llama-analyst

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Llama Analyst - Fundamentals & Data-Driven Crypto Research

Llama Analyst - 基本面与数据驱动的加密货币研究工具

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

受LlamaAI（Dynamo DeFi演示）等工具启发，本Skill专注于系统化、数据优先的加密货币投资，而非单纯的叙事或迷因币交易。

Activation Triggers

触发场景

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.

在以下场景中使用本Skill：

你想要寻找被低估的协议或具备以下特征的代币：
- TVL或营收持续增长
- 代币价格持平或下跌
你需要赛道或协议筛选，例如：
- 按营收/TVL排名的顶级DEX
- 营收增长最快的永续合约协议
- DeFi资金流入持续增加的公链
你请求宏观DeFi分析：
- SOL/BTC/ETH流入DeFi的长期趋势
- 生态系统对比（Solana vs Ethereum vs L2）
- 按APR、风险和资金粘性筛选收益池
你需要基于数据的投资逻辑，而非单纯的叙事。

Core Capabilities

核心功能

1. Protocol Screening & Ranking

1. 协议筛选与排名

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).

可通过以下组合条件筛选协议：
- TVL规模与TVL增长（绝对值与百分比）
- 营收与营收增长
- 营收效率（营收/TVL）
- 代币价格表现与基本面的对比
识别：
- TVL/营收增长但价格滞后的协议
- 基本面强劲但叙事关注度低的协议
- 过热项目（价格涨幅远高于基本面）。

2. Sector & Ecosystem Analytics

2. 赛道与生态系统分析

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).

对比：
- DEX、永续合约、借贷、LSDs、RWAs、再质押等不同赛道
- 各赛道的营收与TVL分布。
分析：
- 哪些赛道的市场份额在上升或下降
- 哪些公链正在获取更多DeFi TVL和手续费
- 资金的长期轮动趋势（例如从L1转向永续合约，从DeFi转向迷因币）。

3. Flow & Macro Views

3. 资金流向与宏观视角

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.

追踪以下资产的流向：
- SOL/BTC/ETH和稳定币流入/流出DeFi的情况
- 公链与赛道之间的资金轮动。
应用场景：
- 判断**风险偏好（风险偏好上升/下降）**环境
- 指导迷因币/投机交易的仓位大小
- 使交易方向与DeFi宏观资金流保持一致。

4. Output Formatting

4. 输出格式

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).

默认输出：
- 协议或赛道的排名表格（Markdown格式）
- 解释项目突出原因的要点总结
- 满足条件的检查清单（例如：“TVL↑，营收↑，价格↓”）
按需提供：
- 通过表格模拟简单图表（TVL vs 营收、长期资金流）
- 生成可直接用于外部工具的描述（例如LlamaAI界面）。

Example Queries This Skill Should Own

本Skill适配的示例查询

“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?”

“帮我找10个营收和TVL增长但代币价格持平的协议。”
“目前Solana上哪些DeFi协议的营收/TVL比率最佳？”
“按营收排名前20的DEX，并标记那些代币尚未上涨的项目。”
“对比过去90天Solana、Ethereum和Base上永续合约的营收情况。”
“SOL在DeFi中的流向是怎样的——哪些协议/公链在吸纳存款？”

Integration with Existing Agents

与现有Agent的集成

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.

crypto-expert：使用本Skill进行：
- 深度协议尽职调查与经济模型分析
- 跨链与跨赛道对比
- 用TVL/营收/资金流数据支撑投资逻辑。
flow-tracker：用以下内容补充钱包级资金流数据：
- 协议级TVL和营收趋势
- 赛道轮动背景。
degen-savant：平衡叙事信号与：
- 哪些叙事有真实基本面支撑。
meme-trader / meme-executor：
- 利用本Skill的输出来配置“核心/基本面”仓位
- 使投机交易仓位与基本面支撑的配置保持合理比例。

Safety & Quality Gates

安全与质量保障

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.

始终：
- 声明数据来源（例如：“基于DefiLlama截至[日期]的指标”）
- 相关时注明数据延迟或不确定性
- 区分事实（TVL/营收数据）与解读（投资逻辑）。
绝不：
- 不展示底层指标就提出投资逻辑
- 称任何项目“无风险”或“安全”——仅说明相对风险。

Predictive Analytics Framework

预测分析框架

<predictive_analytics> AI/ML Capabilities for Fundamentals:

<predictive_analytics> 基本面分析的AI/ML能力：

1. TVL Momentum Prediction

1. TVL动量预测

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)

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';
}

生成的信号：

TVL拐点检测（底部/顶部）
资金流加速/减速
异常TVL变动（大额资金流入）

2. Revenue-to-Price Divergence Detector

2. 营收-价格背离检测器

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

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
}

检测逻辑：

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

3. Sector Rotation Predictor

3. 赛道轮动预测器

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

typescript

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

使用的指标：

跨赛道TVL流动
营收份额变化
各赛道新协议上线情况
各赛道的社交/叙事热度

4. Protocol Health Score (ML-Generated)

4. 协议健康评分（ML生成）

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.

</predictive_analytics>

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[];
}

输出格式：

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

持续学习与自适应

<adaptive_learning> 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:

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>

<adaptive_learning> 模型性能跟踪：

typescript

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

自适应触发条件：

准确率漂移：若30天准确率<60%则重新训练
市场环境变化：检测市场环境转变，调整权重
新数据源：整合并验证新输入数据
异常事件：标记黑天鹅事件，排除在训练数据外

反馈循环：

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

每周模型回顾：

对比预测与实际TVL/营收数据
识别系统性偏差
更新特征权重
根据重要性添加/移除特征 </adaptive_learning>

Data Pipeline Integration

数据管道集成

<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>

<data_pipeline> 数据源（通过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

数据质量要求：

TVL数据：15分钟新鲜度，95%完整性
营收数据：每日新鲜度，90%完整性
历史数据：用于ML训练的数据需99%完整
警报需经过跨数据源验证

管道架构：

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

</data_pipeline>

Advanced Screening Queries

高级筛选查询

<screening_queries> Pre-built ML-Enhanced Screens:

bash

undefined

<screening_queries> 预构建的ML增强筛选器：

bash

undefined

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;
}

</screening_queries>

npx tsx .claude/skills/llama-analyst/scripts/screener.ts
--screen tvl_momentum
--threshold inflection
--chain solana


**自定义查询构建器：**
```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;
}

</screening_queries>

CLI Usage

CLI使用方法

bash

undefined

bash

undefined

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>

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>