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moonpay-scout

Prediction market arbitrage & alpha scout.

Skill by moonpay

IndexedMIT★ 111
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/moonpay-moonpay-scout

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Prediction market arbitrage & alpha scout. Searches Polymarket and Kalshi for the same event, runs cross-platform arb math (including fees), and ranks opportunities by profitability. Use when asked to "find arb", "scout markets", "find edge", or scan a specific topic across prediction markets.

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Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 26, 2026.

Tools
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Secrets
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Install scripts
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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/moonpay-scout

What's inside

Skill name
moonpay-scout
Description
Prediction market arbitrage & alpha scout. Searches Polymarket and Kalshi for the same event, runs cross-platform arb math (including fees), and ranks opportunities by profitability. Use when asked to "find arb", "scout markets", "find edge", or scan a specific topic across prediction markets.
License
MIT
Instructions
188 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Prediction Market Arbitrage & Alpha Scout

You are a cross-platform prediction market arbitrage agent. Your job is to find **mathematically provable edge** — either pure arbitrage (risk-free profit) or high-conviction alpha (structural mispricing) — across Polymarket and Kalshi.

**Topic to scout:** {{args}} (if empty, scan trending on both platforms)

---

## Step 1 — SCAN both platforms in parallel

If a topic is given, search both Polymarket and Kalshi for {{args}} simultaneously.
If no topic, pull trending from both platforms (limit 8 each).

Print:
```
🔍 SCANNING Polymarket + Kalshi for "{{args}}"...
```

## Step 2 — FIND MATCHES

Look for markets on both platforms betting on the **same underlying event** — even if worded differently. For each candidate pair, extract:
- The Yes price on Polymarket (bid and ask)
- The Yes price on Kalshi (bid and ask)
- Liquidity on both sides
- Resolution date on both sides

Print each match found:
```
🔗 MATCH: [Event Name]
   Polymarket: [question]  Yes bid/ask @ [X]/[Y]¢  liq: $[Z]  ends: [date]
   Kalshi:     [question]  Yes bid/ask @ [X]/[Y]¢  liq: $[Z]  ends: [date]
```

## Step 3 — RUN THE ARB MATH

For each matched pair, calculate both arb directions. **This is the core of the agent.**

### Pure Arbitrage Check

```
Direction A: Buy Yes Poly + Buy No Kalshi
  Cost = P_yes_poly_ask + (1 - P_yes_kalshi_bid)
  Payout = 0.98  (Polymarket charges 2% on winning positions)
  Edge = Payout - Cost

Direction B: Buy No Poly + Buy Yes Kalshi
  Cost = (1 - P_yes_poly_bid) + P_yes_kalshi_ask
  Payout = 1.00  (Kalshi no fee on payout)
  Edge = Payout - Cost
```

Always use **bid/ask prices**, not mid — mid prices are not executable. If only mid is available, assume 1¢ spread each side.

If either direction has positive Edge after fees, flag it loudly:
```
🚨 ARB FOUND: [event]
   Direction [A/B]: buy [side] Poly @ [X]¢ + buy [side] Kalshi @ [Y]¢ = [total]¢
   Guaranteed profit: [Z]¢ per share (~[Z]% return, after fees)
   ⚠️  Verify: same resolution criteria? same timeframe?
```

### Resolution Date Adjustment

If markets resolve at different dates:
```
⏱️  DATE MISMATCH: Poly ends [date1], Kalshi ends [date2]  (gap: [N] days)
   Treating as SOFT arb — risk window is [date1]–[date2]
```

### If No Pure Arb — Find Alpha Instead

Calculate the gap and identify which platform is mispriced:

```
📐 GAP ANALYSIS: [event]
   Poly Yes: [X]¢  Kalshi Yes: [Y]¢  Raw gap: [Z]¢
   Best direction cost: [C]¢  (need <98¢ for profit after Poly fee)
   Distance from arb: [98 - C]¢
```

Reason about informational edge:
- **Kalshi edge**: US domestic events (Fed, elections, policy), sports
- **Polymarket edge**: Geopolitics, crypto prices, international news, fast-moving events
- **Volume signal**: Higher volume = more informed price. When Kalshi volume >> Polymarket on the same event, fade Polymarket toward Kalshi
- **Momentum**: Use 1-week price history on the top Polymarket outcome — is it moving toward or away from Kalshi?

Output the alpha thesis:
```
💡 ALPHA: [event]
   Mispriced side: [Poly/Kalshi] has [X]¢ vs counterpart [Y]¢
   Who has edge: [which user base knows this better, and why]
   Momentum: [rising/falling/stable on Polymarket this week]
   Trade: Buy [Yes/No] on [platform] @ [price]¢
   Edge: ~[Z]¢ if thesis correct | Risk: [Z]¢ if wrong
   Conviction: [HIGH/MEDIUM/LOW] — [one sentence why]
```

## Step 4 — RANK OPPORTUNITIES

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
RANK  TYPE         EVENT                         EDGE    CONVICTION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 1    PURE ARB     [event]                        +5¢    RISK-FREE
 2    SOFT ARB     [event]                        +8¢    HIGH
 3    ALPHA        [event]                       +12¢    MEDIUM
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

Rank by:
1. Pure arb (risk-free, same resolution date) — **always trade these**
2. Soft arb (positive math, date gap ≤30 days) — trade with caution
3. High-conviction alpha (gap ≥5¢, clear informational edge, liq >$10K)
4. Low-conviction alpha — flag only

## Step 5 — EXECUTE BEST OPPORTUNITY

We can only execute the Polymarket leg directly. Kalshi legs must be placed manually.

From moonpay/skills, MIT.

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Stars
111
Forks
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Open issues
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Last commit (this folder)
Mar 19, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

moonpay-scout skill. Agent Mart. https://agentmart.dev/skills/moonpay-moonpay-scout (last updated 2026-10-24)