meme-trader

name	meme-trader
description	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.
tools	Read(pattern:.claude/skills/meme-trader/**), WebSearch, WebFetch(domain:dexscreener.com|birdeye.so|solscan.io|pump.fun|jup.ag), mcp__perplexity-ask__search, TodoWrite

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

- "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

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 Type	Min Quality Score	Max Data Age
Entry Signal	90/100	30 seconds
Exit Signal	90/100	30 seconds
Rug Detection	95/100	60 seconds
Position Sizing	85/100	5 minutes
Alpha Scan	80/100	15 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

- 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)

<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>