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ljg-rank

Decompose a domain into irreducible generators and expose root-rank structure.

Updated Feb 2, 2016
Search Tags:#methodology#explanation#generators#interpretability#domain-analysis#root-rank
One-click install
npx skills add https://github.com/zjykzk/blog --skill ljg-rank-zjykzk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ljg-rank
Source: https://github.com/zjykzk/blog/tree/main/.agents/skills/ljg-rank
Command: npx skills add https://github.com/zjykzk/blog --skill ljg-rank-zjykzk

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you decompose any domain into its irreducible generators, revealing the minimal set of independent drivers that explain observed phenomena. It guides you from surface features to deep root structure, avoiding shallow lists of "key factors."

Core Features & Use Cases

  • Root-rank discovery: identify the minimal, independent generators that can reconstruct observed phenomena.
  • Penetration workflow: perform multi-layer analysis (phenomena -> mechanisms -> fundamental propositions) and validate through recursive down-steps.
  • Operational visuals: generate ASCII sketches of root rank shapes and optional coordinate systems to assist understanding and communication.
  • Use Case: a researcher decomposes a domain to its generators and uses the resulting rank and diagrams to explain the field to students or stakeholders.

Quick Start

Provide a domain description and run the engine to output its root rank and an initial ASCII diagram.

Frequently Asked Questions about ljg-rank

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

FAQPage Schema
How do I decompose a domain into irreducible generators for theory-building?▼

Root-rank discovery identifies the minimal, independent generators that reconstruct observed phenomena. It enforces disciplined analysis by guiding you recursively from surface features down to deep root structure, avoiding shallow lists of key factors.

How does recursive down-sampling work for domain analysis?▼

You provide a domain description and run the engine to output its root rank along with an initial ASCII diagram. The engine then enforces disciplined analysis through recursive down-sampling steps and explicit validation criteria.

What is the best way to visualize root rank structure for stakeholders?▼

Yes, this domain analysis approach suits research, theory-building, and strategic explanation across disciplines. It guides users from observed phenomena to mechanisms to fundamental propositions, making it applicable to any field requiring robust explanations.

Can I use root-rank analysis for strategic explanation across different research disciplines?▼

Domain decomposition into irreducible generators identifies the minimal independent drivers explaining observed phenomena. You provide a domain description, and the engine recursively down-samples from surface features to fundamental propositions.

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