Invariant set repelling nearby trajectories
Scanned 9/2/2026
Install to Claude Code
npx -y skills add majiayu000/claude-skill-registry --skill repeller --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: repeller
description: Invariant set repelling nearby trajectories
version: 1.0.0
---
# Repeller
**Trit**: -1 (MINUS)
**Domain**: Dynamical Systems Theory
**Principle**: Invariant set repelling nearby trajectories
## Overview
Repeller is a fundamental concept in dynamical systems theory, providing tools for understanding the qualitative behavior of differential equations and flows on manifolds.
## Mathematical Definition
```
REPELLER: 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
# Repeller 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**: repeller
**Type**: Dynamical Systems / Repeller
**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)
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