Best use case
worldmat-tidar is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
worldmat-tidar
Teams using worldmat-tidar 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
$curl -o ~/.claude/skills/worldmat-tidar/SKILL.md --create-dirs "https://raw.githubusercontent.com/plurigrid/asi/main/plugins/asi/skills/worldmat-tidar/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/worldmat-tidar/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How worldmat-tidar Compares
| Feature / Agent | worldmat-tidar | 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?
worldmat-tidar
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
# worldmat-tidar
> World Matrices via TiDAR Executions: 3×3×3 Parallel Triadic Computation
**Version**: 1.0.0
**Trit**: 0 (ERGODIC - coordinates execution)
**Color**: #55D9A0
## Overview
**Worldmat** is a 3×3×3 matrix of TiDAR executions where:
- **Rows**: MINUS/ERGODIC/PLUS polarities (GF(3) agents)
- **Columns**: PAST/PRESENT/FUTURE temporal phases
- **Depth**: OBSERVATION/ACTION/PREDICTION modalities
Each cell executes the TiDAR pattern:
1. **DIFFUSION**: Draft tokens in parallel (like SplitRng.split)
2. **AR VERIFY**: Verify sequentially (autoregressive)
## Architecture
```
TEMPORAL AXIS
PAST PRESENT FUTURE
↓ ↓ ↓
┌─────────────────────────────┐
│ ┌───┐ ┌───┐ ┌───┐ │
MINUS │ │-1 │ │ 0 │ │+1 │ ← GF(3)=0
│ └───┘ └───┘ └───┘ │
POLARITY │ ┌───┐ ┌───┐ ┌───┐ │
ERGODIC│ │ 0 │ │+1 │ │-1 │ ← GF(3)=0
│ └───┘ └───┘ └───┘ │
│ ┌───┐ ┌───┐ ┌───┐ │
PLUS │ │+1 │ │-1 │ │ 0 │ ← GF(3)=0
│ └───┘ └───┘ └───┘ │
└─────────────────────────────┘
↑ ↑ ↑
GF(3)=0 for each column
```
## Key Properties
| Property | Value | Guarantee |
|----------|-------|-----------|
| **GF(3) Conservation** | All slices sum to 0 | Row, Column, Depth |
| **SPI** | Same seed → Same result | Parallel or Sequential |
| **Spectral Gap** | 0.25 (1/4) | Ergodic mixing |
| **Cells** | 27 | 3³ TiDAR executions |
## TiDAR Pattern (arXiv:2511.08923)
```python
# Phase 1: DIFFUSION (parallel drafting)
def diffusion_draft(self, n_tokens: int = 8):
streams = self.rng.split(n_tokens)
return [stream.next()[0] for stream in streams]
# Phase 2: AR VERIFY (sequential verification)
def ar_verify(self):
prev = self.seed
for token in self.draft_tokens:
verified = mix64(prev ^ token)
self.verified_tokens.append(verified)
prev = verified
```
## Work Stealing
Idle agents steal work from busy agents:
```python
class WorkStealingScheduler:
def steal_work(self, thief: Polarity) -> Optional[TiDARCell]:
busiest = max(self.queues.keys(), key=lambda p: len(self.queues[p]))
if busiest != thief and self.queues[busiest]:
return self.queues[busiest].pop(0)
return None
```
## ACSet Export
```python
wm = Worldmat(master_seed=0x87079c9f1d3b0474)
wm.execute_parallel()
acset = wm.to_acset()
# Returns: {schema, parts, subparts, metadata}
```
## Commands
```bash
# Run demo
python worldmat.py
# Verify SPI
python worldmat.py verify
# Export ACSet
python worldmat.py acset > worldmat.json
```
## GF(3) Triads
```
worldmat-tidar (0) forms balanced triads:
three-match (-1) ⊗ worldmat-tidar (0) ⊗ gay-mcp (+1) = 0 ✓
spi-parallel-verify (-1) ⊗ worldmat-tidar (0) ⊗ triad-interleave (+1) = 0 ✓
tidar_streaming (-1) ⊗ worldmat-tidar (0) ⊗ gay_triadic_exo (+1) = 0 ✓
```
## Integration
### With OpenAI ACSet
```python
from worldmat import Worldmat
from openai_acset import build_openai_acset
# Process conversations through worldmat
wm = Worldmat(master_seed=conv_fingerprint)
wm.execute_parallel()
# Each message → cell in worldmat
# Role (user/assistant/tool) → polarity
# Time → temporal phase
# Type (obs/action/pred) → modality
```
### With Gay-MCP
```python
from gay import SplitMixTernary
# Worldmat colors from Gay-MCP
gen = SplitMixTernary(seed=worldmat.fingerprint())
palette = gen.palette_hex(n=27) # One color per cell
```
## Files
| File | Purpose |
|------|---------|
| `worldmat.py` | Core implementation |
| `SKILL.md` | This documentation |
## References
- TiDAR: arXiv:2511.08923
- Gay.jl/src/spc_repl.jl - Whale synergy matrix
- rio/gayzip/tidar_streaming.py - TiDAR ZIP implementation
- gay_triadic_exo.py - Triadic agent orchestration
Base directory: file:///Users/bob/.claude/skills/worldmat-tidar
## Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
### Graph Theory
- **networkx** [○] via bicomodule
- Universal graph hub
### 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)
```
This ensures compositional coherence in the Cat# equipment structure.Related Skills
We are still matching the closest adjacent skills for this page. In the meantime, continue through the full directory.