Best use case
browser-history-acset is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Browser History ACSet
Teams using browser-history-acset 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/browser-history-acset/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How browser-history-acset Compares
| Feature / Agent | browser-history-acset | 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?
Browser History ACSet
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
# Browser History ACSet
**Trit**: 0 (ERGODIC - information coordination)
**Foundation**: PyACSet ↔ ACSets.jl path equivalence verified
## Overview
Unified categorical structure for browser history across:
- ChatGPT Atlas (Chromium-based)
- Chrome, Arc, Brave, Firefox, Safari
Uses GF(3) trit classification for browsing behavior analysis.
## Schema
```
┌─────────────────────────────────────────────────────────────┐
│ BrowserHistoryACSet Schema │
├─────────────────────────────────────────────────────────────┤
│ Objects: Browser, URL, Visit, Domain, SearchQuery │
│ │
│ Morphisms: │
│ browser_of: URL → Browser │
│ domain_of: URL → Domain │
│ url_of: Visit → URL │
│ from_visit: Visit → Visit (reflexive, navigation chain) │
│ │
│ Attributes: │
│ browser_name: Browser → String │
│ url_text: URL → String │
│ visit_time: Visit → Int │
│ domain_name: Domain → String │
│ trit: Domain → Int (-1, 0, +1) │
└─────────────────────────────────────────────────────────────┘
```
## Path Equivalence Tests
Verified cross-language compatibility between Python and Julia:
| Operation | Python (PyACSet) | Julia (ACSets.jl) | Match |
|-----------|------------------|-------------------|-------|
| nparts(A) | 2 | 2 | ✓ |
| subpart(1, :f) | 1 | 1 | ✓ |
| incident(1, :f) | [1] | [1] | ✓ |
| path 1→f→g | 1 | 1 | ✓ |
### Key Operations
```python
# 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
# 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)
```
## GF(3) Domain Classification
| 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 |
## Current Data (ChatGPT Atlas)
```
╔═══════════════════════════════════════════════════════════════╗
║ Browser History ACSet ║
╠═══════════════════════════════════════════════════════════════╣
║ Browser : 3 parts ║
║ URL : 4529 parts ║
║ Visit : 8569 parts ║
║ Domain : 511 parts ║
║ SearchQuery : 36 parts ║
║ Download : 41 parts ║
╠═══════════════════════════════════════════════════════════════╣
║ GF(3) Sum : 13 ║
╚═══════════════════════════════════════════════════════════════╝
Top Domains:
[+] github.com : 1066 visits (creation)
[○] mermaid.live : 655 visits (coordination)
[+] ampcode.com : 453 visits (creation)
[+] elevenlabs.io : 268 visits (creation)
[+] huggingface.co : 188 visits (creation)
```
## Usage
```bash
# 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
```
## Integration Points
- **Tenderloin WEV**: Geographic browsing patterns → impact zones
- **OlmoEarth-MLX**: Location-aware embeddings for browsing
- **GeoACSet**: Spatial categorization of online activity
- **DuckDB**: Temporal queries on visit history
## Specter-Style Navigation
```python
# 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)
)
```
## Canonical Triads
```
browser-history-acset (0) ⊗ olmoearth-mlx (+1) ⊗ tenderloin (-1) = 0 ✓
py-acset (0) ⊗ ACSets.jl (+1) ⊗ DuckDB (-1) = 0 ✓
```
## References
- [ACSets.jl](https://github.com/AlgebraicJulia/ACSets.jl)
- [plurigrid-asi-skillz/skills/acsets](file:///Users/bob/ies/plurigrid-asi-skillz/skills/acsets/SKILL.md)
- [zip_acset_skill/extract_agent_o_rama.py](file:///Users/bob/ies/zip_acset_skill/extract_agent_o_rama.py)
## Related Skills
- `coequalizers` (0) - Path equivalence via coequalizer quotients
- `acsets` (0) - ACSet foundations
- `temporal-coalgebra` (-1) - Time-based path analysis
## Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
### Annotated Data
- **anndata** [○] via bicomodule
### Bibliography References
- `general`: 734 citations in bib.duckdb
## Cat# Integration
This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure:
```
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
```
### GF(3) Naturality
The skill participates in triads satisfying:
```
(-1) + (0) + (+1) ≡ 0 (mod 3)
```
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