scbe-entropy-dynamics
Monitor and compute entropy, time flow, and quantum state dynamics for the SCBE-AETHERMOORE 7th/8th/9th dimensions. Use when debugging entropy anomalies, time drift, quantum decoherence, or tuning the Ornstein-Uhlenbeck process parameters.
Best use case
scbe-entropy-dynamics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Monitor and compute entropy, time flow, and quantum state dynamics for the SCBE-AETHERMOORE 7th/8th/9th dimensions. Use when debugging entropy anomalies, time drift, quantum decoherence, or tuning the Ornstein-Uhlenbeck process parameters.
Teams using scbe-entropy-dynamics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scbe-entropy-dynamics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scbe-entropy-dynamics Compares
| Feature / Agent | scbe-entropy-dynamics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Monitor and compute entropy, time flow, and quantum state dynamics for the SCBE-AETHERMOORE 7th/8th/9th dimensions. Use when debugging entropy anomalies, time drift, quantum decoherence, or tuning the Ornstein-Uhlenbeck process parameters.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# SCBE Entropy Dynamics Use this skill for reasoning about the three higher-dimensional dynamics (time, entropy, quantum) that govern SCBE-AETHERMOORE system health. ## Three Dynamic Dimensions ### Dimension 7: Time Flow τ̇(t) ``` τ̇(t) = 1.0 + DELTA_DRIFT_MAX · sin(OMEGA_TIME · t) ``` - Normal flow = 1.0 - Oscillates in range [1 - DELTA_DRIFT_MAX, 1 + DELTA_DRIFT_MAX] = [0.5, 1.5] - Period = 60 seconds (OMEGA_TIME = 2π/60) - **Hard constraint**: τ̇ > 0 (causality — time never reverses) - With current parameters, minimum is 0.5 > 0, so causality is always satisfied under normal operation ### Dimension 8: Entropy Flow η̇ ``` η̇ = BETA · (ETA_TARGET - η) + 0.1 · sin(t) ``` - Ornstein-Uhlenbeck mean-reverting drift toward ETA_TARGET = 4.0 - BETA = 0.1 controls reversion speed - Periodic perturbation amplitude = 0.1 - **Bounds**: η must stay within [ETA_MIN=2.0, ETA_MAX=6.0] ### Dimension 9: Quantum State q(t) ``` q(t) = q₀ · e^(-iHt) ``` - Unitary evolution preserves |q| = |q₀| - Phase rotates at rate H (Hamiltonian energy) - **Health checks**: Fidelity f_q ≥ 0.9, Von Neumann entropy S_q ≤ 0.2 ## Shannon Entropy Computation ```python # For the 6D context vector: magnitudes = [|x| if complex else float(x) for x in context_vector] histogram = np.histogram(magnitudes, bins=16, density=True) η = -Σ p · log₂(p + 1e-9) # over non-zero bins ``` - Uses 16 bins for granularity - density=True normalizes to probability distribution - 1e-9 epsilon prevents log(0) ## Key Constants | Constant | Value | Role | |----------------|---------|-------------------------------------| | DELTA_DRIFT_MAX| 0.5 | Max time drift amplitude | | OMEGA_TIME | 2π/60 | Time cycle frequency (1/min) | | BETA | 0.1 | Entropy mean-reversion rate | | ETA_TARGET | 4.0 | Entropy attractor | | ETA_MIN | 2.0 | Entropy floor (QUARANTINE below) | | ETA_MAX | 6.0 | Entropy ceiling (QUARANTINE above) | | KAPPA_ETA_MAX | 0.1 | Max entropy curvature | | DOT_TAU_MIN | 0.0 | Causality floor (τ̇ must exceed) | ## Diagnostic Workflow 1. **Entropy anomaly**: Check if context vector has degenerate components (all same value → low entropy, or uniform random → high entropy). 2. **Time drift**: Verify OMEGA_TIME period matches expected system cycle. Check if external clock sync is causing discontinuities. 3. **Quantum decoherence**: Check if Hamiltonian H is stable. Large H causes fast phase rotation which can reduce fidelity measurements. 4. **Curvature spike**: Compute numerical second derivative of η(t). If |κ_η| > KAPPA_ETA_MAX, the entropy landscape is too volatile. ## Guardrails 1. Entropy computation must handle mixed float/complex arrays gracefully. 2. The O-U process parameters (BETA, ETA_TARGET) are tuned together — changing one requires re-evaluating the other. 3. Quantum evolution must use exact unitary operator, not approximations. 4. Time flow monitoring should raise alerts well before τ̇ approaches 0.
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