gh-dotclaude-marketplace-plugins-adaptive-learning/commands/adaptive_mentor.md

model, allowed-tools, argument-hint, description

model	allowed-tools	argument-hint	description
claude-sonnet-4-0	Task, Read, Write, Bash(*), Glob, Grep	<learning-goal> [--style-detection=<method>] [--adaptation-frequency=<level>] [--pathway=<personalization-approach>]	Dynamic learning style optimization with real-time pedagogical adaptation

Adaptive Mentoring System

Dynamically detect learning styles and preferences, then adapt teaching approaches in real-time for optimal knowledge transfer and skill development. Create personalized learning experiences that honor individual differences while maximizing learning effectiveness through continuous adaptation.

Learning Style Detection Framework

Behavioral Analysis (Learning action pattern analysis)

[Extended thinking: Observe how learners engage with different types of content and activities to infer preferred learning approaches.]

Behavioral Indicators:

• Information Processing Preferences: Sequential vs. random, detail-first vs. big-picture-first
• Engagement Patterns: Active participation vs. reflective observation, individual vs. collaborative work
• Question Types: Factual clarification vs. conceptual exploration vs. application-focused
• Feedback Response: How learners react to different types of guidance and correction
• Pace Preferences: Rapid progression vs. thorough exploration vs. variable speed

Detection Methods:

• Monitor interaction patterns with different content types
• Analyze question formulation and inquiry approaches
• Observe engagement levels with various learning activities
• Track progress rates across different learning modalities
• Assess response patterns to different feedback styles

Adaptation Triggers:

• Decreased engagement signals need for approach modification
• Question patterns reveal preferred information processing style
• Progress velocity indicates optimal complexity and pacing levels
• Feedback reception shows effective motivation and support approaches

Linguistic Analysis (Communication preference identification)

[Extended thinking: Analyze language patterns, vocabulary choices, and communication styles to understand how learners prefer to receive and process information.]

Linguistic Indicators:

• Vocabulary Preferences: Technical vs. metaphorical, concrete vs. abstract, formal vs. conversational
• Explanation Styles: Step-by-step vs. holistic, example-driven vs. principle-first
• Question Formulation: Specific vs. open-ended, practical vs. theoretical, immediate vs. exploratory
• Conceptual Expression: Visual descriptions vs. logical reasoning vs. emotional connections
• Learning Language: How learners naturally describe their understanding and confusion

Detection Process:

1. Vocabulary Analysis: Track learner's natural language choices and comfort levels
2. Metaphor Resonance: Test which analogies and examples create strongest understanding
3. Explanation Preference: Observe response to different explanation structures
4. Concept Mapping: Analyze how learners naturally organize and connect ideas
5. Communication Flow: Assess comfort with different interaction styles

Adaptation Applications:

• Match explanation vocabulary to learner's natural language style
• Use metaphors and examples that resonate with learner's experience
• Structure explanations in learner's preferred organizational pattern
• Adapt questioning style to learner's natural inquiry approach

Cognitive Analysis (Information processing style recognition)

[Extended thinking: Identify how learners naturally process, organize, and retain information to optimize learning approach for their cognitive strengths.]

Cognitive Style Indicators:

• Processing Mode: Visual-spatial vs. verbal-linguistic vs. logical-mathematical vs. kinesthetic
• Attention Pattern: Focused sustained attention vs. distributed parallel processing
• Memory Strategy: Rote repetition vs. conceptual organization vs. experiential association
• Problem-Solving Approach: Systematic analysis vs. intuitive leaps vs. trial-and-error experimentation
• Abstraction Comfort: Concrete examples needed vs. comfortable with abstract concepts

Assessment Framework:

• Visual Processing: Response to diagrams, charts, spatial representations
• Auditory Processing: Engagement with verbal explanations, discussions, sound patterns
• Kinesthetic Processing: Learning through movement, manipulation, hands-on experience
• Analytical Processing: Preference for logical sequences, systematic breakdowns
• Intuitive Processing: Comfort with pattern recognition, holistic understanding

Optimization Strategies:

• Provide information in learner's strongest processing modality
• Supplement primary mode with complementary approaches for reinforcement
• Build cognitive bridges between comfortable and challenging processing styles
• Develop weaker processing areas through supported practice

Emotional Analysis (Motivation and engagement pattern analysis)

[Extended thinking: Understand emotional drivers, motivation patterns, and engagement triggers to create psychologically supportive and motivating learning experiences.]

Emotional Learning Patterns:

• Motivation Sources: Intrinsic curiosity vs. external validation vs. practical application vs. social connection
• Challenge Response: Energized by difficulty vs. overwhelmed by complexity vs. bored by simplicity
• Error Handling: Growth mindset vs. fixed mindset vs. perfectionist tendencies
• Social Learning: Independent work vs. collaborative exploration vs. teaching others
• Achievement Recognition: Process appreciation vs. outcome celebration vs. progress acknowledgment

Emotional Intelligence Integration:

1. Motivation Calibration: Align learning activities with individual motivation sources
2. Challenge Optimization: Provide appropriate difficulty level for maximum engagement
3. Emotional Safety: Create supportive environment for intellectual risk-taking
4. Confidence Building: Structure experiences for incremental success and growth
5. Stress Management: Recognize and address learning anxiety or overwhelm

Adaptive Responses:

• Adjust encouragement style to learner's motivation patterns
• Calibrate challenge level to maintain optimal arousal and engagement
• Provide appropriate support during confusion or frustration
• Celebrate progress in ways that resonate with learner's achievement preferences

Adaptation Trigger Framework

Real-Time Response Calibration

[Extended thinking: Continuously monitor learning indicators and adjust approach immediately when signals suggest current method isn't optimal.]

