# Polymarket Multi Strategy Arb

> Execute multiple Polymarket prediction strategies covering arbitrage, news sentiment, and traditional odds differences

- Skill: `knuckles-team/polymarket-multi-strategy-arb` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add knuckles-team/polymarket-multi-strategy-arb`
- Raw SKILL.md: https://api.skillmd.com/api/skills/knuckles-team/polymarket-multi-strategy-arb/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Knuckles-Team (https://skillmd.com/u/knuckles-team)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/knuckles-team/polymarket-multi-strategy-arb

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# polymarket-multi-strategy-arb Workflow

Execute multiple Polymarket prediction strategies covering arbitrage, news sentiment, and traditional odds differences

### Step 0: mcp_market_data
Fetch current contract odds from target Polymarket outcome market
Expected: contract_odds

### Step 1: web-search [depends_on: Step 0]
Scrape and analyze news and social sentiment signals for prediction probability
Expected: sentiment_score

### Step 2: mcp_signals [depends_on: Step 0]
Retrieve pricing data from external prediction markets and traditional sportsbooks
Expected: external_market_odds

### Step 3: mcp_strategy [depends_on: Step 1, Step 2]
Compare prices across platforms to identify positive expected value (+EV) arbitrage opportunities
Expected: selected_arbitrage_target

### Step 4: mcp_orders [depends_on: Step 3]
Route execution orders to capture the pricing discrepancy
Expected: arbitrage_execution_result

## Execution

Run this workflow as a dependency-ordered DAG. Steps with no unmet `depends_on` run in parallel; dependents run after their prerequisites complete.

- **Run first (in parallel):** Step 0 — mcp_market_data
- **After level 0:** Step 1 — web-search; Step 2 — mcp_signals
- **After level 1:** Step 3 — mcp_strategy
- **After level 2:** Step 4 — mcp_orders

**Execution:** If graph-os is reachable, offload the whole DAG via `graph_orchestrate action=execute_workflow` (or the `kg-delegate` skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet `depends_on` in parallel, then their dependents.