When to use this skill
Use when the user asks about:
- Analyzing numerical data (prices, volumes, metrics)
- Calculating statistics (averages, percentiles, correlations)
- Identifying trends or anomalies in data
- Comparing performance across time periods or assets
- Presenting data in a clear, structured format
Analysis Framework
1. Data Understanding
Before analyzing, assess the data:
- Source: Where does the data come from? Is it reliable?
- Time range: What period does the data cover?
- Granularity: Daily, hourly, per-block?
- Completeness: Are there gaps or missing data points?
- Units: USD, ETH-denominated, percentage, raw count?
- Adjustments needed: Inflation adjustment, normalization, outlier handling?
2. Descriptive Statistics
Compute baseline statistics:
- Central tendency: Mean, median, mode — median is more robust for skewed crypto data
- Dispersion: Standard deviation, range, interquartile range (IQR)
- Distribution shape: Skewness (crypto returns are typically negatively skewed) and kurtosis (fat tails are common)
- Percentiles: 5th, 25th, 50th, 75th, 95th — useful for setting expectations
Present as a summary table:
| Metric | Value |
|---|---|
| Mean | X |
| Median | Y |
| Std Dev | Z |
| Min | A |
| Max | B |
| Count | N |
3. Trend Analysis
Identify and quantify trends:
- Moving averages: 7-day, 30-day, 90-day to smooth noise
- Growth rates: Period-over-period percentage change (daily, weekly, monthly)
- CAGR: Compound Annual Growth Rate for longer-term performance
- Trend direction: Classify as uptrend, downtrend, or sideways based on moving average slopes
- Trend strength: How consistent is the trend? R-squared of linear regression
4. Comparative Analysis
When comparing across entities or time periods:
- Normalize data: Convert to percentage change from a common starting point for fair comparison
- Relative performance: Calculate alpha (excess return) relative to a benchmark (BTC, ETH, or market index)
- Correlation matrix: How closely do the compared items move together?
- Ratio analysis: Asset A / Asset B ratio to identify relative value trends
- Ranking: Order by performance metric with percentile rankings
5. Anomaly Detection
Flag unusual data points:
- Z-score method: Values beyond 2-3 standard deviations from the mean
- IQR method: Values below Q1 - 1.5IQR or above Q3 + 1.5IQR
- Volume spikes: Daily volume exceeding 3x the 30-day average
- Price gaps: Sudden moves exceeding 2x the average daily range
- Contextual check: Always check if an anomaly has a known cause (hack, listing, upgrade)
6. Data Presentation
Structure output for clarity:
Tables — best for exact values and multi-metric comparison:
- Align numbers to the right
- Use consistent decimal places
- Include units in column headers
- Sort by the most relevant column
Series summaries — when presenting time-series data textually:
- Start with the current value and direction
- Reference key inflection points (when did the trend change?)
- Compare to relevant time periods (YTD, QoQ, YoY)
- Highlight the single most significant data point
7. Caveats and Limitations
Always note:
- Survivorship bias: Analysis of "top tokens" ignores failed ones
- Look-ahead bias: Past data analysis doesn't predict future performance
- Sample size: Small samples (less than 30 data points) produce unreliable statistics
- Data quality: On-chain data may include wash trading, bots, or fake volume
- Correlation vs causation: Two metrics moving together doesn't mean one causes the other
8. Output Format
- Analysis type: Descriptive / Comparative / Trend / Anomaly
- Data summary: Key statistics in a table
- Main finding: The single most important insight from the data
- Supporting findings: 2-4 additional observations
- Trend assessment: Direction and strength
- Anomalies: Any flagged data points with context
- Confidence: High / Medium / Low based on data quality and sample size
- Limitations: Relevant caveats for this specific analysis