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Meme Trader - Solana Memecoin Trading System

meme-trader

Solana memecoin trading analysis and execution support. Use when analyzing tokens, detecting rugs, finding alpha, or planning trades on pump.fun, Raydium, Jupiter. Covers: token metrics, liquidity analysis, holder distribution, entry/exit signals, position sizing, degen strategies.

SKILL.md

Full skill instructions

Meme Trader - Solana Memecoin Trading System

Aggressive memecoin analysis, rug detection, and trade execution support for Solana ecosystem. Built for speed, alpha generation, and maximum degen potential.

Activation Triggers

<triggers> - "Analyze [token/​CA]" - "Is this a rug?" - "Find me alpha" - "Entry point for [token]" - "Pump.fun launches" - "Best memes to ape" - "Liquidity check [token]" - "Holder distribution [CA]" - Keywords: memecoin, pump.fun, raydium, jupiter, dexscreener, birdeye, solana meme, ape, degen </​triggers>

Core Capabilities

1. Token Analysis

  • Contract verification (mint authority, freeze authority)
  • Liquidity depth and lock status
  • Holder distribution (whale concentration, dev wallets)
  • Social sentiment scraping
  • Volume/​MCAP ratio analysis

2. Rug Detection

  • Honeypot detection (sell tax, blacklist functions)
  • Dev wallet tracking
  • Liquidity pull risk assessment
  • Contract red flags (hidden mints, proxy patterns)
  • Team verification (KOL backing, doxxed devs)

3. Trade Signals

  • Entry point identification (support levels, breakout detection)
  • Exit signals (resistance, volume divergence)
  • Position sizing based on risk tolerance
  • Stop-loss recommendations
  • Take-profit laddering strategies

4. Alpha Generation

  • New launch monitoring (pump.fun, Raydium)
  • Social trend detection (Twitter/​X, Telegram)
  • Whale wallet tracking
  • Cross-reference with successful patterns

Data Sources

<data_sources>

  • Dexscreener: Price, volume, liquidity, charts
  • Birdeye: Token analytics, holder data, trades
  • Solscan: Contract verification, token info
  • Pump.fun: New launches, bonding curves
  • Jupiter: Swap routing, price impact
  • Helius/​Shyft: RPC, transaction parsing </​data_sources>

Data Quality & Governance

<data_governance> Quality Requirements (via data-orchestrator): All trading signals require minimum data quality scores:

Signal TypeMin Quality ScoreMax Data Age
Entry Signal90/​10030 seconds
Exit Signal90/​10030 seconds
Rug Detection95/​10060 seconds
Position Sizing85/​1005 minutes
Alpha Scan80/​10015 minutes

Validation Pipeline:

Raw Price Data → Schema Check → Cross-Source Verify → Anomaly Flag → Quality Score
                                    ↓
                        Min 2 sources agree (5% tolerance)

Data Quality Indicators in Output:

DATA QUALITY: 94/​100 ✓
├─ Sources: 3/​3 (dexscreener, birdeye, jupiter)
├─ Price Agreement: 99.2%
├─ Freshness: 12s ago
└─ Anomaly Check: PASS

Rejection Criteria:

  • Quality score < 80%: REJECT signal, show warning
  • Single source only: Add "LOW CONFIDENCE" flag
  • Price divergence > 10%: REJECT, investigate
  • Data age > 60s for live signals: STALE warning </​data_governance>

ML-Enhanced Signal Generation

<ml_signals> AI/​ML Signal Sources:

  1. Anomaly Detection: Flag unusual volume/​price patterns

    • Isolation forest on 24h price/​volume deviation
    • Alert when score > 0.8 (potential pump or dump)
  2. Sentiment Classification: Social momentum scoring

    • NLP analysis of Twitter/​Telegram mentions
    • Bullish/​Bearish/​Neutral with confidence score
  3. Pattern Recognition: Historical pattern matching

    • Compare current setup to 1000+ historical pumps
    • Match score indicates similarity to successful entries
  4. Predictive Indicators: ML-derived signals

    • 1h price direction probability (up/​down/​sideways)
    • Optimal entry window prediction
    • Volume momentum forecast

Signal Confidence Framework:

interface MLSignal {
  type: 'anomaly' | 'sentiment' | 'pattern' | 'predictive';
  value: number;          // -1 to 1 (bearish to bullish)
  confidence: number;     // 0 to 1
  data_quality: number;   // 0 to 100
  features_used: string[];
  model_version: string;
  timestamp: Date;
}

interface EnhancedTradeSignal {
  traditional_score: number;  // Technical analysis
  ml_score: number;           // ML ensemble
  combined_score: number;     // Weighted average
  confidence: 'high' | 'medium' | 'low';
  reasoning: string[];
}

ML Signal Output Format:

ML SIGNALS: $MEME
├─ Anomaly Score: 0.72 (elevated activity detected)
├─ Sentiment: BULLISH (0.68 confidence)
├─ Pattern Match: 78% similarity to "early pump" template
├─ 1h Direction: UP (62% probability)
└─ COMBINED ML SCORE: 7.2/​10

RECOMMENDATION: Traditional + ML signals ALIGNED
                Confidence: HIGH

</​ml_signals>

Adaptive Learning

<adaptive_learning> Continuous Improvement Loop:

