Llama Analyst – Fundamentals & Data-Driven Crypto Research

Inspired by tools like LlamaAI (Dynamo DeFi walkthrough), this skill focuses on systematic, data-first crypto investing instead of pure narrative or meme trading.

Activation Triggers

Use this skill when:

• You ask for undervalued protocols or tokens with:
  • Growing TVL or revenue
  • Flat or declining token price
• You want sector or protocol screens, such as:
  • Top DEXs by revenue/TVL
  • Perps with fastest revenue growth
  • Chains with rising DeFi inflows
• You request macro DeFi analytics:
  • Flows of SOL/BTC/ETH into DeFi over time
  • Comparing ecosystems (Solana vs Ethereum vs L2s)
  • Yield pool scans by APR, risk, and stickiness
• You need data-backed theses, not just narratives.

Core Capabilities

1. Protocol Screening & Ranking

• Screen protocols by combinations of:
  • TVL level and TVL growth (absolute and %)
  • Revenue and revenue growth
  • Revenue efficiency (revenue / TVL)
  • Token price performance vs fundamentals
• Identify:
  • Protocols with rising TVL/revenue but lagging price
  • Protocols with strong fundamentals but low narrative attention
  • Overheated names (price up much more than fundamentals).

2. Sector & Ecosystem Analytics

• Compare:
  • DEXs, perps, lending, LSDs, RWAs, restaking, etc.
  • Revenue and TVL distribution across sectors.
• Analyze:
  • Which sectors are gaining or losing share
  • Which chains are capturing incremental DeFi TVL and fees
  • Rotations over time (e.g., from L1s to perps, from DeFi to memes).

3. Flow & Macro Views

• Map flows of:
  • SOL/BTC/ETH and stablecoins into and out of DeFi.
  • Capital rotations between chains and sectors.
• Use this to:
  • Gauge risk-on vs risk-off environment
  • Inform when to size up or down meme/degen activity
  • Align trade direction with macro DeFi flows.

4. Output Formatting

• Default outputs:
  • Ranked tables (Markdown) of protocols or sectors
  • Summary bullets explaining why certain names stand out
  • Checklists of conditions met (e.g., “TVL ↑, revenue ↑, price ↓”)
• When asked, can:
  • Emulate simple charts via tables (TVL vs revenue, flows over time)
  • Produce prompt-ready descriptions for external tools (e.g., LlamaAI UI).

Example Queries This Skill Should Own

• “Find me 10 protocols with growing revenue and TVL but flat token price.”
• “Which Solana DeFi protocols have the best revenue/TVL ratios right now?”
• “Show top 20 DEXs by revenue and flag those whose tokens haven’t moved yet.”
• “Compare perps revenue on Solana vs Ethereum vs Base over the last 90 days.”
• “Where is SOL flowing in DeFi – which protocols/chains are capturing deposits?”

Integration with Existing Agents

• crypto-expert: uses this skill for:
  • Deep protocol due diligence and economic modeling
  • Cross-chain and cross-sector comparisons
  • Backing theses with TVL/revenue/flows data.
• flow-tracker: complements wallet-level flow data with:
  • Protocol-level TVL and revenue trends
  • Sector rotation context.
• degen-savant: balances narrative signals with:
  • Which narratives are supported by real fundamentals.
• meme-trader / meme-executor:
  • Use outputs from this skill to size the “core/fundamentals” book
  • Keep degen trades sized relative to fundamentals-backed allocations.

Safety & Quality Gates

• Always:
  • State data sources (e.g., “Based on DefiLlama metrics as of [date]”).
  • Note data lag or uncertainty when relevant.
  • Separate facts (TVL/revenue numbers) from interpretation (thesis).
• Never:
  • Present a thesis without showing the underlying metrics.
  • Call anything “risk-free” or “safe” – only relative risk.

Predictive Analytics Framework

<predictive_analytics> AI/ML Capabilities for Fundamentals:

1. TVL Momentum Prediction

interface TVLPrediction {
  protocol: string;
  current_tvl: number;
  predicted_tvl_7d: number;
  predicted_tvl_30d: number;
  confidence: number;
  features_used: string[];
  model: 'lstm' | 'arima' | 'ensemble';
}

Signals Generated:

• TVL inflection point detection (bottom/top)
• Acceleration/deceleration of flows
• Anomalous TVL movements (whale inflows)

2. Revenue-to-Price Divergence Detector

interface DivergenceSignal {
  protocol: string;
  revenue_growth_90d: number;
  price_change_90d: number;
  divergence_score: number;  // Positive = undervalued
  similar_historical_cases: HistoricalCase[];
  expected_catch_up: number;  // % price move to close gap
}

Detection Logic:

Divergence Score = (Revenue Growth % - Price Change %) * Correlation Factor
If Divergence > 50: Strong undervaluation signal
If Divergence < -50: Strong overvaluation signal

3. Sector Rotation Predictor

interface SectorRotation {
  from_sector: string;
  to_sector: string;
  flow_volume: number;
  rotation_strength: number;  // 0-1
  time_horizon: '1w' | '1m' | '3m';
  confidence: number;
}

Indicators Used:

• Cross-sector TVL flows
• Revenue share changes
• New protocol launches by sector
• Social/narrative momentum by sector

