菜单
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Telegram/Discord community building, holder psychology, retention mechanics, cult creation. Use when building communities, managing holder sentiment, designing engagement systems, or optimizing retention.
Central data strategy coordinator for AI-driven crypto trading. Use when building data pipelines, validating data quality, coordinating multi-source data aggregation, or implementing ML-ready data infrastructure. Covers: data governance, quality validation, source management, real-time pipelines, backtesting infrastructure.
DeFi fundamentals and cross-chain analytics using DefiLlama-style data. Use when you want to find undervalued protocols, screen by TVL/revenue growth vs token price, compare sectors, or run data-driven crypto research beyond pure memes.
Solana memecoin trade execution and simulation. Use when you already have a TradePlan (token, side, size, SL/TP, slippage, risk mode) from meme-trader / flow-tracker / degen-savant and want to either simulate or send a swap through a Solana wallet.
Solana memecoin launch strategy and execution. Use when planning token launches, designing tokenomics, building pre-launch hype, deploying on pump.fun/Raydium, or optimizing launch mechanics. Covers: token creation, bonding curves, liquidity strategy, community building, anti-rug best practices.
Viral memecoin content creation and distribution engineering. Use when creating memes, launching viral campaigns, coordinating raids, or building narrative momentum. Covers: meme format selection, viral hooks, platform-specific distribution, community activation, engagement farming.
| 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 |
Aggressive memecoin analysis, rug detection, and trade execution support for Solana ecosystem. Built for speed, alpha generation, and maximum degen potential.
<data_sources>
<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:
<ml_signals> AI/ML Signal Sources:
Anomaly Detection: Flag unusual volume/price patterns
Sentiment Classification: Social momentum scoring
Pattern Recognition: Historical pattern matching
Predictive Indicators: ML-derived signals
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> Continuous Improvement Loop:
Signal Generated → Trade Outcome Tracked → Performance Feedback
↑ ↓
Model Updated ← Weekly Retraining ← Outcome Analysis
Signal Performance Tracking:
Adaptation Triggers:
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';
}
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
Use templates from references/token-analysis-templates.md
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%)
Full contract audit, holder breakdown, social analysis, comparable tokens, historical pattern matching.
$MEME: BUY @ 0.00042 | TP 0.00065/0.00098 | SL 0.00032 | Size: 2% port
<rug_indicators> CRITICAL (Instant Avoid):
WARNING (Proceed with caution):
GREEN FLAGS:
<validation_rules>
<error_recovery>
<see_also>