learning-first-principles

A cognitive framework based on learning first principles, providing learning method diagnosis, efficiency assessment, and optimization advice. Use when: (1) Diagnosing if current learning methods align with first principles, (2) Evaluating learning plan efficiency and time investment, (3) Analyzing learning behavior problems and providing improvement suggestions, (4) Determining if learning content is worth the time investment. Core principle chain: Self-learning → Induction → Self-output → Expression restructuring → Logical understanding → Practice.

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Install skill "learning-first-principles" with this command: npx skills add hexbee/hello-skills/hexbee-hello-skills-learning-first-principles

Learning First Principles Analysis

Core Principle

The essence of learning is internal drive rather than external infusion:

LevelAnti-pattern (Avoid)Positive Pattern (Pursue)
Learning ViewRelying on tutoring/external inputSelf-learning driven
MethodologyTime-consuming/mechanical repetitionInduction & summary
ProcessingMechanical copyingSelf-output
OutputSimple repetitionExpression restructuring
ExpressionFormal/template-basedLogic-driven
UnderstandingStopping at theoryPractice verification

Analysis Framework

When users provide learning content, methods, or plans, analyze from these dimensions:

1. Self-learning Drive

  • Diagnosis: Relying on external push (tutoring, supervision)?
  • Action: Transform into self-driven exploration goals

2. Induction & Summary

  • Diagnosis: "Killing time" rather than "thinking"?
  • Action: Extract core, transferable patterns

3. Self-output

  • Diagnosis: Mechanically copying?
  • Action: Restate in your own words

4. Expression Restructuring

  • Diagnosis: Simply repeating textbook wording?
  • Action: Reorganize knowledge from new angles and frameworks

5. Logic-driven

  • Diagnosis: Applying templates/forms blindly?
  • Action: Trace causal chains, understand why

6. Practice Verification

  • Diagnosis: Stopping at theoretical understanding?
  • Action: Design minimal verification experiments

Output Format

Problem Diagnosis

  • Point out specific behaviors violating first principles
  • Use principle chain to explain problem roots

Improvement Suggestions

  • Provide 1-3 immediately actionable steps
  • Each action corresponds to a principle level

Efficiency Assessment

  • Estimate time ROI of current methods
  • Provide expected efficiency improvement after optimization

Usage Example

User Input: I want to learn Python, signed up for a training class, 2 hours of class daily

Analysis Output:

Diagnosis:
- Relying on external input (training class) instead of self-learning driven
- Passive reception instead of active exploration

Improvement Suggestions:
1. First set a specific project goal (e.g., office automation script)
2. Use projects to drive learning, training class as supplementary resource
3. Spend 1 hour daily on projects, 0.5 hours on targeted lectures

Efficiency Assessment:
- Current: Low (passive learning, high forgetting rate)
- Optimized: High (active construction, transferable)

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