القائمة
Category-theoretic graph rewriting with DPO, SPO, and SqPO pushouts for C-Sets. Declarative transformation of acset data structures.
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 algebraic-rewritingيبقى 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.
عرض SKILL.md
استنادا إلى تصنيف SOC المهني
| name | algebraic-rewriting |
| description | Category-theoretic graph rewriting with DPO, SPO, and SqPO pushouts for C-Sets. Declarative transformation of acset data structures. |
| metadata | {"trit":-1,"color":"#0BAD20"} |
AlgebraicRewriting.jl is a Julia library for performing category-theoretic rewrites over C-Sets and other Catlab.jl data structures.
| Type | Description | Use Case |
|---|---|---|
| DPO | Double Pushout | Safe deletion (no dangling edges) |
| SPO | Single Pushout | Greedy deletion |
| SqPO | Sesqui-Pushout | Cloning + deletion |
A rewrite rule consists of:
using AlgebraicRewriting
# Define a rule: merge two vertices
L = @acset Graph begin V=2; E=1; src=[1]; tgt=[2] end
K = @acset Graph begin V=1 end
R = @acset Graph begin V=1 end
rule = Rule(L, K, R)
G = @acset Graph begin
V = 4
E = 3
src = [1, 2, 3]
tgt = [2, 3, 4]
end
# Find matches and rewrite
matches = homomorphisms(L, G)
G′ = rewrite(rule, G, matches[1])
L ←─ K ─→ R
↓ ↓ ↓
G ←─ D ─→ H
The context D ensures no "dangling edges" after deletion.
Supports cloning via the final pullback complement:
# Clone a vertex
L = @acset Graph begin V=1 end
K = @acset Graph begin V=1 end
R = @acset Graph begin V=2 end
clone_rule = Rule(L, K, R; type=:SqPO)
# sRGB boundary learning with rewriting seed
gay_seed!(0xabfca37b6b4bc699)
# Forward mode autodiff
∂params = Enzyme.gradient(Forward, loss, params, seed)
0xabfca37b6b4bc699algebraic-rewriting (-1) ⊗ acsets-hatchery (0) ⊗ gay-monte-carlo (+1) = 0 ✓
acsets-hatchery - ACSet data structurestopos-adhesive-rewriting - Adhesive categoriesdpo-rewriting - Graph transformationworld-a - AlgebraicJulia ecosystemPart of: para-mensch-commons.