meme-trader

安装量: 72
排名: #10693

安装

npx skills add https://github.com/dreamineering/meme-times --skill meme-trader

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

Activation 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

  • 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 Quality & Governance

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

| 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

ML-Enhanced Signal Generation

AI/ML Signal Sources:

  • Anomaly Detection: Flag unusual volume/price patterns

Isolation forest on 24h price/volume deviation

  • Alert when score > 0.8 (potential pump or dump)

  • Sentiment Classification: Social momentum scoring

NLP analysis of Twitter/Telegram mentions

  • Bullish/Bearish/Neutral with confidence score

  • Pattern Recognition: Historical pattern matching

Compare current setup to 1000+ historical pumps

  • Match score indicates similarity to successful entries

  • 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

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

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

  • Contract Check � Verify no malicious functions

  • Liquidity Check � Assess depth and lock status

  • Holder Analysis � Distribution and whale activity

  • Social Scan � Sentiment and narrative strength

  • 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

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

Quality Gates

  • 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

Error Handling

  • 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

Performance Targets

  • Token scan: < 3 seconds

  • Full analysis: < 10 seconds

  • Signal accuracy: > 60% profitable (degen mode)

  • Rug detection: > 90% accuracy

Security Considerations

  • references/meme-trading-strategies.md � Degen playbook

  • references/token-analysis-templates.md � Analysis frameworks

  • scripts/fetch-meme-data.ts � CLI implementation

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