MO

Identifies and ranks arbitrage opportunities across prediction markets like Polymarket and Kalshi.

Install

mkdir -p .claude/skills/moonpay-scout && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/16017" && unzip -o skill.zip -d .claude/skills/moonpay-scout && rm skill.zip

Installs to .claude/skills/moonpay-scout

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

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.
294 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Advanced

Key capabilities

  • Scan Polymarket and Kalshi for matching prediction market events.
  • Calculate pure arbitrage opportunities between platforms, including fees.
  • Identify high-conviction alpha opportunities based on mispricing.
  • Adjust for resolution date mismatches between markets.
  • Rank opportunities by profitability and conviction.
  • Execute Polymarket legs of trades using the MoonPay CLI.

How it works

The skill scans prediction markets, performs cross-platform arbitrage math, identifies mispricing, ranks opportunities, and can execute Polymarket trades via the MoonPay CLI.

Inputs & outputs

You give it
A topic to scout, e.g., 'US elections' or 'BTC price'.
You get back
A scout report detailing matched events, arbitrage/alpha opportunities, and executed trades.

When to use moonpay-scout

  • Scouting prediction markets
  • Finding arbitrage opportunities
  • Comparing event pricing
  • Trading strategy analysis

About this skill

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.

If pure arb:

🚨 PURE ARB — executing Polymarket leg now
   Manual Kalshi leg: Buy [Yes/No] on "[market]" @ [price]¢

If alpha:

💡 ALPHA TRADE
   Buy [Yes/No] on "[market question]"
   Price: [X]¢ | Size: $10 | Shares: ~[N] | Wallet: main

Ask: Execute Polymarket leg? (yes to proceed)

If yes, place the position using the tokenId and main wallet via:

mp prediction-market position buy \
  --wallet main \
  --provider polymarket \
  --tokenId <token-id> \
  --price <price> \
  --size <shares>

Step 6 — FINAL REPORT

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🎯 SCOUT REPORT — [topic] — [date]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Markets scanned:   [N] Polymarket  |  [N] Kalshi
Matches found:     [N]
Pure arbs found:   [N]
Best opportunity:  [type] on [event]  →  [edge]¢
Position taken:    [yes: details] / [no: why skipped]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Agent Rules

  • Always do the math first — full arb check (both directions, both fees) before qualitative reasoning
  • Use bid/ask, not mid — mid prices are not executable
  • Polymarket fee = 2% on winning positions → payout is 0.98, not 1.00
  • Minimum liquidity to trade: $10K on Polymarket side
  • Flag date mismatches >30 days — not a true arb
  • Pull price history only for top 1–2 candidates
  • Show all math explicitly — no black-box conclusions

Prerequisites

  • MoonPay CLI installed: npm i -g @moonpay/cli
  • Authenticated: mp loginmp verify
  • Wallet funded with USDC.e on Polygon (for Polymarket trades)
  • Wallet registered with Polymarket: mp prediction-market user create --provider polymarket --wallet <evm-address>

MoonPay Integration

Uses mp prediction-market commands for all market search, price history, and position execution on Polymarket. The MoonPay wallet handles USDC.e signing and submission on Polygon.

Related Skills

  • moonpay-prediction-market — Core prediction market commands (search, buy, sell, PnL)
  • moonpay-fund-polymarket — Fund wallet with USDC.e and POL for gas
  • moonpay-check-wallet — Verify balances before trading

When not to use it

  • When the user needs to trade on prediction markets not supported by Polymarket or Kalshi.
  • When the user does not have the MoonPay CLI installed and authenticated.
  • When the user's wallet is not funded with USDC.e on Polygon.

Prerequisites

MoonPay CLI installedAuthenticated with `mp login` and `mp verify`Wallet funded with USDC.e on PolygonWallet registered with Polymarket

Limitations

  • The skill only supports Polymarket and Kalshi.
  • Kalshi legs must be placed manually.
  • Polymarket charges 2% on winning positions.

How it compares

This workflow automates the complex process of cross-platform prediction market analysis and arbitrage calculation, providing a systematic way to find and act on profitable opportunities that would be difficult to identify manually.

Compared to similar skills

moonpay-scout side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
moonpay-scout (this skill)05moReviewAdvanced
startup-analyst84moNo flagsAdvanced
dcf-valuation53moNo flagsIntermediate
a-share-research-digest04moReviewBeginner

Try saying

Example prompts that trigger this skill in your AI assistant.

You might also like

startup-analyst

sickn33

Expert startup business analyst specializing in market sizing, financial modeling, competitive analysis, and strategic planning for early-stage companies. Use PROACTIVELY when the user asks about market opportunity, TAM/SAM/SOM, financial projections, unit economics, competitive landscape, team planning, startup metrics, or business strategy for pre-seed through Series A startups.

827

dcf-valuation

virattt

Performs discounted cash flow (DCF) valuation analysis to estimate intrinsic value per share. Triggers when user asks for fair value, intrinsic value, DCF, valuation, "what is X worth", price target, undervalued/overvalued analysis, or wants to compare current price to fundamental value.

522

a-share-research-digest

aifinlab

A股研报摘要/卖方观点汇总/券商研报速览。当用户说"研报"、"券商研报"、"分析师怎么看"、"卖方观点"、"research digest"、"XX最新研报"、"目标价"、"评级"、"研究报告"、"机构怎么说"、"研报摘要"、"分析师观点"、"研报汇总"时触发。MUST USE when user asks about research report digest, analyst opinions/ratings/target prices, or sell-side consensus views on a stock. 通过 web 搜索汇总券商研报的核心观点、目标价、评级变化,帮助用户

00

markitdown

K-Dense-AI

Convert various file formats (PDF, Office documents, images, audio, web content, structured data) to Markdown optimized for LLM processing. Use when converting documents to markdown, extracting text from PDFs/Office files, transcribing audio, performing OCR on images, extracting YouTube transcripts, or processing batches of files. Supports 20+ formats including DOCX, XLSX, PPTX, PDF, HTML, EPUB, CSV, JSON, images with OCR, and audio with transcription.

177310

whisper

davila7

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

1165

yams

trvon

Code indexing, semantic search, and knowledge graph for project memory

18

Search skills

Search the agent skills registry