--- name: prediction-market-arbitrage-api-2d35d3d0c809 description: 'Find arbitrage opportunities across Polymarket and Kalshi prediction markets via AIsa API. Scan sports markets for cross-platform price discrepancies, compare real-time odds, verify orderbook liquidity. Use when user asks about: prediction market arbitrage, cross-platform price differences, sports betting arbitrage, odds comparison, risk-free profit, market inefficiencies.' license: MIT compatibility: Designed for Agent Skills compatible clients such as OpenClaw, Claude Code, Hermes, and GitHub-backed skill catalogs. Requires system binaries python3, environment variables AISA_API_KEY and internet access to api.aisa.one. metadata: author: AIsa version: 1.0.0 homepage: https://aisa.one repository: https://github.com/baofeng-tech/agent-skills tags: market,prediction,aisa platforms: agentskills.io,agentskill.sh,github primary_env: AISA_API_KEY allowed-tools: Read Bash Grep --- # Prediction Market Arbitrage Api Find arbitrage opportunities across [Polymarket](https://polymarket.com) and [Kalshi](https://kalshi.com) via [AIsa API](https://aisa.one). ## Setup ```bash export AISA_API_KEY="your-key" ``` Get a key at [aisa.one](https://aisa.one) ($0.01/query, pay-as-you-go). ## Workflow Finding arbitrage involves these steps: 1. **Scan matching markets** across platforms (`scan` for bulk, `match` for specific) 2. **Review spreads** — the tool calculates price differences automatically 3. **Verify liquidity** — use `prediction_market_client.py orderbooks` to check depth before acting ## Quick Examples ### Scan a sport for arbitrage ```bash # Scan all NBA markets on a date — shows spreads automatically python3 scripts/arbitrage_finder.py scan nba --date 2025-04-01 ``` ### Analyze a specific market ```bash # By Polymarket slug python3 scripts/arbitrage_finder.py match --polymarket-slug # By Kalshi ticker python3 scripts/arbitrage_finder.py match --kalshi-ticker ``` ### Verify liquidity before acting ```bash # Check orderbook depth on both sides python3 scripts/prediction_market_client.py polymarket orderbooks --token-id python3 scripts/prediction_market_client.py kalshi orderbooks --ticker ``` ## Commands ### arbitrage_finder.py — Automated Detection ```bash python3 scripts/arbitrage_finder.py scan --date [--min-spread ] [--min-liquidity ] [--json] python3 scripts/arbitrage_finder.py match --polymarket-slug [--min-spread ] [--min-liquidity ] [--json] python3 scripts/arbitrage_finder.py match --kalshi-ticker [--min-spread ] [--min-liquidity ] [--json] ``` Sports: `nba`, `nfl`, `mlb`, `nhl`, `soccer`, `tennis`. ### prediction_market_client.py — Raw Market Data Use for manual price checks and deeper analysis. ```bash # Search markets python3 scripts/prediction_market_client.py polymarket markets --search --status open --limit 5 python3 scripts/prediction_market_client.py kalshi markets --search --status open --limit 5 # Get prices (use token_id / market_ticker from markets output) python3 scripts/prediction_market_client.py polymarket price python3 scripts/prediction_market_client.py kalshi price # Cross-platform sports matching python3 scripts/prediction_market_client.py sports by-date --date python3 scripts/prediction_market_client.py sports matching (--polymarket-slug | --kalshi-ticker ) # Orderbook depth python3 scripts/prediction_market_client.py polymarket orderbooks --token-id python3 scripts/prediction_market_client.py kalshi orderbooks --ticker ``` ## Understanding Arbitrage Arbitrage exists when the combined cost of all mutually exclusive outcomes across platforms is less than `1.0`: > "Yes" at `0.40` on Polymarket + "No" at `0.55` on Kalshi = cost `0.95`, guaranteed payout `1.00` → 5.3% profit. Always verify orderbook depth — a spread without liquidity is not executable. ## Security & Permissions **Requires:** `AISA_API_KEY` environment variable. All operations are **read-only** via HTTPS GET to `api.aisa.one`. Arbitrage spreads are calculated locally. No trades executed, no wallets connected, no personal data sent beyond the API key. Every response includes `usage.cost` and `usage.credits_remaining`. Full docs: [AIsa API Reference](https://docs.aisa.one/reference/). --- Source attribution: baofeng-tech Original project: https://github.com/baofeng-tech/agent-skills/blob/6da0a55a3aa88f283251abe4392be1217ccb7b52/prediction-market-arbitrage-api/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: baofeng-tech Original project: https://github.com/baofeng-tech/agent-skills/blob/6da0a55a3aa88f283251abe4392be1217ccb7b52/prediction-market-arbitrage-api/SKILL.md Attribution basis: Source repository record Fields of work: Marketing Roles: Growth Marketer, Marketing Analyst, Product Marketer Specialties: API, Backend, Design, Frontend, Git Tools: Aisa Api Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 56a03bef452ea3ebeef55325ec995f4449f4e6778bb85abd330d558a72b314d8