Attractor

SkillDev tools

Invariant set attracting nearby trajectories

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Attractor skill

What this skill tells your AI

The instructions your AI receives, as published by plurigrid/asi in skills/attractor/SKILL.md and read by ahel’s review.

Trit: 1 (PLUS) Domain: Dynamical Systems Theory Principle: Invariant set attracting nearby trajectories

Overview

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

Mathematical Definition

ATTRACTOR: 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 (PLUS): Sources/generators
  • Conservation: Σ trits ≡ 0 (mod 3) across skill triplets

AlgebraicDynamics.jl Connection

using AlgebraicDynamics

# Attractor 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: attractor Type: Dynamical Systems / Attractor Trit: 1 (PLUS) 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)

Signals

GitHub stars
63
Forks
12
Last commit
Jul 2026
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attractor
Source
github.com/plurigrid/asi