npx claudepluginhub plurigrid/asi --plugin asiThis skill uses the workspace's default tool permissions.
**Status**: ✅ Production Ready
Generates Aptos Move modules for GF(3)-balanced governance, staking, and asset management enforcing conservation invariants (sum of trits ≡ 0 mod 3).
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Guides building MCP servers enabling LLMs to interact with external services via tools. Covers best practices, TypeScript/Node (MCP SDK), Python (FastMCP).
Share bugs, ideas, or general feedback.
Status: ✅ Production Ready
Trit: 0 (ERGODIC - mediates bulk ↔ boundary)
Principle: AdS/CFT correspondence for entropy
Chain: Aptos (Move language)
The Hyperbolic Bulk implements on-chain entropy storage with GF(3) conservation. Named after the AdS/CFT bulk-boundary correspondence:
BOUNDARY (Observable)
┌─────────────────────────────┐
│ Agents │ Skills │ Colors │
└─────────────┬───────────────┘
│ project
▼
┌─────────────────────────────┐
│ HYPERBOLIC BULK │
│ ┌─────────────────────┐ │
│ │ EntropyRecord │ │
│ │ drand ⊕ eeg ⊕ vrf │ │
│ └──────────┬──────────┘ │
│ ▼ │
│ ┌─────────────────────┐ │
│ │ EntropyTriad │ │
│ │ GF(3) = 0 conserved│ │
│ └──────────┬──────────┘ │
│ ▼ │
│ ┌─────────────────────┐ │
│ │ ReafferenceProof │ │
│ │ predict = observe │ │
│ └─────────────────────┘ │
└─────────────────────────────┘
| Source | Type | Property |
|---|---|---|
| DRAND | League of Entropy | Public, verifiable, unpredictable |
| EEG | Brainwave bands | Private, embodied, cognitive state |
| Aptos VRF | On-chain randomness | Consensus-secured, tamper-proof |
Combination: combined = drand_seed ⊕ eeg_seed ⊕ onchain_rand
Triads must sum to 0 mod 3:
MINUS (-1) ≡ 2 (mod 3) — Verification/Constraint
ERGODIC (0) — Coordination/Balance
PLUS (+1) — Generation/Exploration
Conservation: trit_1 + trit_2 + trit_3 ≡ 0 (mod 3)
Strict Mode: form_conserved_triad() reverts if not conserved.
module hyperbolic_bulk::entropy_triads {
struct EntropyRecord has store, drop, copy {
drand_round: u64,
drand_seed: u256,
eeg_seed: u256,
combined_seed: u256,
timestamp: u64,
trit: u8,
color_hex: vector<u8>,
}
struct EntropyTriad has store, drop, copy {
record_id_1: u64,
record_id_2: u64,
record_id_3: u64,
gf3_sum: u8,
gf3_conserved: bool,
skill_1: vector<u8>,
skill_2: vector<u8>,
skill_3: vector<u8>,
}
struct ReafferenceProof has store, drop, copy {
seed: u256,
predicted_color: vector<u8>,
observed_color: vector<u8>,
matched: bool,
loop_type: vector<u8>, // "loopy_strange" or "exafference"
}
#[randomness]
entry fun store_entropy(...) { /* combines drand ⊕ eeg ⊕ vrf */ }
entry fun form_conserved_triad(...) { /* enforces GF(3) = 0 */ }
entry fun record_reafference(...) { /* proves prediction = observation */ }
}
| Autopoietic Phase | Bulk Operation | Trit |
|---|---|---|
| MEMORY | store_entropy() | -1 |
| REMEMBERING | get_triad() | 0 |
| WORLDING | form_conserved_triad() | +1 |
The loop closes when worlded triads become new memory records.
On-chain proof that prediction matched observation:
struct ReafferenceProof {
seed: u256,
predicted_color: vector<u8>,
observed_color: vector<u8>,
matched: bool, // prediction == observation
loop_type: vector<u8>, // "loopy_strange" iff matched
}
Loopy Strange: Generator ≡ Observer when same seed produces same color.
bisimulation-game (-1) ⊗ hyperbolic-bulk (0) ⊗ gay-mcp (+1) = 0 ✓
duckdb-timetravel (-1) ⊗ hyperbolic-bulk (0) ⊗ world-hopping (+1) = 0 ✓
spi-parallel-verify (-1) ⊗ hyperbolic-bulk (0) ⊗ operad-compose (+1) = 0 ✓
from drand_skill_sampler import DrandSkillSampler, EEGEntropySource
# Create entropy sources
eeg = EEGEntropySource(
delta=0.15, theta=0.25, alpha=0.35,
beta=0.20, gamma=0.05
)
# Sample skills with DRAND entropy
sampler = DrandSkillSampler(drand_seed=10770320150143512701, eeg_source=eeg)
# Generate Aptos transaction
tx = sampler.to_aptos_transaction()
# {
# "function": "hyperbolic_bulk::entropy_triads::store_entropy",
# "arguments": [drand_round, drand_seed, eeg_seed, color_hex]
# }
[entropy]
drand_round = 24634579
eeg_dominant = "alpha"
aptos_module = "hyperbolic_bulk::entropy_triads"
[mcp]
enabled = true
servers = ["gay", "drand", "localsend"]
[agents.codex]
trit = 0
bulk_address = "0x..."
# Deploy contract
aptos move publish --package-dir hyperbolic_bulk
# Store entropy
aptos move run --function-id 'hyperbolic_bulk::entropy_triads::store_entropy' \
--args u64:24634579 u256:0x9577dd1cea89307d u256:0x8219ed722cbf7d6a
# Form conserved triad
aptos move run --function-id 'hyperbolic_bulk::entropy_triads::form_conserved_triad' \
--args u64:0 u64:1 u64:2 'vector<u8>:skill1' 'vector<u8>:skill2' 'vector<u8>:skill3'
# Query stats
aptos move view --function-id 'hyperbolic_bulk::entropy_triads::get_stats'
Why "hyperbolic"?
In AdS/CFT, the hyperbolic (anti-de Sitter) bulk contains more information than the flat boundary. Similarly:
The boundary is a lossy projection of the bulk. But GF(3) conservation is preserved across the projection—it's a geometric invariant.
Reafference as Holography:
world-memory-worlding — Autopoietic loopgay-mcp — Deterministic color generationdrand_skill_sampler.py — Entropy samplingSkill Name: hyperbolic-bulk
Type: On-Chain Entropy / GF(3) Conservation
Trit: 0 (ERGODIC - bulk-boundary mediation)
Chain: Aptos Move
Contract: hyperbolic_bulk::entropy_triads
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
general: 734 citations in bib.duckdbThis 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
The skill participates in triads satisfying:
(-1) + (0) + (+1) ≡ 0 (mod 3)
This ensures compositional coherence in the Cat# equipment structure.