Install
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
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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 26, 2026.
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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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Signals
- Stars
- 111
- Forks
- 30
- Open issues
- 3
- Last commit (this folder)
- Mar 19, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/moonpay/skills
- Website
- www.moonpay.com/agents
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
moonpay-scout skill. Agent Mart. https://agentmart.dev/skills/moonpay-moonpay-scout (last updated 2026-10-24)