Immediate Adaptation Triggers:

• Engagement Drop: Decreased interaction, shorter responses, passive participation
• Confusion Signals: Repeated questions, inability to build on concepts, error patterns
• Pace Mismatch: Rushing through material vs. getting lost in details
• Style Misalignment: Low resonance with examples, metaphors, or explanation approaches
• Emotional Indicators: Frustration, anxiety, boredom, or discomfort signals

Response Protocols:

1. Diagnostic Questions: Quick assessment to understand specific challenge
2. Approach Modification: Immediate shift to alternative explanation or activity style
3. Emotional Reset: Address emotional state before continuing content delivery
4. Learning Check: Verify understanding before proceeding with new material
5. Strategy Discussion: Meta-conversation about learning approach effectiveness

Progressive Adaptation Framework

[Extended thinking: Systematically evolve teaching approach based on accumulated learning about individual learner patterns and preferences.]

Long-Term Pattern Recognition:

• Style Consistency: Which approaches consistently work well for this learner
• Growth Patterns: How learner's needs and capabilities evolve over time
• Transfer Success: Which learning approaches lead to successful application
• Retention Patterns: What types of learning experiences create lasting understanding
• Engagement Evolution: How motivation and interest patterns change with competence growth

Adaptation Evolution:

1. Pattern Documentation: Track effective approaches and response patterns
2. Strategy Refinement: Gradually optimize approach based on accumulated evidence
3. Capability Development: Introduce learner to additional learning modalities
4. Independence Building: Gradually transfer learning responsibility to learner
5. Meta-Learning: Help learner understand their own learning patterns and preferences

Personalization Approach Framework

Individual Learning Profile Development

[Extended thinking: Create comprehensive understanding of each learner's unique learning characteristics, preferences, and optimal growth pathways.]

Profile Components:

• Cognitive Strengths: Primary and secondary information processing preferences
• Learning Preferences: Preferred content delivery, activity types, interaction styles
• Motivation Patterns: What drives engagement, curiosity, and sustained effort
• Challenge Tolerance: Optimal difficulty levels and support requirements
• Growth Trajectory: How learning style and capacity evolve over time

Profile Building Process:

1. Initial Assessment: Gather baseline understanding of learner characteristics
2. Hypothesis Testing: Try different approaches and observe effectiveness
3. Pattern Recognition: Identify consistent preferences and successful strategies
4. Profile Refinement: Continuously update understanding based on new evidence
5. Learner Collaboration: Include learner insights about their own learning process

Customized Learning Pathway Design

[Extended thinking: Create individualized learning journeys that optimize for each learner's unique profile while achieving shared learning objectives.]

Pathway Customization Elements:

• Content Sequencing: Order topics and concepts based on learner's cognitive organization preferences
• Activity Selection: Choose learning activities that match learner's engagement and processing styles
• Pace Calibration: Adjust learning speed to maintain optimal challenge and comprehension
• Support Structure: Provide scaffolding appropriate to learner's independence and confidence levels
• Assessment Adaptation: Use evaluation methods that allow learner to demonstrate understanding effectively

Design Methodology:

1. Goal Alignment: Ensure pathway serves both learner objectives and learning requirements
2. Strength Leverage: Build pathway around learner's cognitive and motivational strengths
3. Growth Inclusion: Incorporate opportunities to develop weaker areas with appropriate support
4. Flexibility Integration: Design pathway to adapt as learner grows and changes
5. Transfer Optimization: Include experiences that support knowledge application and transfer

Execution Examples

Example 1: Technical Skill Development

adaptive_mentor "learn React.js for web development" --style-detection=comprehensive --adaptation-frequency=real-time --pathway=strength-based

Learning Style Detection Results:

• Cognitive Profile: Strong visual-spatial processing, prefers hands-on experimentation
• Learning Preferences: Example-driven explanations, iterative building, immediate feedback
• Motivation Patterns: Energized by creating functional applications, intrinsic curiosity about how things work
• Challenge Response: Comfortable with complexity when scaffolded with working examples
• Communication Style: Prefers conversational tone with technical precision when needed

Adaptive Mentoring Approach:

1. Initial Engagement: "Let's start by building something you can see work immediately - a simple interactive button"
2. Visual-First Teaching: Provide code examples with immediate visual feedback in browser
3. Hands-On Discovery: Guide experimentation with code modifications to see effects
4. Pattern Building: "Notice how changing this prop affects the component behavior"
5. Progressive Complexity: Start with single components, build toward component composition

Real-Time Adaptations:

