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Recommended Claude Skills for Intelligent Textbooks

Based on analysis of the intelligent-textbooks repository workflows and content patterns, here are the recommended Claude Skills to complement your existing MicroSim and Learning Graph skills.

Overview

You currently have:

  1. ✅ MicroSim P5 - Creates interactive p5.js simulations
  2. ✅ Learning Graph - Generates concept dependency graphs

Recommended additions:

  1. Glossary Generator - Automated glossary creation from concepts
  2. FAQ Generator - Creates comprehensive FAQ from content
  3. Chapter Content Generator - Generates complete chapter content with scaffolding
  4. Quiz Generator - Creates assessments aligned to Bloom's Taxonomy
  5. Quality Metrics Analyzer - Comprehensive textbook quality analysis
  6. Social Media Generator - Creates social cards and promotional content
  7. Concept Validator - Validates concept integration across all components
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graph TD
    A[Course Description] --> B[Learning Graph Skill]
    B --> C[Glossary Generator]
    B --> D[Chapter Content Generator]
    C --> E[FAQ Generator]
    D --> E
    D --> F[Quiz Generator]
    D --> G[MicroSim P5 Skill]
    E --> H[Concept Validator]
    F --> H
    G --> H
    H --> I[Quality Metrics Analyzer]
    I --> J[Social Media Generator]

    style B fill:#90EE90
    style G fill:#90EE90
    style A fill:#FFD700
    style J fill:#87CEEB

Workflow Phases

Phase 1: Foundation (Existing)

  • Course Description created manually
  • Learning Graph Skill generates concept structure
  • MicroSim P5 Skill creates interactive simulations (ongoing)

Phase 2: Content Generation (New Skills)

  1. Glossary Generator - Run after learning graph finalized
  2. Chapter Content Generator - Run after chapter structure defined
  3. FAQ Generator - Run after chapters written

Phase 3: Assessment (New Skill)

  1. Quiz Generator - Run after each chapter or in batch

Phase 4: Validation (New Skill)

  1. Concept Validator - Run at milestones (every 10 chapters, before release)

Phase 5: Quality & Promotion (New Skills)

  1. Quality Metrics Analyzer - Run before release and periodically
  2. Social Media Generator - Run when ready to promote

Skill Comparison Matrix

Skill Primary Input Primary Output Quality Metric Focus Execution Time
Learning Graph Course Description Concept dependency CSV DAG validity, concept quality Medium (20-30 min)
MicroSim P5 Concept description Interactive simulation Functionality, educational value Medium (15-25 min)
Glossary Generator Concept list glossary.md ISO 11179 compliance Fast (10-15 min)
FAQ Generator All content faq.md Coverage, Bloom's distribution Medium (20-30 min)
Chapter Content Generator Learning graph + outline Chapter markdown files Scaffolding, completeness Slow (30-45 min/chapter)
Quiz Generator Chapter content Quiz markdown/JSON Question quality, coverage Fast (10-15 min/chapter)
Quality Metrics Analyzer Entire repository Quality reports Comprehensive metrics Medium (15-25 min)
Social Media Generator All content Social cards + posts Engagement potential Medium (20-30 min)
Concept Validator All components Validation reports Integration completeness Fast (10-20 min)

Detailed Skill Summaries

1. Glossary Generator ⭐⭐⭐ (High Priority)

Purpose: Converts learning graph concepts into ISO 11179-compliant glossary definitions

Key Features:

  • Generates precise, concise, non-circular definitions
  • Includes relevant examples for context
  • Alphabetically organized
  • Quality scoring for each definition

When to Use: After learning graph concept list is finalized and reviewed

Inputs:

  • Learning graph concept list (02-concept-list-v1.md)
  • Course description for context

Outputs:

  • docs/glossary.md (complete glossary)
  • Quality report assessing definition compliance

Quality Metrics:

  • Input: Concept uniqueness (100%), Title Case formatting (95%+), length <32 chars (98%+)
  • Output: ISO compliance (4 criteria × 25 pts), readability, example coverage (60-80%)

Success Score: >85/100 (no circular definitions, all concepts included)

2. FAQ Generator ⭐⭐⭐ (High Priority)

Purpose: Creates comprehensive FAQ from content, learning graph, and glossary

Key Features: - Questions organized by category and difficulty - Answers with links to relevant sections - Chatbot training data export (JSON) - Bloom's Taxonomy distribution across questions

When to Use: After course description, learning graph, glossary, and 30%+ of chapters exist

Inputs: - Course description - Learning graph (concept dependencies) - Glossary (50+ terms recommended) - Chapter content (5,000+ words preferred)

Outputs: - docs/faq.md (categorized FAQ) - faq-chatbot-training.json (for RAG systems) - Quality report with coverage analysis

Quality Metrics: - Input: Content completeness, learning graph validity, glossary size - Output: Coverage (30 pts - 80%+ concepts), Bloom's distribution (25 pts), answer quality (25 pts), organization (20 pts)

