{
  "protocolVersion": "0.3.0",
  "name": "ANiu — SXS Sports × Science",
  "description": "ANiu is the AI agent of SXS (Sports × Science), a football (soccer) quant data provider operated by an independent operator in China. SXS turns a historical database of ~1.4M football matches and 300+ independently developed models into finished data products: match data, market (odds) moves, model-vs-market probability gaps, and post-match reviews with timestamped receipts. SXS is a data provider only — data only, no picks, no tips, no promises of returns. Every published figure can be checked match by match against its pre-match timestamped record.",
  "url": "https://sxs-ai.com",
  "preferredTransport": "JSONRPC",
  "provider": {
    "organization": "SXS (Sports × Science)",
    "url": "https://sxs-ai.com"
  },
  "iconUrl": "https://sxs-ai.com/favicon.ico",
  "version": "1.0.0",
  "documentationUrl": "https://sxs-ai.com",
  "capabilities": {
    "streaming": false,
    "pushNotifications": false,
    "stateTransitionHistory": false
  },
  "defaultInputModes": ["text/plain"],
  "defaultOutputModes": ["text/plain", "application/json"],
  "skills": [
    {
      "id": "match-data-lookup",
      "name": "Football match data lookup",
      "description": "Look up football match data from the SXS database (~1.4M historical matches): fixtures, model probabilities for full-time / half-time / handicap markets, market odds and how they moved from opening to close. Data is presented as computed; SXS does not issue picks or recommendations.",
      "tags": ["football", "soccer", "match-data", "odds-movement", "probabilities"],
      "examples": [
        "Show me the model probabilities and closing odds for last night's featured matches.",
        "How did the market price move between opening and kickoff for this match?",
        "Which matches today have the largest model-vs-market probability gaps?"
      ]
    },
    {
      "id": "post-match-review-retrieval",
      "name": "Post-match review retrieval",
      "description": "Retrieve post-match reviews published by SXS. Each review is written against a pre-match record that was timestamped before kickoff, so claims can be verified after the fact — receipts first, narrative second.",
      "tags": ["post-match-review", "verification", "timestamps", "receipts"],
      "examples": [
        "Find the SXS post-match review for Bayern Munich vs RB Leipzig.",
        "What did SXS publish before kickoff about this match, and what happened?",
        "Show me recent reviews where the model disagreed with the market."
      ]
    },
    {
      "id": "quant-methodology-qa",
      "name": "Sports quant methodology Q&A",
      "description": "Answer methodology questions about football quant analysis as practised by SXS: why pricing (not the match) is the opponent, sample scarcity, regime drift, the cost of real-world validation (live records vs backtests), and model-vs-market gap analysis. Methodology only — no match picks, no betting advice, no return claims.",
      "tags": ["quant", "methodology", "model-validation", "backtest-vs-live"],
      "examples": [
        "Why does SXS say a backtest never has a bad month?",
        "What is the difference between a model probability and a market-implied probability?",
        "How does SXS validate a model before trusting it with live data?"
      ]
    }
  ],
  "contact": {
    "email": "hello@sxs-ai.com"
  }
}