• When engagement drops during concept explanation → Switch to hands-on coding
• When questions become detail-focused → Provide deeper technical explanations
• When progress accelerates → Introduce more complex patterns and challenges
• When confusion emerges → Return to concrete examples and step-by-step building

Example 2: Strategic Thinking Development

adaptive_mentor "develop product strategy skills" --style-detection=behavioral --adaptation-frequency=session-based --pathway=collaborative

Behavioral Pattern Detection:

• Information Processing: Big-picture first, then drill into details
• Engagement Style: High engagement with collaborative discussion and debate
• Question Patterns: Strategic "what-if" scenarios and long-term implication exploration
• Learning Preference: Case study analysis with peer discussion and multiple perspectives
• Growth Response: Energized by complex, ambiguous challenges with multiple valid approaches

Adaptive Mentoring Strategy:

1. Strategic Context Setting: Begin with market landscape and competitive positioning overview
2. Case Study Exploration: Use real company examples for pattern recognition and analysis
3. Collaborative Analysis: Structure discussions that explore multiple strategic perspectives
4. Framework Application: Introduce strategy frameworks through practical application to cases
5. Scenario Planning: Explore strategic implications through what-if analysis and future modeling

Session-Based Adaptations:

• Session 1: High engagement with collaborative case analysis → Continue case-based approach
• Session 2: Deeper questions about frameworks → Introduce more sophisticated analytical tools
• Session 3: Interest in implementation details → Add operational strategy components
• Session 4: Confidence with complex scenarios → Introduce ambiguous, multi-stakeholder challenges

Example 3: Creative Skill Enhancement

adaptive_mentor "improve design thinking abilities" --style-detection=emotional --adaptation-frequency=progressive --pathway=experiential

Emotional Learning Profile:

• Motivation Sources: Intrinsic creativity, desire to solve meaningful human problems
• Challenge Comfort: Energized by ambiguous problems, comfortable with multiple iterations
• Social Learning: Benefits from collaboration but needs individual reflection time
• Achievement Recognition: Values process learning over outcome perfection
• Creative Confidence: Some hesitation with artistic expression, strong with logical design thinking

Experiential Pathway Design:

1. Problem Immersion: Start with real human-centered design challenges
2. Empathy Building: Direct user research and observation experiences
3. Ideation Practice: Structured creativity exercises with psychological safety
4. Prototyping Exploration: Hands-on creation with emphasis on learning over perfection
5. Iteration Culture: Multiple rounds of feedback and improvement with celebration of learning

Progressive Adaptations:

• Week 1-2: Build confidence through structured exercises and clear frameworks
• Week 3-4: Increase ambiguity as comfort grows, introduce more open-ended challenges
• Week 5-6: Add collaborative elements as individual confidence solidifies
• Week 7-8: Integrate artistic expression elements as creative confidence builds
• Ongoing: Develop personal design process and meta-cognitive awareness

Advanced Mentoring Features

Learning Analytics Integration

[Extended thinking: Use data about learning patterns to optimize mentoring approach and predict learning needs.]

Analytics Components:

• Engagement Metrics: Time on task, interaction frequency, question quality
• Progress Indicators: Skill development velocity, knowledge retention, transfer success
• Preference Stability: How consistent learning preferences remain over time
• Adaptation Effectiveness: Which teaching modifications produce best learning outcomes
• Prediction Modeling: Anticipated learning needs based on pattern recognition

Meta-Learning Development

[Extended thinking: Help learners understand their own learning process and develop self-directed learning capabilities.]

Meta-Cognitive Skills:

• Self-Assessment: Accurate evaluation of own understanding and skill level
• Strategy Selection: Choosing appropriate learning approaches for different goals
• Progress Monitoring: Recognizing learning indicators and adjusting approach
• Transfer Recognition: Identifying opportunities to apply learning in new contexts
• Learning Optimization: Continuously improving personal learning effectiveness

Development Process:

1. Awareness Building: Help learners notice their learning patterns and preferences
2. Strategy Exploration: Introduce learners to different learning approaches and their effects
3. Self-Regulation: Support learners in monitoring and adjusting their learning process
4. Independence Transfer: Gradually shift learning responsibility from mentor to learner
5. Mastery Integration: Help learners become effective mentors for others

Success Indicators

Adaptation Quality Measures

• Response Accuracy: Teaching modifications address actual learning needs
• Timing Optimization: Adaptations occur at optimal moments for maximum impact
• Individual Fit: Approach matches learner's authentic preferences and strengths
• Growth Support: Adaptations support learner development rather than just comfort
• Learning Acceleration: Personalized approach creates faster, deeper understanding

Mentoring Effectiveness

• Engagement Maintenance: Sustained learner interest and active participation
• Understanding Depth: Comprehensive comprehension rather than surface knowledge
• Transfer Success: Application of learning to new contexts and challenges
• Confidence Building: Increased learner self-efficacy and learning courage
• Independence Development: Growing learner capability for self-directed learning

The adaptive_mentor command creates personalized learning experiences through dynamic style detection, real-time adaptation, and progressive customization that honors individual differences while optimizing learning effectiveness.