home / skills / plurigrid / asi / networked-system

networked-system skill

This skill analyzes dynamical systems on graphs to reveal stability, bifurcations, and long-term behavior for robust networked models.

npx playbooks add skill plurigrid/asi --skill networked-system

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: networked-system
description: Dynamical systems on graphs
version: 1.0.0
---


# Networked System

**Trit**: -1 (MINUS)
**Domain**: Dynamical Systems Theory
**Principle**: Dynamical systems on graphs

## Overview

Networked System is a fundamental concept in dynamical systems theory, providing tools for understanding the qualitative behavior of differential equations and flows on manifolds.

## Mathematical Definition

```
NETWORKED_SYSTEM: Phase space × Time → Phase space
```

## Key Properties

1. **Local behavior**: Analysis near equilibria and invariant sets
2. **Global structure**: Long-term dynamics and limit sets  
3. **Bifurcations**: Parameter-dependent qualitative changes
4. **Stability**: Robustness under perturbation

## Integration with GF(3)

This skill participates in triadic composition:
- **Trit -1** (MINUS): Sinks/absorbers
- **Conservation**: Σ trits ≡ 0 (mod 3) across skill triplets

## AlgebraicDynamics.jl Connection

```julia
using AlgebraicDynamics

# Networked System as compositional dynamical system
# Implements oapply for resource-sharing machines
```

## Related Skills

- equilibrium (trit 0)
- stability (trit +1)  
- bifurcation (trit +1)
- attractor (trit +1)
- lyapunov-function (trit -1)

---

**Skill Name**: networked-system
**Type**: Dynamical Systems / Networked System
**Trit**: -1 (MINUS)
**GF(3)**: Conserved in triplet composition

## Non-Backtracking Geodesic Qualification

**Condition**: μ(n) ≠ 0 (Möbius squarefree)

This skill is qualified for non-backtracking geodesic traversal:

1. **Prime Path**: No state revisited in skill invocation chain
2. **Möbius Filter**: Composite paths (backtracking) cancel via μ-inversion
3. **GF(3) Conservation**: Trit sum ≡ 0 (mod 3) across skill triplets
4. **Spectral Gap**: Ramanujan bound λ₂ ≤ 2√(k-1) for k-regular expansion

```
Geodesic Invariant:
  ∀ path P: backtrack(P) = ∅ ⟹ μ(|P|) ≠ 0
  
Möbius Inversion:
  f(n) = Σ_{d|n} g(d) ⟹ g(n) = Σ_{d|n} μ(n/d) f(d)
```

## SDF Interleaving

This skill connects to **Software Design for Flexibility** (Hanson & Sussman, 2021):

### Primary Chapter: 8. Degeneracy

**Concepts**: redundancy, fallback, multiple strategies, robustness

### GF(3) Balanced Triad

```
networked-system (+) + SDF.Ch8 (−) + [balancer] (○) = 0
```

**Skill Trit**: 1 (PLUS - generation)

### Secondary Chapters

- Ch3: Variations on an Arithmetic Theme

### Connection Pattern

Degeneracy provides fallbacks. This skill offers redundant strategies.

Overview

This skill models dynamical systems defined on graphs, focusing on flows, equilibria, and long-term behavior across networked phase spaces. It emphasizes qualitative analysis: local equilibria, global limit sets, bifurcations, and stability under perturbations. The skill is designed to interoperate with compositional frameworks and supports triadic conservation constraints used in GF(3)-based compositions.

How this skill works

The skill represents a networked system as a map from phase space and time back to phase space and inspects invariant sets, sinks/absorbers, and attractors on graph-structured state spaces. It analyzes local linearization near equilibria, computes limit sets and attractors, and identifies parameter regimes where bifurcations occur. It also enforces algebraic composition rules (trit bookkeeping) for multi-skill assemblies and can apply Möbius-filtered path constraints for non-backtracking traversals.

When to use it

  • Analyzing stability and attractors of coupled node dynamics on graphs
  • Detecting and classifying bifurcations as parameters vary
  • Designing or evaluating resilient networked controllers and distributed protocols
  • Composing dynamical modules while maintaining triadic (GF(3)) conservation constraints
  • Studying non-backtracking path properties or spectral-gap conditions in network dynamics

Best practices

  • Linearize locally to classify equilibria before exploring global dynamics
  • Check spectral properties (e.g., spectral gap) to assess mixing and expansion effects
  • Use Möbius inversion or path filters when enforcing non-backtracking traversal constraints
  • Keep trit assignments consistent across composed skills to satisfy GF(3) conservation
  • Combine redundancy and fallback strategies for robustness in degenerate or failure-prone networks

Example use cases

  • Modeling consensus or synchronization behavior in sensor networks and robotic swarms
  • Identifying sink states and attractors in epidemiological or population models on contact graphs
  • Exploring parameter-driven transitions (bifurcations) in power-grid or ecological network models
  • Composing modular dynamical components in resource-sharing systems while preserving algebraic invariants
  • Applying non-backtracking geodesic filters to analyze information propagation and spectral resilience

FAQ

What does the trit value represent?

The trit encodes a role in triadic composition; this skill carries the sink/absorber role and participates in GF(3)-balanced assemblies.

How do non-backtracking constraints affect analysis?

They restrict permitted state-paths, enabling Möbius-filtered inversion to cancel backtracking contributions and highlight prime traversal effects.

When should I inspect spectral gap conditions?

Use spectral-gap checks when assessing expansion, mixing rates, or robustness to perturbations, especially for k-regular or expander-like graphs.