plurigrid Effective Parallelism Skill
GF(3)-balanced parallel agent orchestration with operad composition and prediction market allocation.
Core Pattern: Triadic Agent Dispatch
┌─────────────────────────────────────────────────────────┐
│ MINUS (⊖) ERGODIC (⊙) PLUS (⊕) │
│ trit = -1 trit = 0 trit = +1 │
│ ──────────────────────────────────────────────────── │
│ Backfill Verify Live │
│ Cool hue Neutral Warm hue │
│ ACP protocol DuckDB MCP protocol │
│ gravity operad thread operad little_disks │
└─────────────────────────────────────────────────────────┘
Sum mod 3 = 0 → GF(3) CONSERVED ✓
7-Operad Batch Template
For 7 parallel agents, use these operads (sum = 0 mod 3):
| Operad | Trit | Role |
|---|---|---|
| little_disks | +1 | Forward exploration |
| cubes | -1 | Grid traversal |
| cactus | -1 | Tree decomposition |
| thread | 0 | Sequential anchor |
| gravity | -1 | Attraction dynamics |
| modular | +1 | Composable units |
| swiss_cheese | +1 | Boundary handling |
Sum: +1 -1 -1 +0 -1 +1 +1 = 0 ✓
Usage Patterns
Pattern 1: Random Walk with Replacement Check
from concurrent.futures import ThreadPoolExecutor, as_completed
def parallel_skill_walk(skills: list, n_agents: int = 3):
"""Dispatch n_agents over skills with GF(3) balance."""
trits = [-1, 0, +1][:n_agents] # Ensure balance
with ThreadPoolExecutor(max_workers=n_agents) as executor:
futures = {
executor.submit(agent_task, skills, trit): trit
for trit in trits
}
for future in as_completed(futures):
yield future.result()
Pattern 2: Prediction Market Allocation
def allocate_by_probability(contracts: list, budget: float):
"""Allocate budget proportional to contract probabilities."""
total_p = sum(c.current_price for c in contracts)
return {
c.id: budget * (c.current_price / total_p)
for c in contracts
}
Pattern 3: 23³ Orthogonal Grid Walk
DIM = 23 # Prime for collision avoidance
def hash_to_cell(name: str) -> tuple:
"""Map skill name to 23³ grid cell."""
x = hash(name + "x") % DIM
y = hash(name + "y") % DIM
z = hash(name + "z") % DIM
return (x, y, z)
DuckDB Integration
Query Nov2025 tables for synergy-informed dispatch:
-- Top ego nodes for agent assignment
SELECT ego, COUNT(*) as n_alters, AVG(synergy_score) as avg_synergy
FROM gaymc_diffusion
GROUP BY ego ORDER BY n_alters DESC LIMIT 15;
-- Temporal evolution for scheduling
SELECT month_bin, n_threads, mean_hue FROM gay_equiv_temporal
ORDER BY month_bin DESC;
-- Verification status
SELECT verification_status, hue_entropy_normalized
FROM gay_solomonoff_verification LIMIT 1;
Skill Dispatch Rules
- Always check GF(3) sum before spawning agents
- Use ThreadPoolExecutor with max_workers = 3, 7, or 12 (balanced batches)
- Assign protocols by trit: MCP (+1), DuckDB (0), ACP (-1)
- Color-code agents from Gay.jl seed 42069 palette
- Track boredom: If agent revisits 3+ times, expand skill pool
Gay.jl Palette (seed 42069)
COLORS = ["#28C3BF", "#DDB562", "#AC2A5A", "#A55936", "#5A8C3E", "#7B68EE", "#FF6B6B"]
Verification Command
# Check GF(3) balance of any trit list
python3 -c "print(sum([-1, 0, +1, +1, -1, +1, -1]) % 3)" # Should be 0
End-of-Skill Interface
Related Skills
tripartite-decompositions- GF(3) structured decompositionsparallel-fanout- Maximum synergistic parallelismtriad-interleave- Interleave three color streamsspi-parallel-verify- Strong Parallelism Invariance verificationentropy-sequencer- Interaction interleaving for max info gain
Autopoietic Marginalia
The interaction IS the skill improving itself.
Every use of this skill is an opportunity for worlding:
- MEMORY (-1): Record what was learned
- REMEMBERING (0): Connect patterns to other skills
- WORLDING (+1): Evolve the skill based on use
Add Interaction Exemplars here as the skill is used.
Para(Optic) atlas
Part of: para-mensch-commons.