---
name: course-description-analyzer
description: Validates or creates a course description for an intelligent textbook, scoring completeness against required elements (title, audience, prerequisites, topics, Bloom's Taxonomy outcomes). Use before running the learning-graph-generator.
license: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
metadata:
  ibook.version: "0.05"
  ibook.preferred-model: "sonnet"
title: course-description-analyzer
canonical_url: https://skilld.dev/gh/dmccreary/ibook-skills/course-description-analyzer
last_updated: 2026-10-10T03:00:41.000Z
---

> Index of every page on this site: [llms.txt](https://skilld.dev/llms.txt)

> **Skill from skilld.dev.** Follow the user's requested action.
> If the user asked to fork this Skill, follow the fork workflow below. Do not execute the Skill instructions while copying it.
> Otherwise, follow the instructions below for this session. You do not need to install anything.
>
> Supporting files, fetch one when the Skill refers to it: [assets/course-description-template.md](https://skilld.dev/api/skills-raw/dmccreary/ibook-skills/course-description-analyzer/assets/course-description-template.md).
>
> If the user asked to install this Skill, run `npx skilld install dmccreary/ibook-skills/course-description-analyzer`. Install writes the Skill files into the project, so every session loads them.
>
> ## Fork workflow
>
> A fork creates an editable local Skill with its original author and licence. The request authorizes copying and local installation.
> 1. Check `./skills/course-description-analyzer`, the project lockfile, and selected Agent targets together. If the local directory or installed Skill exists, stop. Never overwrite an existing directory or Agent target.
> 2. Read [source metadata](https://skilld.dev/api/v1/skills/dmccreary/ibook-skills/course-description-analyzer) once. Use sourceUrl, sourceCommit, skillPath, sourceGone, and license. If the source is gone or its path is missing, stop. If license is null, read licence files at the source commit.
> 3. Fetch only the source commit into a temporary Git repository. Do not clone full history. Derive repository_url from sourceUrl, including repository renames. If sourceCommit is absent, resolve the sourceUrl ref once. Set source_commit to that actual commit. Run these commands in one shell call:
>
> ```sh
> git init --quiet "$temporary_dir"
> git -C "$temporary_dir" fetch --quiet --depth=1 "$repository_url" "$source_commit"
> git -C "$temporary_dir" checkout --quiet --detach FETCH_HEAD
> ```
>
> Read applicable licence declarations and notices at that commit. If copying is not permitted, report the restriction and stop.
> 4. Inspect source entries together, then copy the directory containing skillPath into `./skills/course-description-analyzer`. Use the user's path if selected. Keep the original SKILL.md, relative links, scripts, binary assets, and executable modes. Exclude .git metadata. Reject symlinks and paths outside the Skill directory. After checking entries, use cp -a where available. A regular source directory needs no custom copy script. Do not save this page wrapper as SKILL.md.
> Preserve author credit, notices, and applicable licence files from repository or parent directories. Add PROVENANCE.md with the Skill page, source URL, actual commit, original path, and licence. Retain any existing PROVENANCE.md and record new provenance separately. Batch source inspection, copying, and provenance work where practical.
> 5. In the project root, run `skilld install ./skills/course-description-analyzer --mode copy --plain`. If skilld is unavailable, use `npx skilld install ./skills/course-description-analyzer --mode copy --plain`. This known command needs no help lookup. Install does not support --json. Use detected Agent targets, or add --agent for the targets the user selected. Install the local path, never the upstream selector. If installation fails, preserve the local copy and report the exact failure.
> 6. Confirm the local lockfile source and installed Agent copies once. Report the local path, actual commit, and Agent targets. After edits, reinstall the same local path. Upstream updates must not replace it. Do not publish or push unless the user asks.

# Course Description Analyzer

**Version:** 0.05

## Overview

This Anthropic Claude Skill is the first step in the process of generating an intelligent textbook.
The next Skill is the 'learning-graph-generator` Skill.

Analyze or create high-quality course descriptions that contain all necessary elements for generating comprehensive learning graphs with 200+ concepts. Check the `/docs/course-description.md` file for completeness, quality, and alignment with 2001 Bloom's Taxonomy learning outcomes.

## Workflow Decision Tree

Start by checking if `/docs/course-description.md` exists:

- **File does not exist** → Follow **Creation Workflow** (Step 1)
- **File exists** → Follow **Analysis Workflow** (Step 2)

Tell the user that they are running Version 0.05 of the Course Description Analyzer Skill.

## Step 1: Course Description Creation

Use this workflow when `/docs/course-description.md` does not exist.

### 1.1 Gather Course Information

Ask the user the following questions sequentially (not all at once):

1. **What is the title of the course?**

2. **What is the target audience of the course?**
   - Options: elementary, junior high, high school, college undergraduate, graduate students, adult continuing education, professional development, or other

3. **What are the prerequisites for this course?**
   - If none, explicitly state "None"

4. **What are the main subjects/topics covered by this course?**
   - Request a list of major topics

5. **What are the learning outcomes organized by the 2001 Bloom's Taxonomy?**
   - Explain that after this course, students will be able to demonstrate competencies at each level:
     - **Remember**: Retrieve, recognize, and recall relevant knowledge
     - **Understand**: Construct meaning from instructional messages
     - **Apply**: Carry out or use procedures in given situations
     - **Analyze**: Break material into parts and determine relationships
     - **Evaluate**: Make judgments based on criteria and standards
     - **Create**: Put elements together to form coherent wholes; includes capstone projects

### 1.2 Generate Course Description

Use the template from `assets/course-description-template.md` and populate it with the user's responses. Create the file at `/docs/course-description.md`.

