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data-analysis

Analyze numerical crypto datasets for statistical patterns, trends, and anomalies.

5|Updated May 2, 2026
Search Tags:#data visualization#time series#statistics#anomaly detection#trend analysis#crypto#portfolio performance
One-click install
npx skills add https://github.com/nirholas/three-ui --skill data-analysis-nirholas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/nirholas/three-ui/tree/main/data/skills/general/data-analysis
Command: npx skills add https://github.com/nirholas/three-ui --skill data-analysis-nirholas

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data-analysis helps users make sense of messy numerical crypto data by converting raw price, volume, and on-chain metrics into clear, decision-ready summaries.

Core Features & Use Cases

  • Descriptive and statistical summaries: computes central tendency, dispersion, distribution shape, and percentile checkpoints for a fast baseline.
  • Trend, comparison, and ranking: applies moving averages, growth rates, normalization, alpha vs benchmarks, correlation matrices, and ordered performance comparisons.
  • Anomaly detection with context: flags outliers using Z-score and IQR rules and recommends validating whether events have known explanations (e.g., hacks or listings).

Quick Start

Use the data-analysis skill when a user provides a time series of token prices or on-chain metrics and needs a structured summary that includes key statistics, trend assessment, and any anomalies with confidence and limitations.

Frequently Asked Questions about data-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze crypto time series data for trends and anomalies?▼

Crypto anomaly detection flags statistical outliers using Z-score and IQR rules, then recommends validating whether the events have known causes like hacks or new exchange listings. It outputs anomaly flags with context rather than raw alerts.

What is the best way to compare crypto portfolio performance across multiple assets?▼

Crypto portfolio performance comparison requires numerical datasets of asset prices or protocol statistics. You provide the time series data, and the analysis computes growth rates, normalized comparisons, and correlation matrices without needing external dependencies.

How do I get descriptive statistics for messy on-chain metrics?▼

Trend analysis applies moving averages and growth rates to crypto time series data. It processes price series and protocol statistics to produce trend metrics, helping you identify directional movement and normalize comparative performance.

What are the limitations of statistical anomaly detection for crypto price series?▼

Statistical anomaly detection for crypto price series is limited because outliers flagged by Z-score or IQR methods require manual validation against known market events. The resulting structured report includes caveats to guide interpretation of these anomalies.

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