  1. After content creation: Run this skill after generating or updating chapter content
  2. Before review sessions: Generate reports to identify gaps or inconsistencies
  3. During planning: Use difficulty distribution to balance implementation effort
  4. For documentation: Include reports in instructor guides or project documentation

Bundled Resources

scripts/diagram-report.py

This skill includes the complete Python script for generating diagram and MicroSim reports. The script will be installed into the user's project at src/diagram-reports/diagram-report.py when the skill is first used.

The script:

  • Analyzes all chapter markdown files in docs/chapters/
  • Extracts diagram and MicroSim specifications from <details> blocks
  • Calculates difficulty estimates based on UI complexity and features
  • Detects Bloom's Taxonomy levels from specification text
  • Generates both table and detailed report formats
  • Supports multiple output formats (markdown, CSV, HTML)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

26.24%
按下载量换算63

windsurf

23.33%
按下载量换算56

Claude Code

17.12%
按下载量换算41

OpenCode

10.97%
按下载量换算26

Gemini CLI

8.04%
按下载量换算19

Antigravity

3.42%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

来源信息

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