Solana Token Sniper Bot

Educational Solana token sniping tool with creator intelligence and rugpull detection capabilities. This skill provides comprehensive guidance for working with high-performance blockchain analysis, security scanning, and trading simulation systems.

Overview

This skill helps you understand and work with a hyper-efficient educational token sniping tool that:

**CRITICAL**: This is an educational tool. All trading is simulated (DRY_RUN mode is triple-locked). No real trading functionality will be created or enabled.

Technical Stack

Code Structure

```

src/

├── core/ # Configuration and orchestration

├── detection/ # Blockchain monitoring and token detection

├── analysis/ # Security scanning and metrics

├── trading/ # Simulation and exit strategies

├── monitoring/ # Real-time dashboard and WebSocket

└── utils/ # Rate limiting and performance profiling

```

Instructions

1. Analyzing Token Detection Systems

When examining token detection code:

```typescript

interface DetectionConfig {

sources: {

pumpfun: { enabled: true; priority: 1; multiplier: 2.0 };

raydium: { enabled: true; priority: 2; multiplier: 1.8 };

orca: { enabled: true; priority: 3; multiplier: 1.5 };

};

maxLatency: 5000;

showAllTokens: true; // Display ALL tokens with risk info

creatorIntelligence: true;

}

```

2. Security Analysis and Creator Intelligence

When working with security scanning:

```typescript

interface SecurityInfo {

score: number; // 0-100

visualDisplay: {

badge: '🔴' | '🟠' | '🟢';

radarChart: boolean;

tokenIcon: string;

};

creatorIntelligence: {

walletAddress: string;

rugpullHistory: { count: number; priceAtDump: number[] };

successRate: number;

riskMultiplier: number; // 1.3x verified, 1x unknown, 0.7x flagged

};

displayAllTokens: true;

}

```

3. Multi-Strategy Trading Simulation

When analyzing trading strategies:

```typescript

interface TradingStrategy {

simulation: {

enabledFor: 'TOKENS_UNDER_10_MINUTES';

baseAmount: 0.003; // SOL (UI configurable 0.001-0.01)

maxPositions: 500; // Auto-scaling

};

multiStrategy: {

ultraAggressive: { holdPercent: 1, stopLoss: -20 };

aggressive: { holdPercent: 15, stopLoss: -20 };

balanced: { holdPercent: 10, stopLoss: -20 };

conservative: { holdPercent: 5, stopLoss: -15 };

};

performanceBenchmarks: {

targetWinRate: 60;

targetAvgROI: 25;

maxDetectionLatency: 5000;

};

}

```

4. Rugpull Protection and Exit Strategies

When implementing exit logic:

```typescript

interface ExitStrategy {

rugPullProtection: {

realTimeMonitoring: [

'CREATOR_WALLET_ACTIVITY',

'LIQUIDITY_REMOVAL_DETECTION',

'LARGE_HOLDER_DUMPS',

'SOCIAL_SENTIMENT_CRASH'

];

maxExitTime: 100; // 100ms emergency exit

};

creatorAwareExits: {

verifiedCreators: 'EXTENDED_HOLD';

flaggedCreators: 'QUICK_EXIT';

};

}

```

5. Performance Optimization

When optimizing performance:

6. Frontend Integration

When working with the UI:

Constraints

1. **NEVER modify DRY_RUN mode** - all trading remains simulated

2. **NEVER create real trading functionality** - educational simulation only

3. **NEVER bypass safety constraints** - simulation boundaries must remain intact

4. **NEVER enable real targeting** - creator intelligence for educational analysis only

5. **ALWAYS use strict TypeScript** - no `any` types

6. **ALWAYS show ALL tokens** - never filter by security score

7. **ALWAYS respect performance benchmarks** - <5s detection, >60% win rate, >25% ROI

Example Usage

1. **Analyze security scanning logic**: Review honeypot detection, creator intelligence, and visual warning systems

2. **Optimize token detection**: Examine multi-source aggregation, priority weighting, and latency targets

3. **Review exit strategies**: Study multi-strategy options, rugpull protection, and creator-aware timing

4. **Performance profiling**: Monitor win rate, ROI, detection latency, and memory usage against benchmarks

5. **Creator database**: Analyze wallet tracking, rugpull history, and risk scoring systems

Key Files