mj-learn

Deconstruct complex concepts across 8 dimensions and append analyses to a Lark document.

Updated May 30, 2026
One-click install
npx skills add https://github.com/RockerMJ031/mj-claude-skills --skill mj-learn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mj-learn
Source: https://github.com/RockerMJ031/mj-claude-skills/tree/main/mj-learn
Command: npx skills add https://github.com/RockerMJ031/mj-claude-skills --skill mj-learn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the risk of shallow, incomplete understanding of complex concepts by providing a rigorous, multi-dimensional deep dive framework that captures all critical insights and saves them to a centralized, searchable Lark document for long-term reference.

Core Features & Use Cases

  • 8-Dimensional Concept Deconstruction: Breaks down any concept across 8 core angles (history, dialectics, phenomenology, linguistics, formalization, existentialism, aesthetics, meta-philosophy) to eliminate blind spots and common misconceptions.
  • Immersive Insight Compression: Switches to a first-person perspective of the concept, then compresses all findings into a single core epiphany and ASCII structural diagram for easy recall and sharing.
  • Append-Only Knowledge Storage: Automatically appends full concept analyses to the shared Lark Doc "概念解剖册" with sequential numbering, with built-in guardrails to avoid costly reordering of existing entries.
  • Use Case: Ideal for students, researchers, and knowledge workers who need to deeply understand abstract academic terms, industry jargon, or theoretical ideas, with all analyses saved in a single team-accessible location.

Quick Start

Ask the Skill to deconstruct a concept you want to understand deeply, such as "解剖概念:熵" or "explain the concept of game theory", and it will complete the full structured analysis and save it directly to your Lark knowledge base.

Frequently Asked Questions about mj-learn

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deconstruct complex concepts to avoid shallow understanding in academic research?▼

To deconstruct complex concepts and avoid shallow understanding, apply an 8-dimensional analysis framework covering history, dialectics, phenomenology, linguistics, formalization, existentialism, aesthetics, and meta-philosophy. This structured deep dive eliminates blind spots and captures critical insights.

What is the best way to systematically analyze abstract terms and save the results to Lark?▼

The best way to analyze abstract terms and save to Lark is using an automated append-only framework that appends full concept analyses to a shared Lark Doc with sequential numbering. This ensures long-term centralized reference without reordering existing entries.

Does this 8-dimensional concept deconstruction framework work for team knowledge management?▼

Yes, this 8-dimensional concept deconstruction framework works for team knowledge management by automatically appending full analyses to a centralized, searchable Lark document. It provides sequential numbering and built-in guardrails to prevent costly reordering of existing team entries.

How do I compress a deep learning concept analysis into a single core epiphany?▼

To compress a deep learning concept analysis into a single core epiphany, switch to a first-person perspective of the concept, then compress all findings into a single core epiphany alongside an ASCII structural diagram for easy recall and sharing.

Can I use this concept deconstruction tool for understanding industry jargon without prior philosophical training?▼

You can use this concept deconstruction tool for industry jargon without prior philosophical training. It provides a rigorous 8-dimensional framework covering historical context and dialectical framing that systematically guides you through the deep comprehension process.

What are the limitations of using append-only sequential numbering for knowledge management in Lark?▼

The limitation of using append-only sequential numbering for knowledge management in Lark is that it restricts structural modification. Built-in guardrails actively prevent the costly reordering of existing entries, meaning analyses must follow a strict chronological append sequence.