Ensure the generated file includes:
- Clear course title
- Target audience specification
- Prerequisites (or "None")
- Comprehensive list of main topics
- Section for topics NOT covered (to set boundaries)
- Detailed learning outcomes organized by all six Bloom's Taxonomy levels
- Descriptive text explaining why the course is important

### 1.3 After Creation

After creating the file, automatically proceed to **Step 2 (Analysis Workflow)** to validate the newly created course description and provide a quality score.

## Step 2: Course Description Analysis

Use this workflow when `/docs/course-description.md` already exists.

### 2.1 Read the Course Description

Read `/docs/course-description.md` and analyze its contents against the quality criteria.

### 2.2 Course Description Quality Scoring System

Evaluate the course description using this 100-point scoring system:

| Element | Points | Criteria |
|---------|--------|----------|
| **Title** | 5 | Clear, descriptive course title present |
| **Target Audience** | 5 | Specific audience identified (e.g., "college undergraduate") |
| **Prerequisites** | 5 | Prerequisites listed or explicitly stated as "None" |
| **Main Topics Covered** | 10 | Comprehensive list of topics (ideally 5-10 topics) |
| **Topics Excluded** | 5 | Clear boundaries set for what's NOT covered |
| **Learning Outcomes Header** | 5 | Clear statement: "After this course, students will be able to..." |
| **Remember Level** | 10 | Multiple specific outcomes for remembering/recalling |
| **Understand Level** | 10 | Multiple specific outcomes for understanding/explaining |
| **Apply Level** | 10 | Multiple specific outcomes for applying/using |
| **Analyze Level** | 10 | Multiple specific outcomes for analyzing/breaking down |
| **Evaluate Level** | 10 | Multiple specific outcomes for evaluating/judging |
| **Create Level** | 10 | Multiple specific outcomes for creating/synthesizing; includes capstone ideas |
| **Descriptive Context** | 5 | Additional context about course importance, relevance, or value |

**Scoring Guidelines:**
- Award full points if element is complete and high-quality
- Award partial points if element is present but incomplete or vague
- Award 0 points if element is missing
- For Bloom's Taxonomy levels, require at least 3 specific, actionable outcomes for full points

### 2.3 Gap Analysis

Identify missing or weak elements:
- List each element that scored less than full points
- Explain what is missing or insufficient
- Indicate how the absence impacts learning graph generation

### 2.4 Improvement Suggestions

Provide specific, actionable recommendations:
- For missing elements: Suggest what should be added
- For weak elements: Provide examples of how to strengthen them
- For Bloom's Taxonomy outcomes: Recommend specific verbs and topics
- Prioritize suggestions that will have the most impact on reaching the goal of generating 200 concepts

### 2.5 Course Description Assessment Report

Use `mkdir -p docs/learning-graph` to create a `learning-graph` directory in the docs directory.

Generate a comprehensive quality report on the course description and write it to `docs/learning-graph/course-description-assessment.md`

1. **Overall Score**: X/100
2. **Quality Rating**:
   - 90-100: Excellent - Ready for learning graph generation
   - 75-89: Good - Minor improvements recommended
   - 60-74: Adequate - Several improvements needed
   - 40-59: Fair - Significant gaps to address
   - 0-39: Poor - Major revision required

3. **Detailed Scoring Breakdown**: Show points earned for each element
4. **Gap Analysis**: List of missing or weak elements
5. **Improvement Suggestions**: Prioritized recommendations
6. **Next Steps**:
   - If score ≥ 85: Ready to proceed with learning graph generation
   - If score < 85: Recommend addressing specific gaps before generating learning graph

### 2.6 Update Course Description Metadata

In this section NAME is the name of the course taken from the course description.
QUALITY_SCORE is the score you computed for the course description.

If it does not exist, add the following yml metadata at the top of the docs/course-description.md file:

```yml
---
title: Course Description for Course NAME
description: A detailed course description for NAME including overview, topics covered and learning objectives in the format of the 2001 Bloom Taxonomy
quality_score: QUALITY_SCORE
---
```

### 2.7 Concept Generation Readiness

Assess whether the course description contains sufficient detail to generate 200 concepts:
- Evaluate topic breadth and depth
- Check if Bloom's Taxonomy outcomes suggest diverse concept types
- Estimate potential concept count based on current content
- Recommend additions if concept generation may fall short

### Add course-description.md and the course-description-assessment.md to mkdocs.yml Navigation

After the course-description.md file has been added to the /docs directory,
ask the user if the new file should be added to the mkdocs.yml file.
If the answer is yes, place the new file after the about.md file.

```yml
nav:
  - Home: index.md
  - About: about.md
  - Course Description: course-description.md
  # ...other existing entries stay where they are...
  - Learning Graph:
      - Course Description Assessment: learning-graph/course-description-assessment.md
```

Every entry in `nav` is a YAML list item, so each one must begin with `- `,
including the entries nested under a section such as `Learning Graph`. Keep
the indentation the file already uses. A nested entry written without the
dash makes `mkdocs build --strict` fail with "Expected nav to be a list".

## Next Step

For all users with a score over 85, ask if the `learning-graph-generator` skill should be run next.

## Best Practices

When using this skill:

1. **Be thorough**: Don't skip Bloom's Taxonomy levels—all six are essential for comprehensive learning
2. **Be specific**: Vague outcomes like "understand the material" won't support quality learning graphs
3. **Use action verbs**: Each outcome should start with a measurable verb (list, explain, apply, analyze, evaluate, design, etc.)
4. **Think concepts**: Each topic and outcome should suggest multiple learnable concepts
5. **Set boundaries**: Topics excluded are as important as topics covered for scope management

## Resources

### assets/

- `course-description-template.md`: Template structure for creating new course descriptions with all required sections and Bloom's Taxonomy framework