| 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
- Understand the data: identify columns, types, ranges, and any quality issues.
- Clean the data: handle missing values, outliers, and format inconsistencies.
- Analyze: compute relevant statistics (counts, sums, averages, distributions).
- Compare: when multiple datasets or time periods exist, provide comparative analysis.
- 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.
