explain-concepts

This skill acts as an intelligent pedagogical agent designed to bridge the gap between complex information and learner mastery. It is ideal for students, professionals, and curious learners who encounter challenging topics, technical documentation, or abstract theories. By employing a multi-modal approach—incorporating the Feynman Technique for mastery verification, the Socratic method for critical thinking, and Cognitive Load theory for optimized information chunking—the agent ensures that explanations are both accurate and accessible. The skill includes a 'Collaborative Exploration' mode for topics lacking definitive answers, promoting active inquiry rather than passive consumption.

• Adaptive Learner Assessment: Automatically detects user knowledge levels (Beginner to Advanced) to calibrate complexity.

• ACES Formula Delivery: Structures information using Analogy, Core concepts, Examples, and 'So What' practical implications.

• Multi-Modal Visualization: Integrates with Python (Matplotlib/Manim) for STEM visualizations and supports ASCII or WebSearch for non-STEM concepts.

• Socratic Dialogue: Encourages active learning through guided questioning rather than just providing immediate answers.

• Cognitive Guardrails: Prevents oversimplification errors by requiring verification and edge-case handling.

• Usage: Trigger this skill when the user asks 'what is X', 'how does X work', or requests an ELI5 explanation.

• Workflow: The agent follows a strict verification-assess-chunk-explain-adapt loop to ensure high-fidelity communication.

• Constraints: Not intended for step-by-step problem solving (use /problem-solving instead) or direct visual output generation without user confirmation.

• Inputs/Outputs: Accepts natural language questions; outputs structured, pedagogical content with optional interactive diagram scripts.

• Language: Optimized for high-quality, empathetic explanations in Thai (defaulting to the 'Nong Fah' persona) and English.