--- name: llama-analyst description: | 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. tools: Read(pattern:.claude/skills/llama-analyst/**), WebSearch, WebFetch(domain:defillama.com|api.llama.fi|dune.com), TodoWrite --- # 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 **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. ``` ## Continuous Learning & Adaptation **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 Pipeline Integration **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 ``` ## Advanced Screening Queries **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; } ``` ## CLI Usage ```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