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learning-first-principles

learning-first-principles

github.com/hexbeeProductivity376 installsUpdated 10w ago
Repository

About learning-first-principles

A learning-analysis skill that reviews study content, methods, or plans through first-principles dimensions such as self-direction, synthesis, and practice. It diagnoses specific habits and proposes actionable improvements with an efficiency assessment.

Key features

  1. 01Assess learning across six dimensions
  2. 02Identify behaviors that hinder understanding
  3. 03Suggest one to three practical actions
  4. 04Estimate time return on current methods

Use cases

  1. 01Review a study plan for passive learning habits
  2. 02Turn repetition into synthesis and self-expression
  3. 03Add small experiments to verify theoretical learning

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/hexbee/hello-skills --skill learning-first-principles

After installing, type/learning-first-principlesin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

openai.yaml
references/
analysis_template.md
scripts/
analyze.py

Details

From the project README

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)

Comments

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