Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add plurigrid/asi --skill oriented-simplicial-networksgit clone --depth 1 https://github.com/plurigrid/asiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/plurigrid/asi/oriented-simplicial-networks)<a href="https://agentmods.dev/skills/plurigrid/asi/oriented-simplicial-networks"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/oriented-simplicial-networks/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/plurigrid/asi/oriented-simplicial-networks"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/oriented-simplicial-networks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00620 |
| Opus 5 | $0.00000 | $0.00310 |
| Sonnet 5 | $0.00000 | $0.00124 |
| Haiku 4.5 | $0.00000 | $0.00062 |
Grade A, and why
oriented-simplicial-networks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Oriented Simplicial Networks
Category: Phase 3 Core - Geometric Deep Learning
Status: Skeleton Implementation
Dependencies: categorical-composition, persistent-homology
Overview
Implements directional simplicial neural networks (Dir-SNNs) with asymmetric message passing operators, E(n)-equivariance constraints, and persistent homology tracking for topological feature learning.
Capabilities
- Directional Message Passing: Asymmetric operators respecting simplex orientation
- E(n)-Equivariance: Rotation/translation invariant representations
- Persistent Homology: Track topological features during training
- Simplicial Attention: Higher-order attention mechanisms on simplicial complexes
Core Components
-
Simplicial Complex Builder (
simplicial_complex.jl)- Construct oriented simplicial complexes from data
- Boundary operator computation
- Coboundary and Laplacian matrices
-
Dir-SNN Layers (
dirsnn_layers.jl)- Asymmetric message passing on simplices
- E(n)-equivariant convolutions
- Higher-order pooling operators
-
Persistent Homology Tracker (
persistent_homology.jl)- Compute persistence diagrams during forward pass
- Track birth/death of topological features
- Bottleneck/Wasserstein distance metrics
-
Training Loop (
train_dirsnn.jl)- Integration with Flux.jl
- Topologically-aware loss functions
- Gradient flow on simplicial manifolds
Integration Points
- Input from:
sheaf-theoretic-coordination(sheaf structures on simplicial complexes) - Output to:
categorical-composition(functorial network composition) - Coordinates with:
formal-verification-ai(verify topological invariants)
Usage
using OrientedSimplicialNetworks
# Build simplicial complex from point cloud
complex = SimplicialComplex(points, max_dimension=2)
# Create Dir-SNN model
model = DirSNN([
SimplicialConv(in_features=3, out_features=16, dimension=0),
SimplicialConv(in_features=16, out_features=32, dimension=1),
SimplicialPooling(dimension=1)
])
# Train with persistent homology tracking
train!(model, complex, labels; track_topology=true)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 78 lines · 0 tokens per session scan A 3a9d4441943f
oriented-simplicial-networks is a skill published in the GitHub repository plurigrid/asi (62 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 620 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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