4. Protocol Health Score (ML-Generated)

interface ProtocolHealthScore {
  protocol: string;
  overall_score: number;  // 0-100
  components: {
    growth_score: number;      // TVL + revenue growth
    efficiency_score: number;  // Revenue/TVL ratio
    stability_score: number;   // Volatility, consistency
    adoption_score: number;    // User growth, retention
    risk_score: number;        // Concentration, dependencies
  };
  trend: 'improving' | 'stable' | 'declining';
  alerts: string[];
}

Output Format:

PROTOCOL HEALTH: Raydium
══════════════════════════════

OVERALL SCORE: 78/100 (↑ +5 from 30d ago)

COMPONENTS:
├─ Growth: 82/100 (TVL +15%, revenue +22%)
├─ Efficiency: 75/100 (0.8% rev/TVL, above median)
├─ Stability: 71/100 (moderate volatility)
├─ Adoption: 85/100 (users +18%, retention 65%)
└─ Risk: 79/100 (diversified, no concentration)

TREND: IMPROVING
├─ Revenue outpacing TVL growth
├─ User retention above sector average
├─ No concerning dependencies detected

ML PREDICTION:
├─ 30d TVL: +8-12% (confidence: 72%)
├─ 30d Revenue: +15-20% (confidence: 68%)
└─ Divergence Status: UNDERVALUED (price lagging fundamentals)

SIMILAR PROTOCOLS HISTORICALLY:
When protocols showed this pattern, 70% saw
price appreciation of 40-80% within 60 days.

</predictive_analytics>

Continuous Learning & Adaptation

<adaptive_learning> Model Performance Tracking:

interface ModelPerformance {
  model_id: string;
  predictions_made: number;
  accuracy_30d: number;
  accuracy_90d: number;
  last_retrained: Date;
  data_quality_score: number;
}

Adaptation Triggers:

1. Accuracy Drift: Retrain if 30d accuracy < 60%
2. Regime Change: Detect market regime shift, adjust weights
3. New Data Source: Incorporate and validate new inputs
4. Outlier Events: Flag black swans, exclude from training

Feedback Loop:

Prediction → Outcome Tracked → Error Analysis
     ↑                              ↓
Model Weights Updated ← Feature Importance Review

Weekly Model Review:

• Compare predicted vs actual TVL/revenue
• Identify systematic biases
• Update feature weights
• Add/remove features based on importance </adaptive_learning>

Data Pipeline Integration

<data_pipeline> Data Sources (via data-orchestrator):

Data Quality Requirements:

• TVL data: 15-min freshness, 95% completeness
• Revenue data: Daily freshness, 90% completeness
• Historical data: 99% completeness for ML training
• Cross-source verification required for alerts

Pipeline Architecture:

DefiLlama → Validation → Enrichment → Feature Store → ML Models
     ↓                                      ↓
   Cache ←───────── API Response ←──── Predictions

</data_pipeline>

Advanced Screening Queries

<screening_queries> Pre-built ML-Enhanced Screens:

# Find undervalued protocols (ML divergence detector)
npx tsx .claude/skills/llama-analyst/scripts/screener.ts \
  --screen divergence_undervalued \
  --min-tvl 10000000 \
  --sector defi

# Predict sector rotation
npx tsx .claude/skills/llama-analyst/scripts/screener.ts \
  --screen sector_rotation \
  --lookback 30d \
  --prediction-horizon 7d

# Protocol health ranking
npx tsx .claude/skills/llama-analyst/scripts/screener.ts \
  --screen health_score \
  --top 20 \
  --sort-by overall_score

# TVL momentum detection
npx tsx .claude/skills/llama-analyst/scripts/screener.ts \
  --screen tvl_momentum \
  --threshold inflection \
  --chain solana

Custom Query Builder:

interface ScreenerQuery {
  filters: {
    min_tvl?: number;
    max_tvl?: number;
    min_revenue_growth?: number;
    sectors?: string[];
    chains?: string[];
  };
  sort_by: 'health_score' | 'divergence' | 'tvl_growth' | 'revenue_efficiency';
  ml_enhancements: {
    include_predictions: boolean;
    include_health_score: boolean;
    include_similar_cases: boolean;
  };
  limit: number;
}

</screening_queries>

CLI Usage

# Get protocol health score
npx tsx .claude/skills/llama-analyst/scripts/health-score.ts \
  --protocol raydium \
  --include-prediction

# Run divergence analysis
npx tsx .claude/skills/llama-analyst/scripts/divergence.ts \
  --lookback 90d \
  --min-divergence 30

# Sector rotation analysis
npx tsx .claude/skills/llama-analyst/scripts/sector-rotation.ts \
  --timeframe 30d \
  --predict-horizon 7d

# Full fundamentals report
npx tsx .claude/skills/llama-analyst/scripts/full-report.ts \
  --protocol jupiter \
  --include-ml \
  --format detailed

<see_also>

• references/ml-models.md – Model specifications
• references/feature-catalog.md – Available features
• scripts/health-score.ts – Health score calculator
• scripts/divergence.ts – Price/fundamentals divergence
• scripts/sector-rotation.ts – Rotation predictor </see_also>