mega
CommunityUncertainty-aware reasoning for complex graphs.
AuthorZpankz
Version1.0.0
Installs0
System Documentation
What problem does it solve?
MEGA provides a structured framework to manage and reason over complex knowledge graphs by using bounded n-SuperHyperGraphs with grounded uncertainty, enabling self-refactoring and controlled complexity escalation.
Core Features & Use Cases
- Pareto-governed complexity escalation: start at simple graphs and elevate only when necessary to preserve tractability.
- Grounded uncertainty: plithogenic attributes with confidence, coverage, and source quality.
- Self-refinement: autopoietic loops that bridge gaps, compress redundancy, and expand abstractions when invariants fail.
- Integrations: supports graph (γ), ontolog (ω), hierarchical (η), non-linear (ν), infranodus (ι), and abduct (β) tools for end-to-end PKM workflows.
Quick Start
Provide an initial graph and a query to trigger MEGA's escalation, resulting in a validated, resolved holon.
Dependency Matrix
Required Modules
scipynumpy
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: mega Download link: https://github.com/Zpankz/mcp-skillset/archive/main.zip#mega Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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