用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/plurigrid/asi --skill cantordust-viz命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
Latched Hyperprior Hypothesis (LHH) — 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 — 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 inference→learning landscape write-path, or commit-to-disk of a held prediction.
正在显示 SKILL.md
基于 SOC 职业分类
| name | cantordust-viz |
| description | Binary visualization for human pattern recognition - Ghidra plugin by Chris Domas (xoreaxeaxeax) |
| metadata | {"trit":-1,"color":"#E54951","gf3_role":"MINUS","version":"1.0.0","repo":"Battelle/cantordust","author":"Chris Domas (xoreaxeaxeax)","interface_ports":["References"]} |
Use when embeddings fail: humans see patterns algorithms miss.
Visual binary analysis tool for Ghidra. Converts binary data to bitmaps/visualizations where structural patterns become visible to human pattern recognition.
cantordust-viz (-1) ⊗ skill-embedding-vss (0) ⊗ radare2-hatchery (+1) = 0 ✓
| Tool | Approach | Strength |
|---|---|---|
| Cantordust | Visual/human | Sees patterns ML misses |
| Zignatures | Soft signatures | Fuzzy matching + keyspace reduction |
| skill-embedding-vss | MLX embeddings | O(1) similarity at scale |
git clone https://github.com/Battelle/cantordust.git
# Add to Ghidra Script Manager
From xoreaxeaxeax's work:
| Repo | Stars | Category |
|---|---|---|
| movfuscator | 10,075 | obfuscation |
| sandsifter | 4,998 | hardware security |
| rosenbridge | 2,380 | hardware backdoors |
| REpsych | 1,031 | anti-RE |
# When embeddings show high similarity but you want visual confirmation
from cantordust import visualize_binary
from skill_embedding_vss import SkillEmbeddingVSS
vss = SkillEmbeddingVSS('/path/to/skills')
similar = vss.find_nearest('target', k=5)
# Visual confirm top matches
for name, dist in similar[:3]:
visualize_binary(f'/path/to/{name}') # Human reviews
# cantordust_gay_bridge.jl connects:
# 1. Cantordust 2-tuple byte pair visualization
# 2. CJ Carr spectral features (diffusion transformers)
# 3. Gay.jl deterministic coloring (SPI)
result = analyze_binary_with_gay("target.bin")
# Returns: matrix, diagonal_score, ascii_score, trit_sum, sample_colors
| Domain | Representation | Gay.jl Mapping |
|---|---|---|
| Binary (Cantordust) | 2-tuple → 256×256 | entropy → trit → color |
| Audio (CJ Carr) | Mel spectrogram | centroid/flatness → HSL |
| Color (Gay.jl) | SplitMix64 + golden angle | SPI deterministic |