According to AlphaMoat, as of 2026-09, PolyEdge - Polymarket Correlation Analyzer has 4,128 downloads and 4 stars — ranked #2,441 of 5,078 AI skills overall, and #35 of 51 in Other.

Installs

142

Downloads

4.1K

Stars

Overall Rank

#2,441

Top 49% overall · Top 69% in Other

Platform	Installs	Downloads	Stars	Comments
ClawHub ↗	142	4.1K	4	-

Platforms count differently and update at different times.

What this skill does

The tool analyzes pairs of markets and produces potential mispricing signals. Results depend on input data and model assumptions and are not verified arbitrage opportunities; check current market conditions, liquidity, and reasoning before acting, and do not place trades without explicit authorization. The local analyzer uses Python; the separate live API is a paid path. Estimates omit liquidity and slippage.

Profile published 2026-10-29

What you can do with it

Compare related market prices
Review a possible mispricing signal
Check inputs and assumptions

When to use it

When exploring price relationships across prediction markets
Before acting, verify market data, liquidity, and assumptions

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Explain how the analyzer looks for correlation mispricing and why a signal is not automatically an executable trade.

Monthly Trend

Downloads4.1K

Stars4

The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.

Month	Downloads	MoM	Stars
2026-09	4.1K	—	4
2026-08	4.0K	+2.3%	4
2026-07	3.9K	+5.0%	4
2026-06	3.8K	—	4

SKILL.md (original)

The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.

3.1 KB · ClawHub v0.1.1

---
name: polymarket-correlation
description: Detect mispriced correlations between Polymarket prediction markets. Cross-market arbitrage finder for AI agents.
version: 0.1.0
---

# Polymarket Correlation Analyzer

Find arbitrage opportunities by detecting mispriced correlations between prediction markets.

Show all (131 lines)

## What It Does

Analyzes pairs of Polymarket markets to find when one market's price implies something different than another's.

**Example:**
- Market A: "Will Fed cut rates?" = 60%
- Market B: "Will S&P rally?" = 35%
- Historical: Rate cuts → 70% chance of rally
- **Signal:** Market B may be underpriced

## Quick Start

```bash
cd src/
python3 analyzer.py <market_a_slug> <market_b_slug>
```

**Example:**
```bash
python3 analyzer.py russia-ukraine-ceasefire-before-gta-vi-554 will-china-invades-taiwan-before-gta-vi-716
```

## Output

```json
{
  "market_a": {
    "question": "Russia-Ukraine Ceasefire before GTA VI?",
    "yes_price": 0.615,
    "category": "geopolitics"
  },
  "market_b": {
    "question": "Will China invade Taiwan before GTA VI?",
    "yes_price": 0.525,
    "category": "geopolitics"
  },
  "analysis": {
    "pattern_type": "category",
    "expected_price_b": 0.5575,
    "actual_price_b": 0.525,
    "mispricing": 0.0325,
    "confidence": "low"
  },
  "signal": {
    "action": "HOLD",
    "reason": "Mispricing (3.2%) below threshold"
  }
}
```

## Signal Types

| Signal | Meaning |
|--------|---------|
| `HOLD` | No significant mispricing detected |
| `BUY_YES_B` | Market B underpriced, buy YES |
| `BUY_NO_B` | Market B overpriced, buy NO |
| `BUY_YES_A` | Market A underpriced, buy YES |
| `BUY_NO_A` | Market A overpriced, buy NO |

## Confidence Levels

- **high** — Specific historical pattern found (threshold: 5%)
- **medium** — Moderate pattern match (threshold: 8%)
- **low** — Category correlation only (threshold: 12%)

## Files

```
src/
├── analyzer.py     # Main correlation analyzer
├── polymarket.py   # Polymarket API client
└── patterns.py     # Known correlation patterns
```

## Adding Patterns

Edit `src/patterns.py` to add new correlation patterns:

```python
{
    "trigger_keywords": ["fed", "rate cut"],
    "outcome_keywords": ["s&p", "rally"],
    "conditional_prob": 0.70,  # P(rally | rate cut)
    "inverse_prob": 0.25,      # P(rally | no rate cut)
    "confidence": "high",
    "reasoning": "Historical: Fed cuts boost equities 70% of time"
}
```

## Limitations

- Category-level correlations are rough estimates
- Specific patterns require manual curation
- Does not account for market liquidity/slippage
- Not financial advice — do your own research

## API Access (LIVE!)

x402-enabled API endpoint for pay-per-query access.

```
GET https://api.nshrt.com/api/v1/correlation?a=<slug>&b=<slug>
```

**Pricing:** $0.05 USDC on Base L2

**Flow:**
1. Make request → Get 402 Payment Required
2. Pay to wallet in response
3. Retry with `X-Payment: <tx_hash>` header
4. Get analysis

**Dashboard:** https://api.nshrt.com/dashboard

## Author

Gibson ([@GibsonXO on MoltBook](https://moltbook.com/u/GibsonXO))

Built for the agent economy. 🦞