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 spectral-random-walkergit 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/spectral-random-walker)<a href="https://agentmods.dev/skills/plurigrid/asi/spectral-random-walker"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/spectral-random-walker/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/spectral-random-walker"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/spectral-random-walker.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 | $0.00693 |
| Opus 5 | $0.00000 | $0.00347 |
| Sonnet 5 | $0.00000 | $0.00139 |
| Haiku 4.5 | $0.00000 | $0.00069 |
Grade A, and why
spectral-random-walker 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spectral Random Walker
Category: Theorem Discovery + Comprehension Type: Random Walk Analysis Language: Julia Status: Production Ready Version: 1.0.0 Date: December 22, 2025
Overview
Integrates spectral gaps with random walk theory using the Benjamin Merlin Bumpus comprehension model. Samples proof space via random walks to discover related theorems through co-visitation patterns, enabling "comprehension neighborhoods" - clusters of theorems that are naturally explored together.
Key Data Structures
struct RandomWalkAnalysis
start_node::Int
current_node::Int
visited_path::Vector{Int}
visit_counts::Dict{Int, Int}
transition_count::Int
stationary_approximation::Dict{Int, Float64}
end
Key Functions
estimate_mixing_time(gap, n_nodes): Mixing time from spectral gapsimulate_random_walk(adjacency, start, steps): Uniform neighbor selectionsample_proof_paths(adjacency, num_samples): Metropolis-Hastings samplingcomprehension_discovery(adjacency, gap): Co-visitation clusteringgenerate_random_walk_report(): Analysis report generation
Mathematical Foundation
Benjamin Merlin Bumpus Comprehension Model
Three perspectives on proof connectivity:
- Spectral (gap): "How optimal?" - measures expansion property
- Combinatorial (Möbius): "Where tangled?" - identifies problem paths
- Probabilistic (random walks): "How explore?" - discovery mechanism
Mixing Time Theory
mixing_time ≈ log(n) / spectral_gap
High gap → Fast mixing → Easy theorem discovery
Low gap → Slow mixing → Tangled dependencies impede exploration
Co-visitation Matrix
- Records theorems frequently reached together in random walks
- Cluster via 75th percentile threshold
- Forms "comprehension regions" - natural theorem groupings
Usage
using SpectralRandomWalk
# 1. Check system health
gap = SpectralAnalyzer.analyze_all_provers()["lean4"]
# 2. Estimate exploration time
mixing_time = estimate_mixing_time(gap, n_theorems)
# 3. Sample comprehension regions
comprehension = comprehension_discovery(adjacency, gap)
# 4. Discover related theorems
region = comprehension["comprehension_regions"][theorem_id]
related = sample(region, 10) # 10 related theorems
What ships with it
1 file 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 · 93 lines · 0 tokens per session scan A 835de2214478
spectral-random-walker 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 693 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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