Success Score: >75/100 (minimum 40 questions, 60% concept coverage)

3. Chapter Content Generator ⭐⭐⭐⭐ (Critical Priority)

Purpose: Generates comprehensive chapter content with proper scaffolding and Bloom's alignment

Key Features: - Concept-to-chapter mapping from learning graph - Prerequisite compliance validation - Examples and practice exercises - MicroSim recommendations - Progressive complexity

When to Use: After learning graph complete, chapter structure defined in mkdocs.yml

Inputs: - Learning graph dependencies (CSV) - Concept taxonomy (CSV) - Course description - Chapter outline (concept-to-chapter mapping) - Glossary (optional but recommended)

Outputs: - Chapter markdown files (docs/[section]/[chapter].md) - Chapter metadata (JSON) - Generation report with quality scores - Content gaps analysis - Quiz bank (suggested questions) - MicroSim recommendations

Quality Metrics: - Input: Learning graph quality (100 pts scale), chapter specification clarity - Output: Scaffolding (25 pts), Bloom's alignment (20 pts), completeness (25 pts), readability (15 pts), engagement (15 pts)

Success Score: >75/100 per chapter (100% prerequisite compliance, 2000+ words, 3+ examples)

4. Quiz Generator ⭐⭐⭐ (High Priority)

Purpose: Creates multiple-choice quizzes aligned to Bloom's Taxonomy for each chapter

Key Features: - 8-12 questions per chapter - Distributed across cognitive levels - Quality distractors (plausible wrong answers) - Explanations for all answers - Links to chapter sections

When to Use: After chapter content exists (1000+ words per chapter)

Inputs: - Chapter content (markdown files) - Learning graph (concept mapping) - Glossary (for terminology questions) - Course description (Bloom's outcomes)

Outputs: - Quiz markdown (embedded or separate files) - Quiz metadata (JSON) - Quiz bank (JSON for LMS/chatbot) - Generation report with quality scores

Quality Metrics: - Input: Chapter word count (1000+), example coverage (60%+), glossary coverage (80%+) - Output: Question quality (30 pts), Bloom's distribution (25 pts), concept coverage (20 pts), answer balance (15 pts), pedagogical value (10 pts)

Success Score: >70/100 (8-12 questions, Bloom's ±15% target, 75% concept coverage)

5. Quality Metrics Analyzer ⭐⭐⭐⭐ (Critical Priority)

Purpose: Comprehensive quality analysis across all textbook dimensions

Key Features: - Content structure metrics (navigation, balance, coverage) - Engagement features (MicroSims, quizzes, interactivity) - Technical quality (build config, links, accessibility) - Educational effectiveness (Bloom's, scaffolding, learning graph) - Trend analysis (if historical data available)

When to Use: Before deployment, after milestones, periodically for mature textbooks

Inputs: - Entire docs/ directory - mkdocs.yml configuration - Learning graph - Glossary, MicroSims, all content

Outputs: - quality-report.md (comprehensive summary) - metrics-[date].json (machine-readable) - Content structure analysis - Engagement analysis - Technical quality analysis - Educational effectiveness analysis - Prioritized recommendations

Quality Metrics: - Content structure (25 pts): Navigation, balance, coverage - Engagement features (25 pts): Interactivity, variety, practice - Technical quality (25 pts): Build config, code quality, production readiness - Educational effectiveness (25 pts): Bloom's alignment, scaffolding, learning support

Success Score: >75/100 for publication readiness

6. Social Media Generator ⭐⭐ (Medium Priority)

Purpose: Creates social media assets and promotional campaign for textbook marketing

Key Features: - Custom Open Graph images (1200×630px) - Platform-specific posts (Twitter, LinkedIn, Facebook, Reddit) - Launch campaign schedule - MicroSim preview GIFs - UTM tracking links

When to Use: After 70%+ chapters complete, before marketing/promotion efforts

Inputs: - All markdown content - Learning graph - MicroSims - Course description - Branding assets (logo, colors)

Outputs: - Social cards (PNG images) - Open Graph metadata (frontmatter) - MicroSim preview GIFs - Social media posts (20-30 pre-written) - Launch campaign plan - Visual quote cards - UTM link library

Quality Metrics: - Social card quality: Visual design (25 pts), content effectiveness (25 pts), technical quality (25 pts), platform optimization (25 pts) - Promotional content: Messaging (30 pts), platform appropriateness (25 pts), campaign coherence (25 pts), engagement potential (20 pts)

Success Score: >75/100 for social cards, >70/100 for promotional content

7. Concept Validator ⭐⭐⭐⭐ (Critical Priority)

Purpose: Validates concept integration across all textbook components (chapters, glossary, quizzes, MicroSims)

Key Features: - Coverage matrix (concept × component) - Scaffolding compliance verification - Terminology consistency checking - Gap identification and prioritization - Cross-component integration validation

