Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
Polymarket Wallet Xray
X-ray any Polymarket wallet — skill level, entry quality, bot detection, and edge analysis. Queries Polymarket's public APIs, no authentication needed. Inspi...
skill openclawclawhub Free
X-ray any Polymarket wallet — skill level, entry quality, bot detection, and edge analysis. Queries Polymarket's public APIs, no authentication needed. Inspi...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, clawhub.json, scripts/status.py, wallet_xray.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.4
Provenance
- Publisher
- adlai88
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Polymarket Wallet X-Ray
Analyze any Polymarket wallet's trading patterns, skill level, and edge detection. No authentication needed. Queries Polymarket's public CLOB API directly. Inspired by: The Autopsy: How to Read the Mind of a Polymarket Whale by @thejayden 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) ✅ Identify potential anomalies (bots, arbitrageurs) ✅ Understand trader psychology (FOMO vs. discipline) ✅ Inform your own strategy decisions DO NOT use this skill to: ❌ Automatically copytrade wallets ❌ Expect to replicate their returns ❌ Trade on these metrics without understanding why ❌ Risk significant capital on patterns you don't understand
When to Use This Skill
Use this skill when you want to: Learn how skilled traders operate — What metrics separate winners from losers? Understand trading psychology — Who chases prices? Who has discipline? Detect bots and anomalies — Identify suspicious patterns for research Research arbitrage activity — Find wallets with hedged positions (educational) Compare trader profiles — What does a consistent trader look like vs. a lucky one? Inform your own strategy — Use patterns as input to YOUR decision-making, not as direct signals NOT for: Copying trades blindly or automatically Assuming past returns = future returns Making large bets on these metrics alone
Quick Commands
# Analyze a single wallet python wallet_xray.py 0x1234...abcd # Analyze wallet + only look at specific market python wallet_xray.py 0x1234...abcd "Bitcoin" # Compare two wallets head-to-head python wallet_xray.py 0x1111... 0x2222... --compare # Find wallets matching criteria (top Time Profitable in market) python wallet_xray.py "Will BTC hit $100k?" --top-wallets 5 --dry-run # Check your account status python scripts/status.py APIs Used (Public, No Auth Required): Gamma API: https://gamma-api.polymarket.com — Market search 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) Assess behavior — FOMO accumulation? Disciplined sizing? Rotating positions? Generate recommendation — Is this wallet worth following? What's the risk?
⏱️ 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. < $0.95 = Strong potential edge. Likely institutional/pro. $0.95-1.00 = Slight edge detected. > $1.00 = No edge; betting on direction.
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 JUDGMENT if data["profitability"]["time_profitable_pct"] > 75 and \ 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.")
Running the Skill
Analyze a single wallet (default): python wallet_xray.py 0x1234...abcd Analyze wallet for a specific market: python wallet_xray.py 0x1234...abcd "Bitcoin" Output as JSON (for scripts): python wallet_xray.py 0x1234...abcd --json Compare two wallets: python wallet_xray.py 0x1111... 0x2222... --compare Limit analysis to recent trades (faster): python wallet_xray.py 0x1234...abcd --limit 100
Troubleshooting
"Wallet has no trades" This wallet hasn't traded yet, or all trades are too old Try a wallet you know is active "Market not found" The market query didn't match anything on Polymarket Try a more specific market name or leave it blank to analyze all markets "Analysis took too long" For wallets with >500 trades, analysis can take 30+ seconds Use --limit 100 to analyze only recent trades for faster results "API rate limited" You're analyzing many wallets in quick succession Wait a minute before trying again, or use --limit to speed up individual analyses "Connection error" Check that Polymarket's CLOB API is reachable: curl https://clob.polymarket.com/trades If down, try again later or use --limit 50 to reduce load
Credits
This skill is based on the forensic trading analysis framework from @thejayden's "Autopsy of a Polymarket Whale". The original post shows how to: Spot fake gurus (high PnL, terrible entries) Detect bots (inhuman trading speed) Find arbitrage opportunities (hedged positions) Understand trader psychology (FOMO vs. discipline) All metrics and analysis patterns used here are derived from that work. If you find this useful, give the original post a read and follow @thejayden.
Links
Full Simmer API Reference: simmer.markets/docs.md Original Analysis: The Autopsy: How to Read the Mind of a Polymarket Whale Dashboard: simmer.markets/dashboard Support: Telegram
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Scripts1 Config
- SKILL.md
- README.md
- scripts/status.py
- wallet_xray.py
- clawhub.json