Hyperliquid Trader Skill Score is a paid API for AI agents from graphadvocate.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-14).
Scores a Hyperliquid trader 0-100 on trading skill, classifying them as sharp/neutral/retail with derived metrics like profit factor, liquidation rate, and funding efficiency
Hyperliquid trader skill score. POST {user}. Returns derived metrics: skill_score (0-100), classification (sharp/neutral/retail), liquidation_count + rate, funding_paid_per_volume, profit_factor, sample_size_trades. Wraps Pinax /v1/hyperliquid/users with compute the upstream doesn't provide. For trading bots vetting copy-trade signals or sizing-the-room before entering a perp position.
Returns a JSON object with: skill_score (0-100 integer), classification (sharp/neutral/retail enum), liquidation_count (integer), liquidation_rate (float), funding_paid_per_volume (float efficiency metric), profit_factor (float), and sample_size_trades (integer indicating data reliability)
POSThttps://graphadvocate.com/hyperliquid/scoreUse this endpoint when you need a fast, single-number skill verdict on a Hyperliquid perpetuals trader — especially for copy-trade vetting bots, pre-position counterparty screening, or any workflow that needs to programmatically classify trader quality. Prefer this over the full PnL report endpoint when you only need the score and classification without per-coin position detail, saving latency and cost.
{
"user": "0x1234567890123456789012345678901234567890"
}| Field | Type | Description |
|---|---|---|
| user | string |
{
"user": "0x1234567890123456789012345678901234567890",
"reason": "no Hyperliquid trading history found for this address",
"skill_score": 0,
"generated_at": "2026-06-18T04:55:10.456604+00:00",
"classification": "no_data",
"hyperliquid_role": {
"hint": "Wallet exists on HL but has no fills, positions, or activity yet.",
"role": "user"
}
}{
"type": "json",
"example": {
"user": "0xecb63caa…",
"skill_score": 62.4,
"classification": "neutral",
"liquidation_count": 0,
"realized_pnl_usdc": 11605542.69,
"sample_size_trades": 14626475
}
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