القائمة
Koopman operator theory for infinite-dimensional linear lifting of nonlinear dynamics. Generates dynamics from observables.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/plurigrid/asi --skill koopman-generatorيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
Latched Hyperprior Hypothesis (LHH) — a sustained vascular contraction engages the smooth-muscle latch-bridge, durably freezing a circuit as a committed hyperprior isolated from global updating; unlatches when its prediction resolves. Use when modeling durable commitments, trauma/PTSD as cemented priors, latch spirals, or the cross-substrate latch (mechanical/bioelectric/immune/sheaf).
Synaptic substrate of biological active inference — long-term potentiation/depression (LTP/LTD) writes priors into synaptic weights = the learning landscape (Deep CANALs). A held vascular latch annealed long enough crystallizes into a neuron prior. Use when modeling consolidation, neuron priors, the inference→learning landscape write-path, or commit-to-disk of a held prediction.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | koopman-generator |
| description | Koopman operator theory for infinite-dimensional linear lifting of nonlinear dynamics. Generates dynamics from observables. |
| source | Brunton+Kutz+Mezić + music-topos |
| license | MIT |
| trit | 1 |
The Koopman operator K linearizes nonlinear dynamics by lifting to infinite-dimensional observable space:
State space (nonlinear) Observable space (linear)
x_{t+1} = f(x_t) → (Kg)(x) = g(f(x))
Key property: K is linear even when f is nonlinear.
DMD finds finite-rank approximation of K:
K ≈ Φ Λ Φ†
Koopman = natural transformation on observable presheaves:
# Observable functor
F: StateSpace → ObservableSpace
# Koopman as pushforward
K = f_*: Sh(X) → Sh(X)
dmd-spectral (-1) ⊗ structured-decomp (0) ⊗ koopman-generator (+1) = 0 ✓
temporal-coalgebra (-1) ⊗ acsets (0) ⊗ koopman-generator (+1) = 0 ✓