---

name: cross-asset-divergence-rotation description: CMC Agent Hub-first crypto analyst skill that uses CoinMarketCap MCP tools to detect cross-asset divergences, rank pair-trade opportunities, and generate regime-aware strategy specs with evidence, catalysts, and optional backtests. allowed-tools: [search_cryptos, get_crypto_quotes_latest, get_crypto_info, get_crypto_technical_analysis, get_crypto_latest_news, search_crypto_info, get_global_metrics_latest, get_crypto_marketcap_technical_analysis, get_global_crypto_derivatives_metrics, trending_crypto_narratives, get_upcoming_macro_events]

Cross-Asset Divergence & Rotation (CADR) Agent Skill

Use this skill when the user asks for a crypto pair trade, relative-strength dislocation, laggard vs leader setup, mean-reversion idea, or an agentic market decision framework.

This skill is designed for CoinMarketCap Agent Hub / CMC MCP first operation. Prefer structured CMC MCP tools over stitching raw REST endpoints manually.

MCP Prerequisite

Before using the workflow below, make sure the agent has the CoinMarketCap MCP server configured.

{
  "mcpServers": {
    "cmc-mcp": {
      "url": "https://mcp.coinmarketcap.com/mcp",
      "headers": {
        "X-CMC-MCP-API-KEY": "your-api-key"
      }
    }
  }
}

If the CMC tools are unavailable or return connection errors, stop and ask for the MCP connection to be fixed before improvising with partial data.

Core Principle

Err on the side of collecting more structured CMC context, not less. This skill should behave like a market analyst:

• Search first, then batch IDs
• Pull both market-wide and asset-specific signals
• Prefer pre-computed indicators from CMC Agent Hub over raw in-context calculation
• Explain not only the trade, but also the evidence, invalidation, and catalysts

Workflow

1. Resolve the Universe

• If the user names exact assets, call search_cryptos for each one and capture the CMC IDs.
• If the user asks for a theme such as "top L1s" or "AI majors", call trending_crypto_narratives first to identify the relevant sector and then search_cryptos for the chosen assets.
• Batch IDs whenever possible.

2. Build the Global Decision Frame

Call these tools before deciding on any trade:

• get_global_metrics_latest
• get_crypto_marketcap_technical_analysis
• get_global_crypto_derivatives_metrics
• trending_crypto_narratives
• get_upcoming_macro_events

Use them to answer:

• Is this a risk-on, risk-off, or crisis regime?
• Is leverage supportive or dangerous?
• Are there macro catalysts that could invalidate a mean-reversion setup?
• Are we trading with or against the dominant narrative?

3. Collect Asset Intelligence

For each candidate asset:

• Call get_crypto_quotes_latest
• Call get_crypto_technical_analysis
• Call get_crypto_info
• Call get_crypto_latest_news
• Call search_crypto_info when the project, sector, tokenomics, or risk profile is unclear

4. Score Divergence Opportunities

When comparing two assets, use the CMC Agent Hub data to measure:

• Relative performance spread across 24h, 7d, and 30d
• RSI dislocation
• MACD asymmetry
• Narrative overlap or narrative divergence
• News/catalyst pressure
• Global market regime and derivatives pressure

Prioritize pairs where:

• Assets usually live in the same macro bucket or narrative cluster
• One asset is stretched/overbought and the other is washed out/underowned
• The regime does not make mean reversion obviously unsafe

5. Generate the Strategy

The strategy should usually be:

• LONG the laggard / undervalued asset
• SHORT the leader / overextended asset

Or, if the user does not want short exposure:

• LONG-only on the laggard with reduced conviction and explicit note that the hedge leg is missing

Always include:

• Entry logic
• Exit logic
• Stop/invalidation logic
• Position size adjusted for regime
• Confidence score
• Evidence block
• Macro and narrative context

6. Backtesting Guidance

• If a historical close series is available, you may attach an optional backtest.
• If the user’s API plan does not support the necessary historical endpoint, do not invent a backtest. Mark it as unavailable and continue with the agent-driven live thesis.

Output Rules

• Prefer a compact, structured JSON strategy spec
• Include data-backed evidence, not vague prose
• Mention if the signal is CMC Agent Hub derived rather than pure spread-history derived
• When confidence is low, say so explicitly

Output Format

Your final response MUST be a valid JSON block inside markdown:

{
  "skill": "cadr",
  "version": "2.0.0",
  "strategy": {
    "name": "CADR_[ASSET_A]_[ASSET_B]_agent_hub",
    "type": "cross_asset_divergence",
    "execution_style": "agent_hub_mcp_first",
    "pair": {"asset_a": "[ASSET_A]", "asset_b": "[ASSET_B]"},
    "direction": "long_[ASSET_A]_short_[ASSET_B]",
    "conviction_score": 4
  },
  "analysis": {
    "signal_method": "cmc_agent_hub_multifactor_proxy",
    "spread_zscore": 2.4,
    "rsi_divergence": {"[ASSET_A]": 34, "[ASSET_B]": 72},
    "macd_context": {"[ASSET_A]": "bullish_cross", "[ASSET_B]": "bearish_cross"},
    "evidence": {
      "performance_gap_pct": {"24h": -7.2, "7d": -15.4, "30d": -11.8},
      "shared_narratives": ["Layer 1"],
      "supporting_news": {"[ASSET_A]": ["headline 1"], "[ASSET_B]": ["headline 2"]}
    }
  },
  "rules": {
    "entry": "Agent Hub divergence proxy >= 2.0 with confirming RSI/MACD context",
    "exit": "Relative spread normalizes and RSI divergence compresses",
    "stop_loss": "Divergence proxy > 3.5 or 5% position loss",
    "invalidation": "Macro regime flip, leverage shock, or thesis-breaking news"
  },
  "risk_management": {
    "position_size_pct": 3.0,
    "stop_loss_pct": 4.0,
    "take_profit_pct": 8.0
  },
  "market_context": {
    "regime": "risk_off",
    "btc_dominance": 60.8,
    "fear_greed_index": 31,
    "trending_narratives": ["Layer 1 Rotation"],
    "macro_events": ["Fed rate decision"],
    "derivatives": {"funding_bias": "risk_off"}
  }
}