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Browser History ACSet
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Compressive Vasomotion Hypothesis (CVH) โ vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taแนhฤ 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
Latched Hyperprior Hypothesis (LHH) โ 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 โ 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 inferenceโlearning landscape write-path, or commit-to-disk of a held prediction.
Showing SKILL.md
| name | browser-history-acset |
| description | Browser History ACSet |
| version | 1.0.0 |
Trit: 0 (ERGODIC - information coordination)
Foundation: PyACSet โ ACSets.jl path equivalence verified
Unified categorical structure for browser history across:
Uses GF(3) trit classification for browsing behavior analysis.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ BrowserHistoryACSet Schema โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
โ Objects: Browser, URL, Visit, Domain, SearchQuery โ
โ โ
โ Morphisms: โ
โ browser_of: URL โ Browser โ
โ domain_of: URL โ Domain โ
โ url_of: Visit โ URL โ
โ from_visit: Visit โ Visit (reflexive, navigation chain) โ
โ โ
โ Attributes: โ
โ browser_name: Browser โ String โ
โ url_text: URL โ String โ
โ visit_time: Visit โ Int โ
โ domain_name: Domain โ String โ
โ trit: Domain โ Int (-1, 0, +1) โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Verified cross-language compatibility between Python and Julia:
| Operation | Python (PyACSet) | Julia (ACSets.jl) | Match |
|---|---|---|---|
| nparts(A) | 2 | 2 | โ |
| subpart(1, :f) | 1 | 1 | โ |
| incident(1, :f) | [1] | [1] | โ |
| path 1โfโg | 1 | 1 | โ |
# Python (PyACSet)
url = acset.subpart(visit_id, "url_of")
domain = acset.path(visit_id, "url_of", "domain_of")
referrers = acset.incident(url_id, "url_of")
# Julia (ACSets.jl)
url = subpart(acs, visit_id, :url_of)
domain = subpart(acs, subpart(acs, visit_id, :url_of), :domain_of)
referrers = incident(acs, url_id, :url_of)
| Trit | Category | Examples | Behavior |
|---|---|---|---|
| +1 | PLUS (Creation) | github.com, ampcode.com, arxiv.org | Building, learning |
| 0 | ERGODIC (Info) | google.com, youtube.com, x.com | Coordination, info |
| -1 | MINUS (Consumption) | amazon.com, netflix.com, reddit.com | Consuming, extracting |
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ Browser History ACSet โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฃ
โ Browser : 3 parts โ
โ URL : 4529 parts โ
โ Visit : 8569 parts โ
โ Domain : 511 parts โ
โ SearchQuery : 36 parts โ
โ Download : 41 parts โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฃ
โ GF(3) Sum : 13 โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Top Domains:
[+] github.com : 1066 visits (creation)
[โ] mermaid.live : 655 visits (coordination)
[+] ampcode.com : 453 visits (creation)
[+] elevenlabs.io : 268 visits (creation)
[+] huggingface.co : 188 visits (creation)
# Extract browser history as ACSet
python3 browser_history_acset.py
# Run path equivalence tests
python3 path_equivalence_test.py
# Julia verification
julia path_equivalence_test.jl
# Select all visits to github.com
github_visits = (
SELECT(ALL("Visit"))
>> FILTER(lambda v: acset.path(v, "url_of", "domain_of")
and acset.subpart(acset.path(v, "url_of", "domain_of"), "domain_name") == "github.com")
)
# Transform: add trit to all URLs in domain
TRANSFORM(
SELECT(ALL("URL")) >> FILTER(lambda u: acset.subpart(u, "domain_of") == d1),
lambda u: acset.set_subpart(u, "trit", 1)
)
browser-history-acset (0) โ olmoearth-mlx (+1) โ tenderloin (-1) = 0 โ
py-acset (0) โ ACSets.jl (+1) โ DuckDB (-1) = 0 โ
coequalizers (0) - Path equivalence via coequalizer quotientsacsets (0) - ACSet foundationstemporal-coalgebra (-1) - Time-based path analysisThis skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
general: 734 citations in bib.duckdbThis skill connects to Software Design for Flexibility (Hanson & Sussman, 2021):
Concepts: autonomous agent, game, synthesis
browser-history-acset (โ) + SDF.Ch10 (+) + [balancer] (โ) = 0
Skill Trit: 0 (ERGODIC - coordination)
Adventure games synthesize techniques. This skill integrates multiple patterns.
This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: โ
Kan Role: Adj
Color: #26D826
The skill participates in triads satisfying:
(-1) + (0) + (+1) โก 0 (mod 3)
This ensures compositional coherence in the Cat# equipment structure.
Based on SOC occupation classification
