multiversal-finance
Multiversal Finance: Prediction Markets for Interesting Observations
Best use case
multiversal-finance is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Multiversal Finance: Prediction Markets for Interesting Observations
Teams using multiversal-finance 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/multiversal-finance/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How multiversal-finance Compares
| Feature / Agent | multiversal-finance | 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?
Multiversal Finance: Prediction Markets for Interesting Observations
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
# Multiversal Finance: Prediction Markets for Interesting Observations
**Trit**: +1 (PLUS - generative, creates value from attention)
**Color**: #E7B367 (Agent-O-Rama stream, seed 1069)
**Source**: color_at determinism + Schmidhuber compression progress
---
## Core Principle
> **Nothing is stored, everything is bet.**
Predictions are bets on which `(seed, index)` paths yield "interesting" observations.
Rewards flow to observers who correctly predict or discover surprising patterns.
---
## Interestingness Metric (Compression Progress)
Following Schmidhuber's curiosity-driven learning:
```
interestingness(observation) = ΔC = C_before - C_after
```
Where `C` = minimum description length of the observer's world model.
**High interestingness**: observation that *compresses* the model (reveals structure).
**Low interestingness**: observation already predictable (no learning).
---
## ACSet Schema: MultiversalMarket
```julia
@present SchMultiversalMarket(FreeSchema) begin
# Objects
Observation::Ob # A (seed, index) → color witness
Bet::Ob # Prediction on future observation
Agent::Ob # Goblin: -1, 0, or +1
# Morphisms
observes::Hom(Agent, Observation) # who witnessed
predicts::Hom(Bet, Observation) # what was predicted
settles::Hom(Bet, Agent) # who settles (Coordinator)
# Attributes
SeedType::AttrType
IndexType::AttrType
HexType::AttrType
TritType::AttrType
RewardType::AttrType
seed::Attr(Observation, SeedType)
index::Attr(Observation, IndexType)
color::Attr(Observation, HexType)
agent_trit::Attr(Agent, TritType) # -1, 0, +1
stake::Attr(Bet, RewardType)
payout::Attr(Bet, RewardType)
# Interestingness score
compression_delta::Attr(Observation, RewardType)
end
```
---
## Goblin Roles in the Market
| Goblin | Trit | Market Role | Action |
|--------|------|-------------|--------|
| **Agent-O-Rama** | +1 | Proposer | Generates predictions, stakes bets |
| **Coordinator** | 0 | Settlement | Verifies `color_at(seed, index)`, transfers rewards |
| **Shadow Goblin** | -1 | Scorer | Measures compression progress, assigns interestingness |
### GF(3) Conservation in Reward Flow
```
stakes_in + payouts_out + fees ≡ 0 (mod 3)
```
Every bet has a balanced flow: proposer stakes (+1), scorer validates (-1), coordinator settles (0).
---
## Verification Protocol
1. **Proposer** (Agent-O-Rama, +1): Claims "at seed S, index I, color will be C"
2. **Scorer** (Shadow Goblin, -1): Checks `color_at(S, I)` via MCP, computes `ΔC`
3. **Coordinator** (CapTP, 0): If verified, transfers `stake × ΔC` to proposer
```ruby
def settle_bet(bet, scorer, coordinator)
actual = Gay.color_at(bet.seed, bet.index)
if actual == bet.predicted_color
delta_c = scorer.compression_progress(actual)
payout = bet.stake * delta_c
coordinator.transfer(payout, to: bet.proposer)
else
coordinator.transfer(bet.stake, to: scorer) # Penalty
end
end
```
---
## Multiversal Aspect
Different seeds = different possible worlds.
```
World 42: #91BE25 → #... → #... (one derivation path)
World 69: #A3F4B2 → #... → #... (another path)
World 1069: #E7B367 → #... → #... (goblin genesis)
```
**Cross-world bets**: Predict which world has higher average interestingness.
**Arbitrage**: If two worlds have identical color at index N, they share structure.
---
## GF(3) Triads
```
curiosity-driven (-1) ⊗ multiversal-finance (+1) ⊗ captp (0) = 0 ✓ [Reward Transport]
shadow-goblin (-1) ⊗ agent-o-rama (+1) ⊗ coordinator (0) = 0 ✓ [Market Roles]
compression-progress (-1) ⊗ multiversal-finance (+1) ⊗ gay-mcp (0) = 0 ✓ [Interestingness]
```
---
## Implementation Bridge
| Concept | Our System | Function |
|---------|------------|----------|
| Price | `compression_delta` | Higher ΔC = higher reward |
| Liquidity | `interleave(n_streams=3)` | 3 parallel betting pools |
| Settlement | `color_at(seed, index)` | Unforgeable proof |
| Sturdy ref | `(seed, index)` tuple | Bet identifier |
---
## Skills Required
- `gay-mcp`: Deterministic color oracle
- `captp`: Secure settlement transport
- `curiosity-driven`: Compression progress metric
- `world-hopping`: Navigate between seeds (possible worlds)
- `acsets-algebraic-databases`: Market schema
---
## Key Insight
> **The oracle is the market.**
Since `color_at(seed, index)` is deterministic and unforgeable, the prediction market
has *perfect settlement*. No disputes possible—the color either matches or it doesn't.
This is "multiversal" because different seeds explore different derivation paths
through the same generative function. Betting = choosing which multiverse to observe.
## 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
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