A/B Test Setup
Use when designing an A/B or split test: define the hypothesis, control and variants, estimate sample size, verify tracking, and predeclare metrics and stopping rules.
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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, sport...
--- 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 <slug> # By Kalshi ticker python3 scripts/arbitrage_finder.py match --kalshi-ticker <ticker> ``` ### Verify liquidity before acting ```bash # Check orderbook depth on both sides python3 scripts/prediction_market_client.py polymarket orderbooks --token-id <id> python3 scripts/prediction_market_client.py kalshi orderbooks --ticker <ticker> ``` ## Commands ### arbitrage_finder.py — Automated Detection ```bash python3 scripts/arbitrage_finder.py scan <sport> --date <YYYY-MM-DD> [--min-spread <pct>] [--min-liquidity <usd>] [--json] python3 scripts/arbitrage_finder.py match --polymarket-slug <slug> [--min-spread <pct>] [--min-liquidity <usd>] [--json] python3 scripts/arbitrage_finder.py match --kalshi-ticker <ticker> [--min-spread <pct>] [--min-liquidity <usd>] [--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 <kw> --status open --limit 5 python3 scripts/prediction_market_client.py kalshi markets --search <kw> --status open --limit 5 # Get prices (use token_id / market_ticker from markets output) python3 scripts/prediction_market_client.py polymarket price <token_id> python3 scripts/prediction_market_client.py kalshi price <market_ticker> # Cross-platform sports matching python3 scripts/prediction_market_client.py sports by-date <sport> --date <YYYY-MM-DD> python3 scripts/prediction_market_client.py sports matching (--polymarket-slug <slug> | --kalshi-ticker <ticker>) # Orderbook depth python3 scripts/prediction_market_client.py polymarket orderbooks --token-id <id> python3 scripts/prediction_market_client.py kalshi orderbooks --ticker <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/).--- 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 <slug> # By Kalshi ticker python3 scripts/arbitrage_finder.py match --kalshi-ticker <ticker> ``` ### Verify liquidity before acting ```bash # Check orderbook depth on both sides python3 scripts/prediction_market_client.py polymarket orderbooks --token-id <id> python3 scripts/prediction_market_client.py kalshi orderbooks --ticker <ticker> ``` ## Commands ### arbitrage_finder.py — Automated Detection ```bash python3 scripts/arbitrage_finder.py scan <sport> --date <YYYY-MM-DD> [--min-spread <pct>] [--min-liquidity <usd>] [--json] python3 scripts/arbitrage_finder.py match --polymarket-slug <slug> [--min-spread <pct>] [--min-liquidity <usd>] [--json] python3 scripts/arbitrage_finder.py match --kalshi-ticker <ticker> [--min-spread <pct>] [--min-liquidity <usd>] [--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 <kw> --status open --limit 5 python3 scripts/prediction_market_client.py kalshi markets --search <kw> --status open --limit 5 # Get prices (use token_id / market_ticker from markets output) python3 scripts/prediction_market_client.py polymarket price <token_id> python3 scripts/prediction_market_client.py kalshi price <market_ticker> # Cross-platform sports matching python3 scripts/prediction_market_client.py sports by-date <sport> --date <YYYY-MM-DD> python3 scripts/prediction_market_client.py sports matching (--polymarket-slug <slug> | --kalshi-ticker <ticker>) # Orderbook depth python3 scripts/prediction_market_client.py polymarket orderbooks --token-id <id> python3 scripts/prediction_market_client.py kalshi orderbooks --ticker <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/).
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Includes source scripts; review them before use. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.