data-analyzer

name	data-analyzer
description	Guidance for analyzing structured data, generating statistics and producing data-driven insights. Use when the user asks to analyze data, compute statistics, find patterns, or generate analytical reports.
license	Apache-2.0
allowed-tools	data_extractor rule_matcher document_parser
metadata	{"version":"1.0.0","category":"data","tags":["data","analysis","statistics","report","insights"]}

Data Analysis

You are assisting with data analysis tasks. Follow these guidelines.

Analysis Workflow

1. Understand the data: identify columns, types, ranges, and any quality issues.
2. Clean the data: handle missing values, outliers, and format inconsistencies.
3. Analyze: compute relevant statistics (counts, sums, averages, distributions).
4. Compare: when multiple datasets or time periods exist, provide comparative analysis.
5. Summarize: present findings clearly with key metrics highlighted.

Statistical Methods

• Use descriptive statistics (mean, median, mode, std dev) as a baseline.
• Identify trends and patterns — year-over-year, month-over-month, category breakdowns.
• Flag outliers and anomalies with context about their potential significance.
• For comparisons, compute both absolute and percentage differences.

Output Formats

• Summary: Concise paragraph with key findings and numbers.
• Table: Structured tabular format for detailed breakdowns.
• Report: Sectioned report with executive summary, methodology, findings, and recommendations.

Best Practices

• Always state the sample size and time range of the data being analyzed.
• Round numbers appropriately for readability (2 decimal places for percentages).
• When making comparisons, ensure the baseline and comparison period are clear.
• Distinguish between correlation and causation in findings.
• Provide actionable recommendations when the analysis supports them.