X-ray any Polymarket wallet — skill level, entry quality, bot detection, and edge analysis. Queries Polymarket's public APIs, no authentication needed. Inspired by @thejayden's "Autopsy of a Polymarket Whale" analysis.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
X-ray any Polymarket wallet — skill level, entry quality, bot detection, and edge analysis. Queries Polymarket's public APIs, no authentication needed. Inspired by @thejayden's "Autopsy of a Polymarket Whale" analysis.
This skill implements the forensic trading analysis framework developed by @thejayden. Read the original post to understand the philosophy behind Time Profitable, hedge checks, bot detection, and accumulation signals.
This is an analysis tool, not a trading signal. The skill returns forensic metrics for ANY Polymarket wallet — your agent uses them to UNDERSTAND traders, learn patterns, and make informed decisions. This is for education and research, not for blindly copying positions.
⚠️ Important Disclaimer
Past performance does not guarantee future results. A wallet's historical metrics tell you about:
✅ How they traded in the past
✅ Their historical win rate and entry quality
❌ NOT whether their strategy will work going forward
Why copying is risky:
Market conditions change constantly
A trader's edge might have been luck, timing, or specific to historical events
Slippage and fees erode thin edges to zero
Other traders copying the same strategy destroy the edge
Use this skill to:
✅ Learn what skilled traders look like (metrics, behavior)
CLOB API: https://clob.polymarket.com — Trade history and orderbook
What You Get Back
The skill returns comprehensive forensic metrics:
{"wallet":"0x1234...abcd","total_trades":156,"total_period_hours":42.5,"profitability":{"time_profitable_pct":75.3,"win_rate_pct":68.2,"avg_profit_per_win":0.035,"avg_loss_per_loss":-0.018,"realized_pnl_usd":2450.00},"entry_quality":{"avg_slippage_bps":28,"quality_rating":"B+","assessment":"Good entries, occasional FOMO"},"behavior":{"is_bot_detected":false,"trading_intensity":"high","avg_seconds_between_trades":45,"price_chasing":"moderate","accumulation_signal":"growing"},"edge_detection":{"hedge_check_combined_avg":0.98,"has_arbitrage_edge":false,"assessment":"No locked-in edge; relies on direction"},"risk_profile":{"max_drawdown_pct":12.5,"volatility":"medium","max_position_concentration":0.22},"recommendation":"Good trader. Skilled entries, disciplined sizing. Good metrics for learning from. Not advice to copytrade."}
How It Works
Fetch trade history — Download all trades this wallet made from Polymarket via Simmer API
Compute profitability timeline — When were they underwater vs. profitable?
Analyze entry quality — Did they buy at optimal prices or chase?
Detect trading patterns — Bot (inhuman speed) vs. human (deliberate timing)?
Check for arbitrage — Combined YES+NO avg < $1.00? (Potential structural edge — depends on execution and fees)
Generate recommendation — Is this wallet worth following? What's the risk?
Understanding the Metrics
⏱️ Time Profitable (e.g., 75.3%)
Wallet was profitable (not underwater) for 75% of their trading period. This wallet endured only 25% painful drawdowns — that's discipline.
>80% = Sniper-like (skilled entries, holds through drawdowns)
50-80% = Solid (good discipline)
<50% = Risky (likely panic-held losses)
🎯 Entry Quality (e.g., 28 bps average slippage)
They buy near the best available price. 28 basis points is normal for active traders. No evidence of FOMO market orders.
<20 bps = Expert. Limit orders, patience.
20-40 bps = Good. Balanced speed/price.
>50 bps = Weak. Chasing prices.
🤖 Bot Detection (e.g., false)
Average 45 seconds between trades. This is human. A bot would be <1 second.
<5 sec = Likely bot. Avoid unless you know it's a legitimate market maker.
5-30 sec = Possible bot.
>30 sec = Human.
💰 Hedge Check (e.g., combined avg 0.98)
If they bought YES at $0.70 and NO at $0.30, combined = $1.00. This wallet spent exactly what they should to be neutral.
If combined < $1.00, they may have entered with a structural edge (lower combined cost than $1 payout). Actual profit depends on execution, fees, and spread.
Example 1: Learning from a skilled trader (Analysis)
import subprocess
import json
# Analyze a wallet known for skilled trading
result = subprocess.run(
["python", "wallet_xray.py", "0x123...abc", "--json"],
capture_output=True,
text=True
)
data = json.loads(result.stdout)
# LEARN from their profile, don't copy blindly
time_prof = data["profitability"]["time_profitable_pct"]
entry_qual = data["entry_quality"]["quality_rating"]
print(f"📊 What this trader does well:")
print(f" • Time Profitable: {time_prof}% (disciplined)")
print(f" • Entry Quality: {entry_qual} (patient buyer)")
print(f" • Behavior: {data['behavior']['accumulation_signal']} (not FOMO)")
# THEN: Ask yourself# - Why are they profitable? (skill or luck?)# - Can I replicate their decision-making process?# - Do I have their capital size, timing, or information?
Example 2: Research anomalies (Education)
# Analyze multiple wallets to understand patterns
wallets = ["0x111...", "0x222...", "0x333..."]
print("Comparing trader profiles:")
for wallet in wallets:
result = subprocess.run(
["python", "wallet_xray.py", wallet, "--json"],
capture_output=True,
text=True
)
data = json.loads(result.stdout)
is_bot = "🤖 BOT"if data["behavior"]["is_bot_detected"] else"👤 HUMAN"print(f"\n{wallet}: {is_bot}")
print(f" Win Rate: {data['profitability']['win_rate_pct']}%")
print(f" Time Profitable: {data['profitability']['time_profitable_pct']}%")
# Use this data to understand what successful trading LOOKS LIKE# Then build your own strategy based on these insights
Example 3: Informed decision-making (NOT blind copying)
# Analyze before you decide what to do
result = subprocess.run(
["python", "wallet_xray.py", "0x123...abc", "--json"],
capture_output=True,
text=True
)
data = json.loads(result.stdout)
# Make an INFORMED decision based on analysis + YOUR OWN JUDGMENTif data["profitability"]["time_profitable_pct"] > 75and \
data["entry_quality"]["quality_rating"] in ["A", "A+"]:
print(f"✅ This wallet shows skill (high Time Profitable, good entries)")
print(f"⚠️ But I will NOT copytrade blindly.")
print(f"📋 Instead, I'll:")
print(f" 1. Backtest their patterns on fresh data")
print(f" 2. Add my own market signals")
print(f" 3. Start with small position (1-2% of capital)")
print(f" 4. Monitor for next 30 days")
print(f" 5. Adjust if it stops working")
else:
print(f"❌ This wallet doesn't show strong enough metrics.")
print(f" Safer to avoid or research further before deciding.")
