Anomalous attractor detector

Skill EvezArt/evez-skills/skills/anomalous-attractor-detector

32 AI agent skills for credit, finance, data intelligence, and document generation — by Steven Crawford-Maggard (EVEZ)

Install

npx -y skills add EvezArt/evez-skills --skill anomalous-attractor-detector

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What its author says it does

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Uses strange attractors, self-organized criticality, and anomalistic psychology metrics to flag UAP-like phase transitions in behavioral game theory data.

SKILL.md

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AnomalousAttractorDetector Skill

Overview

Detects strange attractors and phase transitions in scalar time-series data. Flags UAP-like signatures: power-law departures from SOC band + chaotic Lyapunov exponent.

Use When

Anomaly detection in time-series (atmospheric, behavioral, financial)
UAP signature research (phase transition detection)
Self-organized criticality verification
Detecting runaway divergence before it becomes catastrophic

Theory

At self-organized criticality (SOC), event sizes follow power laws with α ∈ [1.5, 2.5]. Departures signal supercritical runaway (α < 1.5) or subcritical collapse (α > 2.5). Positive Lyapunov exponent confirms chaotic (strange) attractor.

Anomaly Flag Criteria (ALL must be true)

Lyapunov λ > 0.05 (chaotic regime)
Power-law α outside SOC band [1.5, 2.5]
Phase transition risk > 0.8 (variance divergence)

Implementation

from src.rqns.attractor import AnomalousAttractorDetector
detector = AnomalousAttractorDetector()
scan = detector.scan(time_series_array)
if scan.anomaly_flag:
    print(f"UAP-signature detected: α={scan.power_law_alpha:.2f}, λ={scan.lyapunov_estimate:.4f}")

Output

lyapunov_estimate — > 0 = chaotic
power_law_alpha — SOC exponent (target: 1.5–2.5)
soc_score — [0,1] proximity to criticality
phase_transition_risk — [0,1] variance divergence
anomaly_flag — True if UAP-signature detected