llama-analyst

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.

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).

• “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?”

• 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.

• 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.

AI/ML Capabilities for Fundamentals:

1. TVL Momentum Prediction

```typescript

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

```typescript

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

```typescript

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)

```typescript

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.

```

Model Performance Tracking:

```typescript

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

Data Sources (via data-orchestrator):

| Source | Data Type | Update Frequency | Quality |

|--------|-----------|------------------|---------|

| DefiLlama API | TVL, revenue, yields | 15 min | 92/100 |

| Dune Analytics | Custom queries | Hourly | 90/100 |

| Token Terminal | Revenue, P/E | Daily | 95/100 |

| Chain-specific RPCs | Real-time metrics | Real-time | 98/100 |

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

```

Pre-built ML-Enhanced Screens:

```bash

# 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:

```typescript

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;

}

```

```bash

# 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

```

• 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