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 sheaf-theoretic-coordinationgit 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/sheaf-theoretic-coordination)<a href="https://agentmods.dev/skills/plurigrid/asi/sheaf-theoretic-coordination"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/sheaf-theoretic-coordination/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/sheaf-theoretic-coordination"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/sheaf-theoretic-coordination.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00670 |
| Opus 5 | $0.00000 | $0.00335 |
| Sonnet 5 | $0.00000 | $0.00134 |
| Haiku 4.5 | $0.00000 | $0.00067 |
Grade A, and why
sheaf-theoretic-coordination 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 8d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sheaf-Theoretic Coordination
Category: Phase 3 Core - Distributed Reasoning
Status: Skeleton Implementation
Dependencies: oriented-simplicial-networks, categorical-composition
Overview
Implements sheaf-theoretic coordination mechanisms for multi-agent systems, using sheaf Laplacians for consensus, harmonic extension for inference, and cohomology for detecting global obstructions.
Capabilities
- Sheaf Laplacian: Consensus dynamics on cellular sheaves
- Harmonic Extension: Infer missing data via global consistency
- Cohomology Detection: Identify obstructions to global agreement
- Sheaf Neural Networks: Learn sheaf structures from data
Core Components
-
Cellular Sheaf Builder (
cellular_sheaf.jl)- Construct sheaves over cell complexes
- Define restriction maps between stalks
- Compute sheaf cohomology groups
-
Sheaf Laplacian (
sheaf_laplacian.jl)- Weighted Laplacian on sheaf sections
- Consensus dynamics and heat flow
- Spectral analysis for convergence
-
Harmonic Extension (
harmonic_extension.jl)- Solve for globally consistent assignments
- Handle partial observations
- Regularized least-squares formulation
-
Sheaf Neural Networks (
sheaf_nn.jl)- Learn restriction maps via gradient descent
- Sheaf diffusion layers
- Integration with geometric deep learning
Integration Points
- Input from:
oriented-simplicial-networks(base simplicial complex) - Output to:
emergent-role-assignment(coordination constraints) - Coordinates with:
categorical-composition(sheaf functoriality)
Usage
using SheafTheoreticCoordination
# Build cellular sheaf over graph
graph = SimplexGraph(adjacency_matrix)
sheaf = CellularSheaf(graph, stalk_dim=3)
# Define restriction maps (can be learned)
for edge in edges(graph)
sheaf.restrictions[edge] = random_orthogonal_matrix(3)
end
# Solve for harmonic extension (inference)
partial_observations = Dict(1 => [1.0, 0.0, 0.0], 5 => [0.0, 1.0, 0.0])
global_assignment = harmonic_extension(sheaf, partial_observations)
# Check for cohomological obstructions
obstruction = compute_obstruction_cocycle(sheaf, global_assignment)
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.
- 8d ago First seen · 81 lines · 0 tokens per session scan A 3cd57ffa271c
sheaf-theoretic-coordination is a skill published in the GitHub repository plurigrid/asi (64 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 670 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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