When to Use: After learning graph complete, periodically during development, before release

Inputs: - Learning graph (dependencies + taxonomy) - All chapter content - Glossary - Quiz files - MicroSims - FAQ (optional)

Outputs: - validation-report.md (comprehensive analysis) - validation-[date].json (machine-readable results) - concept-coverage-matrix.csv (detailed matrix) - gap-analysis.md (actionable gaps) - scaffolding-validation.md (prerequisite compliance) - consistency-report.md (terminology standardization)

Quality Metrics: - Coverage completeness (40 pts): Chapter, glossary, quiz, MicroSim coverage per concept - Pedagogical quality (30 pts): Scaffolding, Bloom's alignment, examples - Consistency & integration (20 pts): Terminology, cross-references, context - Support & accessibility (10 pts): FAQ coverage, multiple modalities

Concept Health Categories: - Excellent (85-100): Publication ready - Good (70-84): Minor enhancements - Adequate (55-69): Improvements recommended - Insufficient (40-54): Remediation needed - Critical Gap (<40): Immediate attention required

Success Score: Overall health >75/100, no critical gaps, 100% prerequisite compliance

Implementation Recommendations

Priority Ranking

Must Have (Build First):

  1. Chapter Content Generator - Core content creation
  2. Concept Validator - Quality assurance critical path
  3. Quality Metrics Analyzer - Comprehensive quality gate

Should Have (Build Next):

  1. Glossary Generator - Essential reference, fast ROI
  2. FAQ Generator - Student support + chatbot prep
  3. Quiz Generator - Assessment fundamental

Nice to Have (Build Later):

  1. Social Media Generator - Marketing/promotion phase

Build Order Recommendation

Iteration 1 (Weeks 1-2):

  1. Glossary Generator (simplest, high value)
  2. Concept Validator (validates everything, useful immediately)

Iteration 2 (Weeks 3-4):

  1. Chapter Content Generator (most complex, highest impact)
  2. Quiz Generator (complements chapters)

Iteration 3 (Weeks 5-6):

  1. FAQ Generator (leverages existing content)
  2. Quality Metrics Analyzer (comprehensive validation)

Iteration 4 (Week 7):

  1. Social Media Generator (polish for launch)

Integration Strategy

Skill Chaining:

  • Learning Graph → Glossary Generator (automatic handoff)
  • Chapter Generator → Quiz Generator (automatic quiz creation)
  • All skills → Concept Validator (continuous validation)
  • Concept Validator → Quality Metrics (comprehensive report)

Automation Opportunities:

  1. After Learning Graph finalized → Auto-run Glossary Generator
  2. After each chapter generated → Auto-run Quiz Generator
  3. Every 5 chapters → Auto-run Concept Validator
  4. Before deployment → Auto-run Quality Metrics Analyzer

Quality Gates:

  • Gate 1: Concept Validator health score >60 (Alpha)
  • Gate 2: Quality Metrics score >70 (Beta)
  • Gate 3: Both scores >75 + Social Media ready (Production)

Expected Time Savings

Without Skills (Manual work):

  • Glossary (200 terms): ~8-10 hours
  • FAQ (40 questions): ~6-8 hours
  • Chapters (20 chapters): ~40-60 hours
  • Quizzes (20 chapters × 10 questions): ~20-30 hours
  • Quality analysis: ~4-6 hours
  • Social media (30 posts + cards): ~8-12 hours
  • Total: 86-126 hours

With Skills (AI-assisted):

  • Glossary: ~30 minutes (review + refinement)
  • FAQ: ~1 hour (review + refinement)
  • Chapters: ~15-20 hours (review + refinement)
  • Quizzes: ~5 hours (review + refinement)
  • Quality analysis: ~1 hour (review)
  • Social media: ~2 hours (review + customization)
  • Total: 24-30 hours

Time Savings: 60-95 hours (70-75% reduction)

Quality Improvement

Consistency Benefits:

  • 100% prerequisite compliance (vs. ~80-90% manual)
  • ISO 11179 glossary standards (vs. informal definitions)
  • Balanced Bloom's Taxonomy (vs. memorization-heavy)
  • Comprehensive coverage validation (vs. spot checks)

New Capabilities:

  • Automated gap detection (not feasible manually)
  • Trend analysis across textbook versions
  • Systematic quality metrics (objective scoring)
  • Integration validation (cross-component checking)

Next Steps

  1. Review: Examine each skill specification in detail
  2. Prioritize: Confirm build order based on your needs
  3. Prototype: Start with Glossary Generator (simplest, high value)
  4. Iterate: Add skills incrementally, refining based on usage
  5. Integrate: Chain skills together for automated workflows
  6. Measure: Track time savings and quality improvements

Skill Files Created

All detailed specifications available in:

Each file follows the template structure with detailed input/output specifications and quality metrics.