Signal Generated → Trade Outcome Tracked → Performance Feedback
        ↑                                          ↓
  Model Updated ← Weekly Retraining ← Outcome Analysis

Signal Performance Tracking:

  • Track all generated signals with outcomes
  • Calculate accuracy by signal type and market condition
  • Adjust weighting based on recent performance
  • Flag underperforming signal sources for review

Adaptation Triggers:

  • Win rate drops below 55%: Review signal parameters
  • New market regime detected: Retrain models
  • Volatility spike: Tighten quality requirements
  • High correlation breakdown: Recalibrate ensemble </​adaptive_learning>

Implementation Workflow

Step 1: Parse Query Intent

interface MemeQuery {
  token_address?: string;
  token_name?: string;
  action: 'analyze' | 'rug_check' | 'find_alpha' | 'trade_signal' | 'monitor';
  timeframe?: '1m' | '5m' | '1h' | '4h' | '1d';
  risk_level?: 'conservative' | 'moderate' | 'degen';
}

Step 2: Data Retrieval

Execute scripts/​fetch-meme-data.ts with parsed parameters:

npx tsx .claude/​skills/​meme-trader/​scripts/​fetch-meme-data.ts \
  --token "PUMP123...abc" \
  --action analyze \
  --risk degen

Step 3: Analysis Pipeline

  1. Contract Check � Verify no malicious functions
  2. Liquidity Check � Assess depth and lock status
  3. Holder Analysis � Distribution and whale activity
  4. Social Scan � Sentiment and narrative strength
  5. Signal Generation � Entry/​exit recommendations

Step 4: Format Response

Use templates from references/​token-analysis-templates.md

Output Formats

Quick Scan (Default)

TOKEN: $MEME (Contract: abc123...)
VERDICT: APE / WATCH / AVOID
RISK: 7/​10

METRICS:
- MCAP: $500K | Liquidity: $50K (10%)
- Holders: 342 | Top 10: 45%
- 24h Vol: $200K | Buys: 234 | Sells: 89

RED FLAGS: None detected
GREEN FLAGS: LP locked 6mo, renounced mint

ENTRY: $0.00042 (current -5%)
TP1: $0.00065 (+55%)
TP2: $0.00098 (+133%)
SL: $0.00032 (-24%)

Deep Analysis (--format deep)

Full contract audit, holder breakdown, social analysis, comparable tokens, historical pattern matching.

Signal Only (--format signal)

$MEME: BUY @ 0.00042 | TP 0.00065/​0.00098 | SL 0.00032 | Size: 2% port

Risk Framework

Degen Mode (Aggressive)

  • Position size: Up to 5% portfolio per trade
  • Stop-loss: 30-50% from entry
  • Take-profit: 2-5x minimum target
  • Acceptable rug risk: Up to 40%
  • Entry timing: Early (< 50 holders)

Moderate Mode

  • Position size: 1-2% portfolio
  • Stop-loss: 20-30%
  • Take-profit: 50-100% gains
  • Acceptable rug risk: < 20%
  • Entry timing: After initial pump settles

Conservative Mode

  • Position size: 0.5-1% portfolio
  • Stop-loss: 10-15%
  • Take-profit: 20-50% gains
  • Acceptable rug risk: < 10%
  • Entry timing: Established tokens only

Rug Detection Checklist

<rug_indicators> CRITICAL (Instant Avoid):

  • Mint authority NOT renounced
  • Freeze authority enabled
  • Hidden transfer fees > 5%
  • Liquidity < $10K
  • LP not locked
  • Top holder > 20% (non-exchange)

WARNING (Proceed with caution):

  • Dev wallet holds > 5%
  • < 100 holders
  • No social presence
  • Copied contract (no modifications)
  • Launch < 1 hour ago

GREEN FLAGS:

  • Mint renounced + freeze disabled
  • LP locked 3+ months
  • Top 10 holders < 30%
  • Active community (TG/​Twitter)
  • KOL/​influencer backing
  • Audited contract </​rug_indicators>

Quality Gates

<validation_rules>

  • Price data: Max 30 seconds old
  • Holder data: Max 5 minutes old
  • Contract verification: Always fresh
  • Never recommend without liquidity check
  • Always show risk score (1-10)
  • Include stop-loss with every entry signal </​validation_rules>

Error Handling

<error_recovery>

  • API timeout: Retry with fallback source (Birdeye � Dexscreener � Jupiter)
  • Invalid CA: Suggest similar tokens or request clarification
  • No liquidity: Return "AVOID - No liquidity" immediately
  • Rate limited: Queue and batch requests </​error_recovery>

Performance Targets

  • Token scan: < 3 seconds
  • Full analysis: < 10 seconds
  • Signal accuracy: > 60% profitable (degen mode)
  • Rug detection: > 90% accuracy

Security Considerations

<security> - Never expose private keys or wallet seeds - Sanitize all contract addresses - Rate limit API calls (prevent ban) - Warn on suspicious contract patterns - No financial advice disclaimers (user assumes risk) </​security>

<see_also>

  • references/​meme-trading-strategies.md � Degen playbook
  • references/​token-analysis-templates.md � Analysis frameworks
  • scripts/​fetch-meme-data.ts � CLI implementation </​see_also>

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