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:

• Accuracy Drift: Retrain if 30d accuracy < 60%

• Regime Change: Detect market regime shift, adjust weights

• New Data Source: Incorporate and validate new inputs

• 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):

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

</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>